-
Notifications
You must be signed in to change notification settings - Fork 1
/
papers.bib
2009 lines (1812 loc) · 150 KB
/
papers.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
@inproceedings{linnanfft,
author = {Wang, Linnan and Wu, Wei and Zhang, Junyu and Liu, Hang and Bosilca, George and Herlihy, Maurice and Fonseca, Rodrigo},
title = {FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks},
year = {2020},
isbn = {9781450370523},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3369583.3392681},
doi = {10.1145/3369583.3392681},
abstract = {The performance and efficiency of distributed training of Deep Neural Networks (DNN) highly depend on the performance of gradient averaging among participating processes, a step bound by communication costs. There are two major approaches to reduce communication overhead: overlap communications with computations (lossless), or reduce communications (lossy). The lossless solution works well for linear neural architectures, e.g. VGG, AlexNet, but more recent networks such as ResNet and Inception limit the opportunity for such overlapping. Therefore, approaches that reduce the amount of data (lossy) become more suitable. In this paper, we present a novel, explainable lossy method that sparsifies gradients in the frequency domain, in addition to a new range-based float point representation to quantize and further compress gradients. These dynamic techniques strike a balance between compression ratio, accuracy, and computational overhead, and are optimized to maximize performance in heterogeneous environments.Unlike existing works that strive for a higher compression ratio, we stress the robustness of our methods, and provide guidance to recover accuracy from failures. To achieve this, we prove how the FFT sparsification affects the convergence and accuracy, and show that our method is guaranteed to converge using a diminishing θ in training. Reducing θ can also be used to recover accuracy from the failure. Compared to STOA lossy methods, e.g., QSGD, TernGrad, and Top-k sparsification, our approach incurs less approximation error, thereby better in both the wall-time and accuracy. On an 8 GPUs, InfiniBand interconnected cluster, our techniques effectively accelerate AlexNet training up to 2.26x to the baseline of no compression, and 1.31x to QSGD, 1.25x to Terngrad and 1.47x to Top-K sparsification.},
booktitle = {Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing},
pages = {113–124},
numpages = {12},
keywords = {FFT, gradient compression, neural networks, loosy gradients, machine learning},
location = {Stockholm, Sweden},
series = {HPDC '20}
}
@inproceedings{linnanlamcts,
title = {Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search},
author = {Linnan Wang, Rodrigo Fonseca, Yuandong Tian},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), 2020},
year = {2020},
month = Dec,
location = {online}
}
@inproceedings{aifm,
title = {AIFM: High-Performance, Application-Integrated Far Memory},
author = {Zhenyuan Ruan and Malte Schwarzkopf and Marcos Aguilera and Adam Belay},
booktitle = {Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI)},
year = {2020},
month = nov,
location = {Banff, Alberta, Canada}
}
@inproceedings{naseer2020zero,
author = {Naseer, Usama and Niccolini, Luca and Pant, Udip and Frindell, Alan and Dasineni, Ranjeeth and Benson, Theophilus A.},
title = {Zero Downtime Release: Disruption-Free Load Balancing of a Multi-Billion User Website},
year = {2020},
isbn = {9781450379557},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3387514.3405885},
doi = {10.1145/3387514.3405885},
abstract = {Modern network infrastructure has evolved into a complex organism to satisfy the performance and availability requirements for the billions of users. Frequent releases such as code upgrades, bug fixes and security updates have become a norm. Millions of globally distributed infrastructure components including servers and load-balancers are restarted frequently from multiple times per-day to per-week. However, every release brings possibilities of disruptions as it can result in reduced cluster capacity, disturb intricate interaction of the components operating at large scales and disrupt the end-users by terminating their connections. The challenge is further complicated by the scale and heterogeneity of supported services and protocols.In this paper, we leverage different components of the end-to-end networking infrastructure to prevent or mask any disruptions in face of releases. Zero Downtime Release is a collection of mechanisms used at Facebook to shield the end-users from any disruptions, preserve the cluster capacity and robustness of the infrastructure when updates are released globally. Our evaluation shows that these mechanisms prevent any significant cluster capacity degradation when a considerable number of productions servers and proxies are restarted and minimizes the disruption for different services (notably TCP, HTTP and publish/subscribe).},
booktitle = {Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication},
pages = {529–541},
numpages = {13},
keywords = {Update releases, Load-balancing, Reliable networks},
location = {Virtual Event, USA},
series = {SIGCOMM '20}
}
@inproceedings{ye2020accuracy,
author = {Ye, Fangdan and Yu, Da and Zhai, Ennan and Liu, Hongqiang Harry and Tian, Bingchuan and Ye, Qiaobo and Wang, Chunsheng and Wu, Xin and Guo, Tianchen and Jin, Cheng and She, Duncheng and Ma, Qing and Cheng, Biao and Xu, Hui and Zhang, Ming and Wang, Zhiliang and Fonseca, Rodrigo},
title = {Accuracy, Scalability, Coverage: A Practical Configuration Verifier on a Global WAN},
year = {2020},
isbn = {9781450379557},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3387514.3406217},
doi = {10.1145/3387514.3406217},
abstract = {This paper presents Hoyan-- the first reported large scale deployment of configuration verification in a global-scale wide area network (WAN). Hoyan has been running in production for more than two years and is currently used for all critical configuration auditing and updates on the WAN. We highlight our innovative designs and real-life experience to make Hoyan accurate and scalable in practice. For accuracy under the inconsistencies of devices' vendor-specific behaviors (VSBs), Hoyan continuously discovers the flaws in device behavior models, thus aiding the operators in fixing the models. For scalability to verify our global WAN, Hoyan introduces a "global-simulation & local formal-modeling" strategy to model uncertainties in small scales and perform aggressive pruning of possibilities during the protocol simulations. Hoyan achieves near-100% verification accuracy after it detected and fixed O(10) VSBs on our WAN. Hoyan has prevented many potential service failures resulting from misconfiguration and reduced the failure rate of updates of our WAN by more than half in 2019.},
booktitle = {Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication},
pages = {599–614},
numpages = {16},
keywords = {Network Verification, Network Configurations, Reliability},
location = {Virtual Event, USA},
series = {SIGCOMM '20}
}
@article{mcsherry2020shared,
title={Shared Arrangements: practical inter-query sharing for streaming dataflows},
author={McSherry, Frank and Lattuada, Andrea and Schwarzkopf, Malte and Roscoe, Timothy},
journal={Proceedings of the VLDB Endowment},
volume={13},
number={10},
pages={1793--1806},
year={2020},
publisher={Association for Computing Machinery}
}
@article{gonginspector,
title={Inspector Gadget: A Framework for Inferring TCP Con-gestion Control Algorithms and Protocol Configurations.},
author={Gong, Sishuai and Naseer, Usama and Benson, Theophilus A}
}
@inproceedings{wang20nas,
title={Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search},
author={Wang, Linnan and Zhao, Yiyang and Jinnai, Yuu and Tian, Yuandong and Fonseca, Rodrigo},
booktitle={Proceedings of the 2020 AAAI Conference on Artificial Intelligence},
year={2020}
}
@inproceedings{galakatos19fitting,
author = {Galakatos, Alex and Markovitch, Michael and Binnig, Carsten and Fonseca, Rodrigo and Kraska, Tim},
title = {FITing-Tree: A Data-Aware Index Structure},
year = {2019},
isbn = {9781450356435},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3299869.3319860},
doi = {10.1145/3299869.3319860},
abstract = {Index structures are one of the most important tools that DBAs leverage to improve the performance of analytics and transactional workloads. However, building several indexes over large datasets can often become prohibitive and consume valuable system resources. In fact, a recent study showed that indexes created as part of the TPC-C benchmark can account for 55% of the total memory available in a modern DBMS. This overhead consumes valuable and expensive main memory, and limits the amount of space available to store new data or process existing data. In this paper, we present a novel data-aware index structure called FITing-Tree which approximates an index using piece-wise linear functions with a bounded error specified at construction time. This error knob provides a tunable parameter that allows a DBA to FIT an index to a dataset and workload by being able to balance lookup performance and space consumption. To navigate this tradeoff, we provide a cost model that helps determine an appropriate error parameter given either (1) a lookup latency requirement (e.g., 500ns) or (2) a storage budget (e.g., 100MB). Using a variety of real-world datasets, we show that our index is able to provide performance that is comparable to full index structures while reducing the storage footprint by orders of magnitude.},
