Addressing shared resource contention in datacenter servers

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
Multicore processors
Shared resource contention
Performance evaluation

Servers are major energy consumers in modern datacenters. Much of that energy is wasted because applications compete for shared resources and suffer severe performance penalties due to resource contention. Contention for shared resources remains an unsolved problem in existing datacenters despite significant research efforts dedicated to this problem in the past. The goal of this work is to investigate how and to what extent contention for shared resource can be mitigated via workload scheduling. Scheduling is an attractive tool, because it does not require extra hardware and is relatively easy to integrate into the system. I have designed and implemented multiple Open Source and proprietary schedulers during my work on this dissertation. Most notably, I introduced the Distributed Intensity Online (DIO) scheduler to target the shared resource contention in the memory hierarchy of Uniform Memory Access (UMA) systems, followed by the Distributed Intensity NUMA Online (DINO) scheduler that I designed to improve performance and decrease power consumption on Non Uniform Memory Access (NUMA) servers. As part of my internship in HP Labs, I designed a work conserving scheduler that prioritizes access to the multiple CPU cores on an industry level multicore server, thus managing contention for CPU and improving server power efficiency. Finally, the Clavis2D framework extends the contention awareness to the datacenter level and provides a comprehensive cluster scheduling solution that simultaneously takes into account multiple performance- and power-related goals. My dissertation work utilizes state-of-the-art industry level datacenter infrastructure, does not require any modification or prior knowledge about the workload and provides significant performance and energy benefits on-the-fly.

Document type: 
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Senior supervisor: 
Alexandra Fedorova
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) Ph.D.