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Trace analysis in Alibaba production clusters, which co-locates different workloads to improve resource efficiency. |
Presented in IWQoS 2019.
Authors: Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, Yungang Bao (ICT, CAS & Alibaba)
Trace: https://github.com/alibaba/clusterdata/tree/v2018
This paper analyzes the Alibaba production trace, which co-locates different workloads to improve resource efficiency.
Trace: Homogeneous cluster. Each server with 96 cores and 1 unit memory normalized.
- Memory becomes the new bottleneck of datacenters => require efficient memory reclaim techniques
- Batch-processing applications are treated as second-class citizens and restricted to utilize limited resources => potentially overprotect latency-critical applications
- More than 90% of latency-critical applications are written in Java => massive self-contained JVMs complicate resource management
There are many Java applications in Alibaba, which may cause these issues. Is this a common scenario?