booktitle = {Proceedings of the 2019 International Conference on Management of Data},
pages = {1189–1206},
numpages = {18},
location = {Amsterdam, Netherlands},
series = {SIGMOD '19}
}
@inproceedings{yu19dshark,
Address = "Boston, MA",
Author = "Da Yu and Yibo Zhu and Behnaz Arzani and Rodrigo Fonseca and Tianrong Zhang and Karl Deng and Lihua Yuan",
Booktitle = "16th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 19)",
Pages = "207--220",
Publisher = " {USENIX} Association",
Title = "dShark: A General, Easy to Program and Scalable Framework for Analyzing In-network Packet Traces",
Year = "2019"}
@inproceedings{lucas19p4intel,
author = {Castanheira, Lucas and Schaeffer-Filho, Alberto and Benson, Theophilus A.},
title = {P4-InTel: Bridging the Gap between ICF Diagnosis and Functionality},
year = {2019},
isbn = {9781450370004},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3359993.3366648},
doi = {10.1145/3359993.3366648},
abstract = {Data plane programmability promotes a new kind of computing paradigm in which parts of an application's execution can be offloaded into the network. However, this in-network compute functionality (iCF) adds an extra layer of management complexity for the tracing and debugging of distributed applications. Specifically, current programmable hardware does not provide powerful enough primitives or abstractions to enable in-network tracing. Further, existing distributed application debug solutions do not extend directly into programmable data planes.In this paper, we take a step back and revisit the fundamental problem by discussing open research questions and challenges towards a comprehensive iCF telemetry and debugging solution which bridges the gap between traditional and iCF-based debugging. To this end, we introduce a system, P4-InTel, which (i) leverages network telemetry to instrument PDPs into monitoring arbitrary trace data, indicated directly on PDP source code using annotations, and (ii) collects and encapsulates this data in a tracing abstraction. This abstraction provides a global vision of an in-network computation's life-cycle in a standard, readable format, which can either be fed to automatic debugging tools, or used by programmers to facilitate troubleshooting.},
booktitle = {Proceedings of the 1st ACM CoNEXT Workshop on Emerging In-Network Computing Paradigms},
pages = {21–26},
numpages = {6},
keywords = {In-Network Compute, Telemetry, Debugging},
location = {Orlando, FL, USA},
series = {ENCP '19}
}
@inproceedings{wang2018superneurons,
author = {Wang, Linnan and Ye, Jinmian and Zhao, Yiyang and Wu, Wei and Li, Ang and Song, Shuaiwen Leon and Xu, Zenglin and Kraska, Tim},
title = {Superneurons: Dynamic GPU Memory Management for Training Deep Neural Networks},
year = {2018},
isbn = {9781450349826},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3178487.3178491},
doi = {10.1145/3178487.3178491},
abstract = {Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c.},
booktitle = {Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
pages = {41–53},
numpages = {13},
keywords = {runtime scheduling, neural networks, GPU memory management},
location = {Vienna, Austria},
series = {PPoPP '18}
}
@article{wang2018superneurons,
author = {Wang, Linnan and Ye, Jinmian and Zhao, Yiyang and Wu, Wei and Li, Ang and Song, Shuaiwen Leon and Xu, Zenglin and Kraska, Tim},
title = {Superneurons: Dynamic GPU Memory Management for Training Deep Neural Networks},
year = {2018},
issue_date = {January 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {53},
number = {1},
issn = {0362-1340},
url = {https://doi.org/10.1145/3200691.3178491},
doi = {10.1145/3200691.3178491},
abstract = {Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c.},
journal = {SIGPLAN Not.},
month = {feb},
pages = {41–53},
numpages = {13},
keywords = {neural networks, runtime scheduling, GPU memory management}
}
@inproceedings{lascasas18sampling,
author = {Las-Casas, Pedro and Mace, Jonathan and Guedes, Dorgival and Fonseca, Rodrigo},
title = {Weighted Sampling of Execution Traces: Capturing More Needles and Less Hay},
year = {2018},
isbn = {9781450360111},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3267809.3267841},
doi = {10.1145/3267809.3267841},
abstract = {End-to-end tracing has emerged recently as a valuable tool to improve the dependability of distributed systems, by performing dynamic verification and diagnosing correctness and performance problems. Contrary to logging, end-to-end traces enable coherent sampling of the entire execution of specific requests, and this is exploited by many deployments to reduce the overhead and storage requirements of tracing. This sampling, however, is usually done uniformly at random, which dedicates a large fraction of the sampling budget to common, 'normal' executions, while missing infrequent, but sometimes important, erroneous or anomalous executions. In this paper we define the representative trace sampling problem, and present a new approach, based on clustering of execution graphs, that is able to bias the sampling of requests to maximize the diversity of execution traces stored towards infrequent patterns. In a preliminary, but encouraging work, we show how our approach chooses to persist representative and diverse executions, even when anomalous ones are very infrequent.},
booktitle = {Proceedings of the ACM Symposium on Cloud Computing},
pages = {326–332},
numpages = {7},
keywords = {weighted sampling, distributed tracing},
location = {Carlsbad, CA, USA},
series = {SoCC '18}
}
@inproceedings{demarinis2019scanning,
author = {DeMarinis, Nicholas and Tellex, Stefanie and Kemerlis, Vasileios P. and Konidaris, George and Fonseca, Rodrigo},
booktitle = {2019 {International Conference} on {Robotics} and {Automation} ({ICRA})},
file = {files/185/DeMarinis et al. - 2019 - Scanning the internet for ros A view of security .pdf},
pages = {8514--8521},
publisher = {IEEE},
shorttitle = {Scanning the Internet for Ros},
title = {Scanning the Internet for Ros: {A} View of Security in Robotics Research},
year = {2019},
abstract = {Security is particularly important in robotics, as robots can
directly perceive and affect the physical world. We describe the
results of a scan of the entire IPv4 address space of the Internet
for instances of the Robot Operating System (ROS), a widely used
robotics software platform. We identified a number of hosts
supporting ROS that are exposed to the public Internet, thereby
allowing anyone to access robotic sensors and actuators. As a proof
of concept, and with the consent of the relevant researchers, we
were able to read image sensor information from and actuate a
physical robot present in a research lab in an American
university. This paper gives an overview of our findings, including
our methodology, the geographic distribution of publicly-accessible
platforms, the sorts of sensor and actuator data that is available,
and the different kinds of robots and sensors that our scan
uncovered. Additionally, we offer recommendations on best practices
to mitigate these security issues in the future.}
}
@inproceedings{demarinis2017usable,
author = {DeMarinis, Nicholas and Fonseca, Rodrigo},
title = {Toward Usable Network Traffic Policies for IoT Devices in Consumer Networks},
year = {2017},
isbn = {9781450353960},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3139937.3139949},
doi = {10.1145/3139937.3139949},
abstract = {The Internet of Things (IoT) revolution has brought millions of small, low-cost, connected devices into our homes, cities, infrastructure, and more. However, these devices are often plagued by security vulnerabilities that pose threats to user privacy or can threaten the Internet architecture as a whole. Home networks can be particularly vulnerable to these threats as they typically have no network administrator and often contain unpatched or otherwise vulnerable devices.In this paper, we argue that the unique security challenges of home networks require a new network-layer architecture to both protect against external threats and mitigate attacks from compromised devices. We present initial findings based on traffic analysis from a small-scale IoT testbed toward identifying predictable patterns in IoT traffic that may allow construction of a policy-based framework to restrict malicious traffic. Based on our observations, we discuss key features for the design of this architecture to promote future developments in network-layer security in smart home networks.},
booktitle = {Proceedings of the 2017 Workshop on Internet of Things Security and Privacy},
