Stochastic models of load balancing and scheduling in cloud computing clusters

TLDR

Cloud computing services are becoming ubiquitous, serving as the primary source of computing power for enterprises and personal computing applications. The study focuses on resource allocation, specifically designing load‑balancing and VM‑scheduling algorithms for cloud clusters. The authors model a cloud cluster with stochastic job arrivals requesting VMs, define its capacity limits, and evaluate delay performance of alternative load‑balancing and scheduling algorithms through simulation. Best‑Fit scheduling is not throughput‑optimal, and the authors propose alternatives that can achieve any desired fraction of the cloud’s capacity region.

Abstract

Cloud computing services are becoming ubiquitous, and are starting to serve as the primary source of computing power for both enterprises and personal computing applications. We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources such as CPU, memory and storage space. While there are many design issues associated with such systems, here we focus only on resource allocation problems, such as the design of algorithms for load balancing among servers, and algorithms for scheduling VM configurations. Given our model of a cloud, we first define its capacity, i.e., the maximum rates at which jobs can be processed in such a system. Then, we show that the widely-used Best-Fit scheduling algorithm is not throughput-optimal, and present alternatives which achieve any arbitrary fraction of the capacity region of the cloud. We then study the delay performance of these alternative algorithms through simulations.