Scheduling real‐time bag‐of‐tasks applications with approximate computations in SaaS clouds

Abstract

SummarySoftware as a Service (SaaS) cloud computing has emerged as an attractive platform to tackle various problems of the traditional software distribution model, such as the requirement to acquire and maintain expensive hardware and software infrastructure. SaaS, however, involves many challenges, mainly due to the heterogeneity and multitenancy of the underlying host environment, as well as the nature of the applications executed on such platforms. Applications are usually bags‐of‐tasks, consisting of independent component tasks that can be executed in any order, featuring different degrees of variability in their computational demands. Furthermore, according to the service level agreement between the cloud provider and the end‐users, the execution of such applications must typically complete within a deadline, providing results of acceptable quality. Consequently, one of the most important aspects of SaaS cloud computing is the effective scheduling of multiple parallel applications, avoiding any service level agreement violations. Towards this direction, our contribution in this paper is twofold: (1) We enhance some of the most commonly used scheduling algorithms for bag‐of‐tasks applications, by utilizing approximate computations, and (2) we investigate the impact of different levels of variability in the computational demands of the applications on the performance of the examined heuristics.