pages = {43–48},
numpages = {6},
keywords = {intrusion detection, internet of things (iot), home networks, network security},
location = {Dallas, Texas, USA},
series = {IoTS&P '17}
}
@inproceedings{nelson2016switches,
author = {Nelson, Tim and DeMarinis, Nicholas and Hoff, Timothy Adam and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Switches Are Monitors Too! Stateful Property Monitoring as a Switch Design Criterion},
year = {2016},
isbn = {9781450346610},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3005745.3005755},
doi = {10.1145/3005745.3005755},
abstract = {Testing and debugging networks /in situ/ is notoriously difficult. Many vital correctness properties involve histories over multiple packets (e.g., prior established connections). Checking such properties requires /cross-packet state/, which cannot be fully captured on stateless switch hardware.Recent SDN work is enabling limited switch operations on persistent state. We present runtime checking of cross-packet correctness properties as a unique and instructive use case for developing stateful switch primitives. In this paper, we examine a set of cross-packet properties and distill from them switch features needed to monitor their correctness. We then contrast these against features provided by current approaches to switch state in SDNs and identify semantic gaps with an eye toward informing future switch instruction sets.},
booktitle = {Proceedings of the 15th ACM Workshop on Hot Topics in Networks},
pages = {99–105},
numpages = {7},
location = {Atlanta, GA, USA},
series = {HotNets '16}
}
@inproceedings{sambasivan16tracing,
author = {Sambasivan, Raja R. and Shafer, Ilari and Mace, Jonathan and Sigelman, Benjamin H. and Fonseca, Rodrigo and Ganger, Gregory R.},
title = {Principled Workflow-Centric Tracing of Distributed Systems},
year = {2016},
isbn = {9781450345255},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2987550.2987568},
doi = {10.1145/2987550.2987568},
abstract = {Workflow-centric tracing captures the workflow of causally-related events (e.g., work done to process a request) within and among the components of a distributed system. As distributed systems grow in scale and complexity, such tracing is becoming a critical tool for understanding distributed system behavior. Yet, there is a fundamental lack of clarity about how such infrastructures should be designed to provide maximum benefit for important management tasks, such as resource accounting and diagnosis. Without research into this important issue, there is a danger that workflow-centric tracing will not reach its full potential. To help, this paper distills the design space of workflow-centric tracing and describes key design choices that can help or hinder a tracing infrastructures utility for important tasks. Our design space and the design choices we suggest are based on our experiences developing several previous workflow-centric tracing infrastructures.},
booktitle = {Proceedings of the Seventh ACM Symposium on Cloud Computing},
pages = {401–414},
numpages = {14},
location = {Santa Clara, CA, USA},
series = {SoCC '16}
}
@inproceedings{chen16dashs,
author = {Chen, Junyang and Ammar, Mostafa and Fayed, Marwan and Fonseca, Rodrigo},
title = {Client-Driven Network-Level QoE Fairness for Encrypted 'DASH-S'},
year = {2016},
isbn = {9781450344258},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2940136.2940144},
doi = {10.1145/2940136.2940144},
abstract = {Adaptive video streams, when competing behind a bottleneck link, generate flows that lead to instability, under-utilization, and unfairness. Recent studies suggest there is also a negative impact on users' perceived quality of experience as a consequence. Two general classes of solution exist. Client-side bitrate adaptation algorithms can improve stability and may achieve flow-rate equality. However, operating in isolation, bitrate adaptation has no ability to establish QoE fairness. Conversely, network services have been shown to achieve stability and quality of experience by managing bottleneck resources. However, the widespread use of HTTPS renders these services ineffective.In this paper we show that QoE can only be achieved when both network and client interact. We do so by a constructive argument, and then architect client-Driven Video Delivery (cDVD) in response. Our cDVD implementation provides a client-level API into the network and builds on software-defined principles. cDVD measurements reinforce our argument and raise new opportunities for exploration.},
booktitle = {Proceedings of the 2016 Workshop on QoE-Based Analysis and Management of Data Communication Networks},
pages = {55–60},
numpages = {6},
keywords = {Multimedia Streaming, Dynamic Adaptive Streaming over HTTP (DASH), Quality of Experience, QoE fairness, Performance, Network Architecture},
location = {Florianopolis, Brazil},
series = {Internet-QoE '16}
}
@inproceedings{mace16-2dfq,
author = {Mace, Jonathan and Bodik, Peter and Musuvathi, Madanlal and Fonseca, Rodrigo and Varadarajan, Krishnan},
title = {2DFQ: Two-Dimensional Fair Queuing for Multi-Tenant Cloud Services},
year = {2016},
isbn = {9781450341936},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2934872.2934878},
doi = {10.1145/2934872.2934878},
abstract = {In many important cloud services, different tenants execute their requests in the thread pool of the same process, requiring fair sharing of resources. However, using fair queue schedulers to provide fairness in this context is difficult because of high execution concurrency, and because request costs are unknown and have high variance. Using fair schedulers like WFQ and WF²Q in such settings leads to bursty schedules, where large requests block small ones for long periods of time. In this paper, we propose Two-Dimensional Fair Queueing (2DFQ), which spreads requests of different costs across di erent threads and minimizes the impact of tenants with unpredictable requests. In evaluation on production workloads from Azure Storage, a large-scale cloud system at Microsoft, we show that 2DFQ reduces the burstiness of service by 1-2 orders of magnitude. On workloads where many large requests compete with small ones, 2DFQ improves 99th percentile latencies by up to 2 orders of magnitude.},
booktitle = {Proceedings of the 2016 ACM SIGCOMM Conference},
pages = {144–159},
numpages = {16},
keywords = {Multi-Tenant Systems, Fair Request Scheduling},
location = {Florianopolis, Brazil},
series = {SIGCOMM '16}
}
@inproceedings{yu16netex,
Address = "Denver, CO",
Author = "Da Yu and Luo Mai and Somaya Arianfar and Rodrigo Fonseca and Orran Krieger and David Oran",
Booktitle = "Proceedings of the 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud)",
Month = "June",
Publisher = "USENIX Association",
Title = "Towards a Network Marketplace in a Cloud",
Year = "2016"}
@inproceedings{rasley16yak,
author = {Rasley, Jeff and Karanasos, Konstantinos and Kandula, Srikanth and Fonseca, Rodrigo and Vojnovic, Milan and Rao, Sriram},
title = {Efficient Queue Management for Cluster Scheduling},
year = {2016},
isbn = {9781450342407},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2901318.2901354},
doi = {10.1145/2901318.2901354},
abstract = {Job scheduling in Big Data clusters is crucial both for cluster operators' return on investment and for overall user experience. In this context, we observe several anomalies in how modern cluster schedulers manage queues, and argue that maintaining queues of tasks at worker nodes has significant benefits. On one hand, centralized approaches do not use worker-side queues. Given the inherent feedback delays that these systems incur, they achieve suboptimal cluster utilization, particularly for workloads dominated by short tasks. On the other hand, distributed schedulers typically do employ worker-side queuing, and achieve higher cluster utilization. However, they fail to place tasks at the best possible machine, since they lack cluster-wide information, leading to worse job completion time, especially for heterogeneous workloads. To the best of our knowledge, this is the first work to provide principled solutions to the above problems by introducing queue management techniques, such as appropriate queue sizing, prioritization of task execution via queue reordering, starvation freedom, and careful placement of tasks to queues. We instantiate our techniques by extending both a centralized (YARN) and a distributed (Mercury) scheduler, and evaluate their performance on a wide variety of synthetic and production workloads derived from Microsoft clusters. Our centralized implementation, Yaq-c, achieves 1.7x improvement on median job completion time compared to YARN, and our distributed one, Yaq-d, achieves 9.3x improvement over an implementation of Sparrow's batch sampling on Mercury.},
booktitle = {Proceedings of the Eleventh European Conference on Computer Systems},
articleno = {36},
numpages = {15},
location = {London, United Kingdom},
series = {EuroSys '16}
}
@inproceedings{fonseca15losingtrack,
Address = "Asilomar",
