Heavy Traffic Optimal Resource Allocation Algorithms for Cloud Computing Clusters

TL;DR

This paper analyzes resource allocation algorithms in cloud computing clusters, demonstrating that join-the-shortest-queue and power-of-two-choices routing with MaxWeight scheduling are queue length optimal under heavy traffic conditions.

Contribution

It proves that well-known throughput optimal algorithms are also queue length optimal in heavy traffic, extending their theoretical understanding.

Findings

Algorithms are queue length optimal in heavy traffic limit.

Join-the-shortest-queue and power-of-two-choices routing are throughput optimal.

MaxWeight scheduling achieves queue length optimality in the studied model.

Abstract

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.