Mitigating the Cost, Performance, and Power Overheads Induced by Load Variations in Multicore Cloud Servers

<p>Load variations whether in space or time pose a significant challenge to system designers. These load variations may induce inefficiencies such as load imbalance and over-provisioning, resulting in performance/power/cost overheads. The goal of my research is to mitigate such variation-induced overheads in multicore cloud servers.</p> <p>First, I focus on power/performance overheads in on-chip networks of a multicore chip. We design an on-chip network that is robust in both performance and energy across applications for time- and space-varying loads. Existing flow control mechanisms that perform well at high (low) loads suffer power and/or energy overheads at low (high) loads. In contrast, our design dynamically adapts flow control to achieve power and performance of the better-suited flow-control mechanism at all loads.</p> <p>Second, I target cost overheads resulting from time-varying loads for applications hosted in an Infrastructure-as-a-Service (IaaS) cloud. While IaaS clouds may enable significant cost-savings by allowing elastic provisioning, the uncertainty of time-varying loads impose additional cost to maintain quality of service. I demonstrate that, with some knowledge of the statistical properties of time-varying load, one can maximize cost-savings while satisfying response-time targets.</p> <p>Finally, I propose to mitigate the impact of data popularity variations in cloud servers. Sharding is a common technique to partition data among scale-out servers. Unfortunately, skewed popularity of data-elements can cause significant load imbalance among shard servers, leading to response time degradation. I design an augmented variant of a well-known memory-caching system to identify and replicate popular read-mostly data elements, thus achieving better load balance and higher performance.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2013

Author dc:creator

• Hong, Yu-Ju

Contributors dc:contributor

• Mithuna S. Thottethodi
• Anand Raghunathan
• T. N. Vijaykumar
• Vijay S. Pai

• applied sciences
• multicore cloud servers
• load variations
• on-chip networks
• Computer Engineering

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/164

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1051