Author = "Rodrigo Fonseca and Jonathan Mace",
Booktitle = "HPTS",
Month = "October",
Title = "We are Losing Track: a Case for Causal Metadata in Distributed Systems",
Year = "2015"}
@inproceedings{mace15pivot,
author = {Mace, Jonathan and Roelke, Ryan and Fonseca, Rodrigo},
title = {Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems},
year = {2015},
isbn = {9781450338349},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2815400.2815415},
doi = {10.1145/2815400.2815415},
abstract = {Monitoring and troubleshooting distributed systems is notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today -- logs, counters, and metrics -- have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. We have implemented a prototype of Pivot Tracing for Java-based systems and evaluate it on a heterogeneous Hadoop cluster comprising HDFS, HBase, MapReduce, and YARN. We show that Pivot Tracing can effectively identify a diverse range of root causes such as software bugs, misconfiguration, and limping hardware. We show that Pivot Tracing is dynamic, extensible, and enables cross-tier analysis between inter-operating applications, with low execution overhead.},
booktitle = {Proceedings of the 25th Symposium on Operating Systems Principles},
pages = {378–393},
numpages = {16},
location = {Monterey, California},
series = {SOSP '15}
}
@inproceedings{li15fence,
Author = "Tao Li and Albert Rafetseder and Rodrigo Fonseca and Justin Cappos",
Booktitle = " {Proceedings of the USENIX Annual Technical Conference (ATC 2015)}",
Month = "July",
Publisher = "USENIX Association",
Title = "Fence: Protecting Device Availability With Uniform Resource Control",
Year = "2015"}
@inproceedings{martins15tamer,
Author = "Marcelo Martins and Justin Cappos and Rodrigo Fonseca",
Booktitle = " {Proceedings of the USENIX Annual Technical Conference (ATC 2015)}",
Month = "July",
Publisher = "USENIX Association",
Title = "Selectively Taming Background Android Apps to Improve Battery Lifetime",
Year = "2015"}
@inproceedings{nelson15exodus,
author = {Nelson, Tim and Ferguson, Andrew D. and Yu, Da and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Exodus: Toward Automatic Migration of Enterprise Network Configurations to SDNs},
year = {2015},
isbn = {9781450334518},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2774993.2774997},
doi = {10.1145/2774993.2774997},
abstract = {We present the design and a prototype of Exodus, a system that consumes a collection of router configurations (e.g., in Cisco IOS), compiles these into a common, intermediate semantic form, and then produces corresponding SDN controller software in a high-level language. Exodus generates networks that are functionally similar to the original networks, with the advantage of having centralized programs that are verifiable and evolvable. Exodus supports a wide array of IOS features, including non-trivial kinds of packet-filtering, reflexive access-lists, NAT, VLANs, static and dynamic routing. Implementing Exodus has exposed several limitations in both today's languages for SDN programming and in OpenFlow itself. We briefly discuss these lessons learned and provide guidance for future SDN migration efforts.},
booktitle = {Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research},
articleno = {13},
numpages = {7},
keywords = {SDN migration, software-defined networking, OpenFlow},
location = {Santa Clara, California},
series = {SOSR '15}
}
@inproceedings{nelson15simon,
author = {Nelson, Tim and Yu, Da and Li, Yiming and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Simon: Scriptable Interactive Monitoring for SDNs},
year = {2015},
isbn = {9781450334518},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2774993.2774994},
doi = {10.1145/2774993.2774994},
abstract = {Although Software-Defined Networking can simplify network management, it also poses new testing and debugging challenges for operators. Debugging is often an interactive process that involves stepping through data- and control-plane events and performing actions in response. Sometimes, however, this interactive process can become highly repetitive; in such cases, we should be able to script the activity to reduce operator overhead and increase reusability.We introduce Simon, a Scriptable Interactive Monitoring system for SDN. With Simon, operators can probe their network behavior by executing scripts for debugging, monitoring, and more. Simon is independent of the controller platform used, and does not require annotations or intimate knowledge of the controller software being run. Operators may compose debugging scripts both offline and interactively at Simon's debugging prompt. In the process, they can take advantage of the rich set of reactive functions Simon provides as well as the full power of Scala. We present the design of Simon and discuss its implementation and use.},
booktitle = {Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research},
articleno = {19},
numpages = {7},
keywords = {software-defined networking, OpenFlow, debugging},
location = {Santa Clara, California},
series = {SOSR '15}
}
@inproceedings{mace15retro,
Author = "Jonathan Mace and Peter Bodik and Madanlal Musuvathi and Rodrigo Fonseca",
Booktitle = " {NSDI '15: Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation}",
Month = "May",
Organization = "USENIX Association",
Title = "Retro: Targeted Resource Management in Multi-tenant Distributed Systems",
Year = "2015"}
@article{gnawali14ctp-tosn,
author = {Gnawali, Omprakash and Fonseca, Rodrigo and Jamieson, Kyle and Kazandjieva, Maria and Moss, David and Levis, Philip},
title = {CTP: An Efficient, Robust, and Reliable Collection Tree Protocol for Wireless Sensor Networks},
year = {2013},
issue_date = {November 2013},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {10},
number = {1},
issn = {1550-4859},
url = {https://doi.org/10.1145/2529988},
doi = {10.1145/2529988},
abstract = {We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.},
journal = {ACM Trans. Sen. Netw.},
month = {dec},
articleno = {16},
numpages = {49},
keywords = {routing, adaptive beaconing, link-quality estimation, Wireless sensor network, datapath validation, wireless network protocol}
}
@inproceedings{mace14resource,
Address = "Broomfield, CO",
Author = "Jonathan Mace and Peter Bodik and Rodrigo Fonseca and Madanlal Musuvathi",
Booktitle = "10th Workshop on Hot Topics in System Dependability (HotDep 14)",
Month = Oct,
Publisher = "USENIX Association",
Title = "Towards General-Purpose Resource Management in Shared Cloud Services",
Year = "2014"}
@techreport{sambasivan14tracingTR,
Address = "Pittsburgh, PA 15213-3890",
Author = "Raja R. Sambasivan and Rodrigo Fonseca and Ilari Shafer and Gregory R. Ganger",
Institution = "Parallel Data Laboratory, Carnegie Mellon University",
Month = "April",
Number = "CMU-PDL-14-102",
Title = " {So, you want to trace your distributed system? Key design insights from years of practical experience}",
Year = "2014"}
@inproceedings{rasley14ons,
Address = "Santa Clara, CA",
Author = "Jeff Rasley and Brent Stephens and Colin Dixon and Eric Rozner and Wes Felter and Kanak Agarwal and John Carter and Rodrigo Fonseca",
Booktitle = "Presented as part of the Open Networking Summit 2014 (ONS 2014)",
Publisher = "USENIX",
Title = "Low-latency Network Monitoring via Oversubscribed Port Mirroring",
Year = "2014"}
@inproceedings{rasley14planck,
author = {Rasley, Jeff and Stephens, Brent and Dixon, Colin and Rozner, Eric and Felter, Wes and Agarwal, Kanak and Carter, John and Fonseca, Rodrigo},
title = {Planck: Millisecond-Scale Monitoring and Control for Commodity Networks},
year = {2014},
isbn = {9781450328364},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2619239.2626310},
doi = {10.1145/2619239.2626310},
abstract = {Software-defined networking introduces the possibility of building self-tuning networks that constantly monitor network conditions and react rapidly to important events such as congestion. Unfortunately, state-of-the-art monitoring mechanisms for conventional networks require hundreds of milliseconds to seconds to extract global network state, like link utilization or the identity of "elephant" flows. Such latencies are adequate for responding to persistent issues, e.g., link failures or long-lasting congestion, but are inadequate for responding to transient problems, e.g., congestion induced by bursty workloads sharing a link. In this paper, we present Planck, a novel network measurement architecture that employs oversubscribed port mirroring to extract network information at 280 µs--7 ms timescales on a 1 Gbps commodity switch and 275 µs--4 ms timescales on a 10 Gbps commodity switch,over 11x and 18x faster than recent approaches, respectively (and up to 291x if switch firmware allowed buffering to be disabled on some ports). To demonstrate the value of Planck's speed and accuracy, we use it to drive a traffic engineering application that can reroute congested flows in milliseconds. On a 10 Gbps commodity switch, Planck-driven traffic engineering achieves aggregate throughput within 1--4% of optimal for most workloads we evaluated, even with flows as small as 50 MiB, an improvement of up to 53% over previous schemes.},
