Mitigating the Cost, Performance, and Power Overheads Induced by Load Variations in Multicore Cloud Servers
Date of Award
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Mithuna S. Thottethodi
Mithuna S. Thottethodi
Committee Member 1
Committee Member 2
T. N. Vijaykumar
Committee Member 3
Vijay S. Pai
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.
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.
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.
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.
Hong, Yu-Ju, "Mitigating the Cost, Performance, and Power Overheads Induced by Load Variations in Multicore Cloud Servers" (2013). Open Access Dissertations. 164.