Optimal load balancing for heterogeneous clusters

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Performance Analysis of Computer Systems Lab

 Computer Science Department, Stony Brook University

 Location: Room 336, New CS Building. Lab PI: Anshul Gandhi  (anshul (at) cs.stonybrook.edu).

Optimal load balancing for heterogeneous clusters
(August 2014 - present)

Large-scale cluster deployments are common in today's cloud-hosted application environments. Online service providers such as Amazon, Facebook and Google often employ clusters of thousands of nodes for serving web requests. These online services often handle thousands of customer requests per second.

However, developing efficient load balancers for large, distributed clusters is challenging for several reasons: (i) large clusters require numerous scheduling decisions per second, (ii) such clusters typically consist of heterogeneous servers that widely differ in their computing power, and (iii) such clusters often experience significant changes in load.

The goal of this project is to develop scalable, heterogeneity-aware load balancers. We will employ queueing-theoretic ideas to design simple yet adaptive load balancers that provide near-optimal performance.

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