booktitle = {Proceedings of the 2014 ACM Conference on SIGCOMM},
pages = {407–418},
numpages = {12},
keywords = {software-defined networking, networking measurement, traffic engineering},
location = {Chicago, Illinois, USA},
series = {SIGCOMM '14}
}
@article{rasley14planck,
author = {Rasley, Jeff and Stephens, Brent and Dixon, Colin and Rozner, Eric and Felter, Wes and Agarwal, Kanak and Carter, John and Fonseca, Rodrigo},
title = {Planck: Millisecond-Scale Monitoring and Control for Commodity Networks},
year = {2014},
issue_date = {October 2014},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {44},
number = {4},
issn = {0146-4833},
url = {https://doi.org/10.1145/2740070.2626310},
doi = {10.1145/2740070.2626310},
abstract = {Software-defined networking introduces the possibility of building self-tuning networks that constantly monitor network conditions and react rapidly to important events such as congestion. Unfortunately, state-of-the-art monitoring mechanisms for conventional networks require hundreds of milliseconds to seconds to extract global network state, like link utilization or the identity of "elephant" flows. Such latencies are adequate for responding to persistent issues, e.g., link failures or long-lasting congestion, but are inadequate for responding to transient problems, e.g., congestion induced by bursty workloads sharing a link. In this paper, we present Planck, a novel network measurement architecture that employs oversubscribed port mirroring to extract network information at 280 µs--7 ms timescales on a 1 Gbps commodity switch and 275 µs--4 ms timescales on a 10 Gbps commodity switch,over 11x and 18x faster than recent approaches, respectively (and up to 291x if switch firmware allowed buffering to be disabled on some ports). To demonstrate the value of Planck's speed and accuracy, we use it to drive a traffic engineering application that can reroute congested flows in milliseconds. On a 10 Gbps commodity switch, Planck-driven traffic engineering achieves aggregate throughput within 1--4% of optimal for most workloads we evaluated, even with flows as small as 50 MiB, an improvement of up to 53% over previous schemes.},
journal = {SIGCOMM Comput. Commun. Rev.},
month = {aug},
pages = {407–418},
numpages = {12},
keywords = {networking measurement, traffic engineering, software-defined networking}
}
@inproceedings{ferguson13cogent,
author = {Ferguson, Andrew D. and Place, Jordan and Fonseca, Rodrigo},
title = {Growth Analysis of a Large ISP},
year = {2013},
isbn = {9781450319539},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2504730.2504769},
doi = {10.1145/2504730.2504769},
abstract = {We present a time-series analysis of Cogent's inter-continental network. The analysis is based on descriptions of Cogent's routers and their interfaces, collected each week for more than one year. These descriptions are collected from public reverse DNS records, which we cross-validate using iffinder, a full Internet scan, and limited ground truth data provided by Cogent. For example, our dataset, which we make available to the research community, shows that while the number of Cogent routers grew by approximately 11.3 each week, the average number of interfaces per router, and the effective diameter of the inferred network remained stable over the same period. Our collected dataset includes information about interface types, port identifications, router locations, peer and customer attachments, and more.},
booktitle = {Proceedings of the 2013 Conference on Internet Measurement Conference},
pages = {347–352},
numpages = {6},
keywords = {reverse DNS, alias resolution},
location = {Barcelona, Spain},
series = {IMC '13}
}
@inproceedings{ferguson13participatory,
author = {Ferguson, Andrew D. and Guha, Arjun and Liang, Chen and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Participatory Networking: An API for Application Control of SDNs},
year = {2013},
isbn = {9781450320566},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2486001.2486003},
doi = {10.1145/2486001.2486003},
abstract = {We present the design, implementation, and evaluation of an API for applications to control a software-defined network (SDN). Our API is implemented by an OpenFlow controller that delegates read and write authority from the network's administrators to end users, or applications and devices acting on their behalf. Users can then work with the network, rather than around it, to achieve better performance, security, or predictable behavior. Our API serves well as the next layer atop current SDN stacks. Our design addresses the two key challenges: how to safely decompose control and visibility of the network, and how to resolve conflicts between untrusted users and across requests, while maintaining baseline levels of fairness and security. Using a real OpenFlow testbed, we demonstrate our API's feasibility through microbenchmarks, and its usefulness by experiments with four real applications modified to take advantage of it.},
booktitle = {Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM},
pages = {327–338},
numpages = {12},
keywords = {software-defined networks, participatory networking, openflow},
location = {Hong Kong, China},
series = {SIGCOMM '13}
}
@article{ferguson13participatory,
author = {Ferguson, Andrew D. and Guha, Arjun and Liang, Chen and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Participatory Networking: An API for Application Control of SDNs},
year = {2013},
issue_date = {October 2013},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {43},
number = {4},
issn = {0146-4833},
url = {https://doi.org/10.1145/2534169.2486003},
doi = {10.1145/2534169.2486003},
abstract = {We present the design, implementation, and evaluation of an API for applications to control a software-defined network (SDN). Our API is implemented by an OpenFlow controller that delegates read and write authority from the network's administrators to end users, or applications and devices acting on their behalf. Users can then work with the network, rather than around it, to achieve better performance, security, or predictable behavior. Our API serves well as the next layer atop current SDN stacks. Our design addresses the two key challenges: how to safely decompose control and visibility of the network, and how to resolve conflicts between untrusted users and across requests, while maintaining baseline levels of fairness and security. Using a real OpenFlow testbed, we demonstrate our API's feasibility through microbenchmarks, and its usefulness by experiments with four real applications modified to take advantage of it.},
journal = {SIGCOMM Comput. Commun. Rev.},
month = {aug},
pages = {327–338},
numpages = {12},
keywords = {openflow, software-defined networks, participatory networking}
}
@inproceedings{li13metering,
Address = "Cambridge, MA",
Author = "Qiang Li and Marcelo Martins and Omprakash Gnawali and Rodrigo Fonseca",
Booktitle = "Proceedings of the Ninth IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 2013)",
Month = "May",
Title = "On the Effectiveness of Energy Metering on Every Node",
Year = "2013"}
@inproceedings{martins13appmodes,
Address = "Jekyll Island, Georgia, USA",
Author = "Marcelo Martins and Rodrigo Fonseca",
Booktitle = "Proceedings of the 14th International Workshop on Mobile Computing Systems and Applications - HotMobile",
Month = "February",
Publisher = "ACM Press",
Title = " {A}pplication {M}odes: {A} Narrow Interface for End-User Power Management in Mobile Devices",
Year = "2013"}
@inproceedings{riondato12parma,
author = {Riondato, Matteo and DeBrabant, Justin A. and Fonseca, Rodrigo and Upfal, Eli},
title = {PARMA: A Parallel Randomized Algorithm for Approximate Association Rules Mining in MapReduce},
year = {2012},
isbn = {9781450311564},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2396761.2396776},
doi = {10.1145/2396761.2396776},
abstract = {Frequent Itemsets and Association Rules Mining (FIM) is a key task in knowledge discovery from data. As the dataset grows, the cost of solving this task is dominated by the component that depends on the number of transactions in the dataset. We address this issue by proposing PARMA, a parallel algorithm for the MapReduce framework, which scales well with the size of the dataset (as number of transactions) while minimizing data replication and communication cost. PARMA cuts down the dataset-size-dependent part of the cost by using a random sampling approach to FIM. Each machine mines a small random sample of the dataset, of size independent from the dataset size. The results from each machine are then filtered and aggregated to produce a single output collection. The output will be a very close approximation of the collection of Frequent Itemsets (FI's) or Association Rules (AR's) with their frequencies and confidence levels. The quality of the output is probabilistically guaranteed by our analysis to be within the user-specified accuracy and error probability parameters. The sizes of the random samples are independent from the size of the dataset, as is the number of samples. They depend on the user-chosen accuracy and error probability parameters and on the parallel computational model. We implemented PARMA in Hadoop MapReduce and show experimentally that it runs faster than previously introduced FIM algorithms for the same platform, while 1) scaling almost linearly, and 2) offering even higher accuracy and confidence than what is guaranteed by the analysis.},
