Scheduling Policies for Stability and Optimal Server Running Cost in Cloud Computing Platforms

TL;DR

This paper introduces new scheduling algorithms for cloud platforms that optimize job throughput and server costs, balancing delay and migration considerations through theoretical analysis and simulations.

Contribution

It presents novel online and offline scheduling algorithms that minimize migration and operational costs while ensuring system stability, with theoretical guarantees and practical performance evaluation.

Findings

Algorithms achieve near-optimal cost with stability guarantees

Proposed methods outperform existing algorithms in simulations

Trade-offs between delay and cost are effectively managed

Abstract

We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically this algorithm yields a trade-off between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. Later, we consider offline job migration that incurs migration delays. We again present throughput optimal algorithms that minimize server running cost. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.