PARTIES: QoS-Aware Resource Partitioning for Multiple Interactive Services

By Shuang Chen, Christina Delimitrou, José F. Martínez. International Conference on Architectural Support for Programming Languages and Operating Systems, 2019

332 citations recorded by the source corpus.

Multi-tenancy in modern datacenters is currently limited to a single latency-critical, interactive service, running alongside one or more low-priority, best-effort jobs. This limits the efficiency gains from multi-tenancy, especially as an increasing number of cloud applications are shifting from batch jobs to services with strict latency requirements. We present PARTIES, a QoS-aware resource manager that enables an arbitrary number of interactive, latency-critical services to share a physical node without QoS violations. PARTIES leverages a set of hardware and software resource partitioning mechanisms to adjust allocations dynamically at runtime, in a way that meets the QoS requirements of each co-scheduled workload, and maximizes throughput for the machine. We evaluate PARTIES on state-of-the-art server platforms across a set of diverse interactive services. Our results show that PARTIES improves throughput under QoS by 61% on average, compared to existing resource managers, and that the rate of improvement increases with the number of co-scheduled applications per physical host.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/89d8a0ed7f133de2f3f643ee459c32c4fb8dfdca

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  1. Liang Mao
  2. Haiyang Ding
  3. Yihui Feng
  4. Jing Guo
  5. Zihao Chang
  6. Sa Wang
  7. Yungang Bao

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