booktitle = {Proceedings of the 21st ACM International Conference on Information and Knowledge Management},
pages = {85–94},
numpages = {10},
keywords = {frequent itemsets, sampling, MapReduce, association rules},
location = {Maui, Hawaii, USA},
series = {CIKM '12}
}
@inproceedings{ferguson12hft,
author = {Ferguson, Andrew D. and Guha, Arjun and Liang, Chen and Fonseca, Rodrigo and Krishnamurthi, Shriram},
title = {Hierarchical Policies for Software Defined Networks},
year = {2012},
isbn = {9781450314770},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2342441.2342450},
doi = {10.1145/2342441.2342450},
abstract = {Hierarchical policies are useful in many contexts in which resources are shared among multiple entities. Such policies can easily express the delegation of authority and the resolution of conflicts, which arise naturally when decision-making is decentralized. Conceptually, a hierarchical policy could be used to manage network resources, but commodity switches, which match packets using flow tables, do not realize hierarchies directly.This paper presents Hierarchical Flow Tables (HFT), a framework for specifying and realizing hierarchical policies in software defined networks. HFT policies are organized as trees, where each component of the tree can independently determine the action to take on each packet. When independent parts of the tree arrive at conflicting decisions, HFT resolves conflicts with user-defined conflict-resolution operators, which exist at each node of the tree. We present a compiler that realizes HFT policies on a distributed network of OpenFlow switches, and prove its correctness using the Coq proof assistant. We then evaluate the use of HFT to improve performance of networked applications.},
booktitle = {Proceedings of the First Workshop on Hot Topics in Software Defined Networks},
pages = {37–42},
numpages = {6},
keywords = {hierarchical policies, participatory networking, openflow, software defined networks},
location = {Helsinki, Finland},
series = {HotSDN '12}
}
@inproceedings{backman12cmr,
author = {Backman, Nathan and Pattabiraman, Karthik and Fonseca, Rodrigo and Cetintemel, Ugur},
title = {C-MR: Continuously Executing MapReduce Workflows on Multi-Core Processors},
year = {2012},
isbn = {9781450313438},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2287016.2287018},
doi = {10.1145/2287016.2287018},
abstract = {The widespread appeal of MapReduce is due, in part, to its simple programming model. Programmers provide only application logic while the MapReduce framework handles the logistics of data distribution and parallel task management.We present the Continuous-MapReduce (C-MR) framework which implements a modified MapReduce processing model to continuously execute workflows of MapReduce jobs on unbounded data streams. In keeping with the philosophy of MapReduce, C-MR abstracts away the complexities of parallel stream processing and workflow scheduling while providing the simple and familiar MapReduce programming interface with the addition of stream window semantics.Modifying the MapReduce processing model allowed us to: (1) maintain correct stream order and execution semantics in the presence of parallel and asynchronous processing elements; (2) implement an operator scheduler framework to facilitate latency-oriented scheduling policies for executing complex workflows of MapReduce jobs; and (3) leverage much of the work that has gone into the last decade of stream processing research including: pipelined parallelism, incremental processing for both Map and Reduce operations, minimizing redundant computations, sharing of sub-queries, and adaptive query processing.C-MR was developed for use on a multiprocessor architecture, where we demonstrate its effectiveness at supporting high-performance stream processing even in the presence of load spikes and external workloads.},
booktitle = {Proceedings of Third International Workshop on MapReduce and Its Applications Date},
pages = {1–8},
numpages = {8},
keywords = {multi-core, mapreduce, stream processing},
location = {Delft, The Netherlands},
series = {MapReduce '12}
}
@inproceedings{backman12managing,
author = {Backman, Nathan and Fonseca, Rodrigo and \c{C}etintemel, Uundefinedur},
title = {Managing Parallelism for Stream Processing in the Cloud},
year = {2012},
isbn = {9781450311625},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2169090.2169091},
doi = {10.1145/2169090.2169091},
abstract = {Stream processing applications run continuously and have varying load. Cloud infrastructures present an attractive option to meet these fluctuating computational demands. Coordinating such resources to meet end-to-end latency objectives efficiently is important in preventing the frivolous use of cloud resources. We present a framework that parallelizes and schedules workflows of stream operators, in real-time, to meet latency objectives. It supports data- and task-parallel processing of all workflow operators, by all computing nodes, while maintaining the ordering properties of sorted data streams. We show that a latency-oriented operator scheduling policy coupled with the diversification of computing node responsibilities encourages parallelism models that achieve end-to-end latency-minimization goals. We demonstrate the effectiveness of our framework with preliminary experimental results using a variety of real-world applications on heterogeneous clusters.},
booktitle = {Proceedings of the 1st International Workshop on Hot Topics in Cloud Data Processing},
articleno = {1},
numpages = {5},
keywords = {stream processing, heterogeneous clusters, parallelism management},
location = {Bern, Switzerland},
series = {HotCDP '12}
}
@inproceedings{ferguson12jockey,
author = {Ferguson, Andrew D. and Bodik, Peter and Kandula, Srikanth and Boutin, Eric and Fonseca, Rodrigo},
title = {Jockey: Guaranteed Job Latency in Data Parallel Clusters},
year = {2012},
isbn = {9781450312233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2168836.2168847},
doi = {10.1145/2168836.2168847},
abstract = {Data processing frameworks such as MapReduce [8] and Dryad [11] are used today in business environments where customers expect guaranteed performance. To date, however, these systems are not capable of providing guarantees on job latency because scheduling policies are based on fair-sharing, and operators seek high cluster use through statistical multiplexing and over-subscription. With Jockey, we provide latency SLOs for data parallel jobs written in SCOPE. Jockey precomputes statistics using a simulator that captures the job's complex internal dependencies, accurately and efficiently predicting the remaining run time at different resource allocations and in different stages of the job. Our control policy monitors a job's performance, and dynamically adjusts resource allocation in the shared cluster in order to maximize the job's economic utility while minimizing its impact on the rest of the cluster. In our experiments in Microsoft's production Cosmos clusters, Jockey meets the specified job latency SLOs and responds to changes in cluster conditions.},
booktitle = {Proceedings of the 7th ACM European Conference on Computer Systems},
pages = {99–112},
numpages = {14},
keywords = {deadline, dynamic adaptation, SLO, data parallel, Dryad, MapReduce, scheduling},
location = {Bern, Switzerland},
series = {EuroSys '12}
}
@inproceedings{ferguson12pane,
Address = "San Jose, California, USA",
Author = "Andrew D. Ferguson and Arjun Guha and Jordan Place and Rodrigo Fonseca and Shriram Krishnamurthi",
Booktitle = " {Proceedings of the Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE)}",
Month = "April",
Title = "Participatory Networking",
Year = "2012"}
@techreport{martins11powerstates,
Author = "Marcelo Martins and Rodrigo Fonseca",
Institution = "Brown Computer Science",
Month = "October",
Number = "2011-03",
Title = " {The Case for Device Power States}",
Year = "2011"}
@inproceedings{martins10ctp,
author = {Martins, Marcelo and Fonseca, Rodrigo and Schmid, Thomas and Dutta, Prabal},
title = {Network-Wide Energy Profiling of CTP},
year = {2010},
isbn = {9781450303446},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1869983.1870063},
doi = {10.1145/1869983.1870063},
abstract = {We present our experiences evaluating the power-performance tradeoffs of a sensornet network protocol on a power-aware testbed. We characterize the power draw of the entire network while running the Collection Tree Protocol (CTP), as a function of low-power-listening interval. We find that message transmission counts are poor predictors for energy consumption on the CC2420 radio, that CTP routinely creates energy hotspots in the routing tree, and that conclusions based on protocol evaluation performed without low-power listening enabled provide little insight about the same protocol performance using low-power listening.},
booktitle = {Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems},
pages = {439–440},
numpages = {2},
location = {Z\"{u}rich, Switzerland},
series = {SenSys '10}
}
@inproceedings{ferguson10atc,
Address = "Boston, MA, USA",
Author = "Andrew Ferguson and Rodrigo Fonseca",
Booktitle = " {Proceedings of the USENIX Annual Technical Conference (ATC 2010)}",
Month = "June",
Series = "(Poster)",
Title = " {Understanding Filesystem Imbalance in Hadoop}",
Year = "2010"}
@inproceedings{fonseca10tracing,
Author = "Rodrigo Fonseca and Michael J. Freedman and George Porter",
Booktitle = "Proc. Internet Network Management Workshop / Workshop on Research on Enterprise Networking (INM/WREN)",
Month = "April",
Title = "Experiences with Tracing Causality in Networked Services",
Year = "2010"}
@inproceedings{naseer2018inspectorgadget,
title={InspectorGadget: Inferring Network Protocol Configuration for Web Services.},
author={Naseer, Usama and Benson, Theophilus},
booktitle={2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)},
pages={1624--1629},
year={2018},
organization={IEEE}
}
@inproceedings{naseer2017configtron,
title={Configtron: Tackling network diversity with heterogeneous configurations.},
author={Naseer, Usama and Benson, Theophilus},
booktitle={9th $\{$USENIX$\}$ Workshop on Hot Topics in Cloud Computing (HotCloud 17)},
year={2017}
}
@inproceedings{gao2018dcqcn+,
title={DCQCN+: Taming Large-scale Incast Congestion in RDMA over Ethernet Networks},
author={Gao, Yixiao and Yang, Yuchen and Chen, Tian and Zheng, Jiaqi and Mao, Bing and Chen, Guihai},
booktitle={2018 IEEE 26th International Conference on Network Protocols (ICNP)},
pages={110--120},
year={2018},
organization={IEEE}
}
@inproceedings{schwarzkopf2019gdprcbyc,
author = {Schwarzkopf, Malte and Kohler, Eddie and Kaashoek, M. Frans and Morris, Robert},
title = {GDPR Compliance by Construction},
booktitle = {Proceedings of the 2019 VLDB Workshop Towards Polystores that manage multiple Databases, Privacy, Security and/or Policy Issues for Heterogenous Data (Poly)},
month = aug,
year = 2019,
location = {Los Angeles, California, USA},
url = {https://cs.brown.edu/people/malte/pub/papers/2019-poly-gdpr.pdf},
}
@inproceedings{scc:eurosys19,
author = {Liu, Sheng and Benson, Theophilus A. and Reiter, Michael K.},
title = {Efficient and Safe Network Updates with Suffix Causal Consistency},
year = {2019},
isbn = {9781450362818},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3302424.3303965},
doi = {10.1145/3302424.3303965},
abstract = {Though centrally managed by a controller, a software-defined network (SDN) can still encounter routing inconsistencies among its switches due to the non-atomic updates to their forwarding tables. In this paper, we propose a new method to rectify these inconsistencies that is inspired by causal consistency, a consistency model for shared-memory systems. Applied to SDNs, causal consistency would imply that once a packet is matched to ("reads") a forwarding rule in a switch, it can be matched in downstream switches only to rules that are equally or more up-to-date. We propose and analyze a relaxed but functionally equivalent version of this property called suffix causal consistency (SCC) and evaluate an implementation of SCC in Open vSwitch and P4 switches, in conjunction with the Ryu and P4Runtime controllers. Our results show that SCC provides greater efficiency than competing consistent-update alternatives while offering consistency that is strong enough to ensure high-level routing properties (black-hole freedom, bounded looping, etc.).},
booktitle = {Proceedings of the Fourteenth EuroSys Conference 2019},
articleno = {23},
numpages = {15},
keywords = {software-defined networking, consistent update, model checking, causal consistency},
location = {Dresden, Germany},
series = {EuroSys '19}
}
@inproceedings{harmony:hotos19,
author = {Benson, Theophilus A.},
title = {In-Network Compute: Considered Armed and Dangerous},
year = {2019},
isbn = {9781450367271},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3317550.3321436},
doi = {10.1145/3317550.3321436},
abstract = {Programmable data planes promise unprecedented flexibility and innovation. But enormous management issues arise when these programmable data-planes, and the in-network compute functionality they enable, are deployed within production networks. In this paper, we present an overview of these management challenges, then explore the limitations of existing management techniques. Finally, we propose a system, Harmony, that encapsulates new abstractions and primitives to address these problems.},
booktitle = {Proceedings of the Workshop on Hot Topics in Operating Systems},
pages = {216–224},
numpages = {9},
keywords = {in-network computing, programmable network devices},
location = {Bertinoro, Italy},
series = {HotOS '19}
}
@inproceedings{mozart:socc19,
author = {Zhou, Zhenyu and Benson, Theophilus A.},
title = {Composing SDN Controller Enhancements with Mozart},
year = {2019},
isbn = {9781450369732},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3357223.3362712},
doi = {10.1145/3357223.3362712},
abstract = {Over the last few years, we have experienced a massive transformation of the Software Defined Networking ecosystem with the development of SDNEnhancements, e.g., Statesman, ESPRES, Pane, and Pyretic, to provide better composability, better utilization of TCAM, consistent network updates, or congestion free updates. The end-result of this organic evolution is a disconnect between the SDN applications and the data-plane. A disconnect which can impact an SDN application's performance and efficacy.In this paper, we present the first systematic study of the interactions between SDNEnhancements and SDN applications -- we show that an SDN application's performance can be significantly impacted by these SDNEnhancements: for example, we observed that the efficiency of a traffic engineering SDN application was reduced by 24.8%. Motivated by these insights, we present, Mozart, a redesigned SDN controller centered around mitigating and reducing the impact of these SDNEnhancements. Using two prototypes interoperating with seven SDN applications and two SDNEnhancements, we demonstrate that our abstractions require minimal changes and can restore an SDN application's performance. We analyzed Mozart's scalability and overhead using large scale simulations of modern cloud networks and observed them to be negligible.},
booktitle = {Proceedings of the ACM Symposium on Cloud Computing},
pages = {351–363},
numpages = {13},
keywords = {Composition, Compilers, Software Defined Networks},
location = {Santa Cruz, CA, USA},
series = {SoCC '19}
}
@inproceedings{mud:sosr19,
author = {Hamza, Ayyoob and Gharakheili, Hassan Habibi and Benson, Theophilus A. and Sivaraman, Vijay},
title = {Detecting Volumetric Attacks on LoT Devices via SDN-Based Monitoring of MUD Activity},
year = {2019},
isbn = {9781450367103},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3314148.3314352},
doi = {10.1145/3314148.3314352},
abstract = {Smart environments equipped with IoT devices are increasingly under threat from an escalating number of sophisticated cyber-attacks. Current security approaches are inaccurate, expensive, or unscalable, as they require static signatures of known attacks, specialized hardware, or full packet inspection. The IETF Manufacturer Usage Description (MUD) framework aims to reduce the attack surface on an IoT device by formally defining its expected network behavior. In this paper, we use SDN to monitor compliance with the MUD behavioral profile, and develop machine learning methods to detect volumetric attacks such as DoS, reflective TCP/UDP/ICMP flooding, and ARP spoofing to IoT devices.Our first contribution develops a machine for detecting anomalous patterns of MUD-compliant network activity via coarse-grained (device-level) and fine-grained (flow-level) SDN telemetry for each IoT device, thereby giving visibility into flows that contribute to a volumetric attack. For our second contribution we measure network behavior of IoT devices by collecting benign and volumetric attacks traffic traces in our lab, label our dataset, and make it available to the public. Our last contribution prototypes a full working system (built with an OpenFlow switch, Faucet SDN controller, and a MUD policy engine), demonstrates its application in detecting volumetric attacks on several consumer IoT devices with high accuracy, and provides insights into cost and performance of our system. Our data and solution modules are released as open source to the community.},
booktitle = {Proceedings of the 2019 ACM Symposium on SDN Research},
pages = {36–48},
numpages = {13},
location = {San Jose, CA, USA},
series = {SOSR '19}
}
@article{pdp:dagstuhl,
author = {Gianni Antichi and
Theophilus Benson and
Nate Foster and
Fernando M. V. Ramos and
Justine Sherry},
title = {Programmable Network Data Planes (Dagstuhl Seminar 19141)},
journal = {Dagstuhl Reports},
volume = {9},
number = {3},
pages = {178--201},
year = {2019},
url = {https://doi.org/10.4230/DagRep.9.3.178},
doi = {10.4230/DagRep.9.3.178},
timestamp = {Fri, 13 Sep 2019 01:00:00 +0200},
biburl = {https://dblp.org/rec/bib/journals/dagstuhl-reports/AntichiBFRS19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{minerva:bigdata18,
author = {Christopher Streiffer and
Ramya Raghavendra and
Theophilus Benson and
Mudhakar Srivatsa},
title = {Learning to Simplify Distributed Systems Management},
booktitle = {\{IEEE\} International Conference on Big Data, Big Data 2018, Seattle,
WA, USA, December 10-13, 2018},
pages = {1837--1845},
year = {2018},
crossref = {DBLP:conf/bigdataconf/2018},
url = {https://doi.org/10.1109/BigData.2018.8622058},
doi = {10.1109/BigData.2018.8622058},
timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
biburl = {https://dblp.org/rec/bib/conf/bigdataconf/StreifferRBS18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{p4visor:conext18,
author = {Zheng, Peng and Benson, Theophilus and Hu, Chengchen},
title = {P4Visor: Lightweight Virtualization and Composition Primitives for Building and Testing Modular Programs},
year = {2018},
isbn = {9781450360807},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3281411.3281436},
doi = {10.1145/3281411.3281436},
abstract = {Programmable data planes, PDPs, enable an unprecedented level of flexibility and have emerged as a promising alternative to existing data planes. Despite the rapid development and prototyping cycles that PDPs promote, the existing PDP ecosystem lacks appropriate abstractions and algorithms to support these rapid testing and deployment life-cycles. In this paper, we propose P4Visor, a lightweight virtualization abstraction that provides testing primitives as a first-order citizen of the PDP ecosystem. P4Visor can efficiently support multiple PDP programs through a combination of compiler optimizations and program analysis-based algorithms. P4Visor s algorithm improves over state-of-the-art techniques by significantly reducing the resource overheads associated with embedding numerous versions of a PDP program into hardware. To demonstrate the efficiency and viability of P4Visor, we implemented and evaluated P4Visor on both a software switch and an FPGA-based hardware switch using fourteen different PDP programs. Our results demonstrate that P4Visor introduces minimal overheads (less than 1%) and is one order of magnitude more efficient than existing PDPs primitives for concurrently supporting multiple programs.},
booktitle = {Proceedings of the 14th International Conference on Emerging Networking EXperiments and Technologies},
pages = {98–111},
numpages = {14},
keywords = {code merge, programmable data plane, testing},
location = {Heraklion, Greece},
series = {CoNEXT '18}
}
@inproceedings{inspectorgadget:icdcs18,
author = {Usama Naseer and
Theophilus Benson},
title = {InspectorGadget: Inferring Network Protocol Configuration for Web
Services},
booktitle = {38th {IEEE} International Conference on Distributed Computing Systems,
{ICDCS} 2018, Vienna, Austria, July 2-6, 2018},
pages = {1624--1629},
year = {2018},
crossref = {DBLP:conf/icdcs/2018},
url = {https://doi.org/10.1109/ICDCS.2018.00183},
doi = {10.1109/ICDCS.2018.00183},
timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
biburl = {https://dblp.org/rec/bib/conf/icdcs/NaseerB18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{mphula:netcompute18,
author = {Benet, Cristian Hernandez and Kassler, Andreas J. and Benson, Theophilus and Pongracz, Gergely},
title = {MP-HULA: Multipath Transport Aware Load Balancing Using Programmable Data Planes},
year = {2018},
isbn = {9781450359085},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3229591.3229596},
doi = {10.1145/3229591.3229596},
abstract = {Datacenter networks offer a large degree of multipath in order to provide large bisectional bandwidth. The end-to-end performance is determined by the load-balancing strategy which needs to be designed to effectively manage congestion. Consequently, congestion aware load-balancing strategies such as CONGA or HULA have been designed. Recently, more and more applications that are hosted on cloud servers use multipath transport protocols such as MPTCP. However, in the presence of MPTCP, existing load-balancing schemes including ECMP, HULA or CONGA may lead to suboptimal forwarding decisions where multiple MPTCP subflows of one connection are pinned on the same bottleneck link.In this paper, we present MP-HULA, a transport layer multi-path aware load-balancing scheme using Programmable Data Planes. First, instead of tracking congestion information for the best path towards the destination, each MP-HULA switch tracks congestion information for the best-k paths to a destination through the neighbor switches. Second, we design MP-HULA using Programmable Data Planes, where each leaf switch can identify, using P4, which MPTCP subflow belongs to which connection. MP-HULA then load-balances different MPTCP subflows of a MPTCP connection on different next hops considering congestion state while aggregating bandwidth. Our evaluation shows that MP-HULA with MPTCP outperforms HULA in average flow completion time (2.1x at 50% load, 1.7x at 80% load).},
booktitle = {Proceedings of the 2018 Morning Workshop on In-Network Computing},
pages = {7–13},
numpages = {7},
keywords = {Network Congestion, In-Network Load Balancing, Multipath, Programmable Switches},
location = {Budapest, Hungary},
series = {NetCompute '18}
}
@inproceedings{deepconfig:netai18,
author = {Salman, Saim and Streiffer, Christopher and Chen, Huan and Benson, Theophilus and Kadav, Asim},
title = {DeepConf: Automating Data Center Network Topologies Management with Machine Learning},
year = {2018},
isbn = {9781450359115},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3229543.3229554},
doi = {10.1145/3229543.3229554},
abstract = {In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware.In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals share design and architectural similarities. We present a framework for developing general intermediate representations of network topologies using deep learning that is amenable to solving a large class of data center problems. We develop a framework, DeepConf, that simplifies the process of configuring and training deep learning agents by using our intermediate representation to learn different tasks. To illustrate the strength of our approach, we implemented and evaluated a DeepConf-agent that tackles the data center topology augmentation problem. Our initial results are promising --- DeepConf performs comparably to the optimal solution.},
booktitle = {Proceedings of the 2018 Workshop on Network Meets AI & ML},
pages = {8–14},
numpages = {7},
keywords = {deep reinforcement learning, topology management, Data center networks},
location = {Budapest, Hungary},
series = {NetAI'18}
}
@inproceedings{shadowp4:sigcomm18,
author = {Zheng, Peng and Benson, Theophilus and Hu, Chengchen},
title = {ShadowP4: Building and Testing Modular Programs},
year = {2018},
isbn = {9781450359153},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3234200.3234231},
doi = {10.1145/3234200.3234231},
booktitle = {Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos},
pages = {150–152},
numpages = {3},
keywords = {testing, code merge, programmable data plane},
location = {Budapest, Hungary},
series = {SIGCOMM '18}
}
@inproceedings{chopin:apnet17,
author = {Benson, Theophilus},
title = {A Call To Arms for Tackling the Unexpected Implications of SDN Controller Enhancements},
year = {2017},
isbn = {9781450352444},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3106989.3107006},
doi = {10.1145/3106989.3107006},
abstract = {The last few years have seen a massive and organic transformation of the Software Defined Networking ecosystem with the development of enhancements, e.g., Statesman, ESPRES, PANE, and Athens, to provide better composability, better utilization of TCAM, consistent network updates, or congestion free updates. The end-result of this organic evolution is a disconnect between the SDN applications and the dataplane. A disconnect which can impact an SDN application's performance or correctness.In this paper, we present the first systematic study of the interactions between enhancements and SDN applications -- we show that an application's performance can be significantly impacted by these enhancements: with the efficiency of a traffic engineering App reduced by 24.8%. Motivated by these insights, we argue for a redesign of the SDN controller centered around mitigating and reducing the impact of these enhancements. We demonstrate through an initial prototype and with experiments that our abstractions require minimal changes and can restore an SDN application's performance and efficiency.},
booktitle = {Proceedings of the First Asia-Pacific Workshop on Networking},
pages = {15–21},
numpages = {7},