Cost efficient scheduling of MapReduce applications on public clouds

Abstract

MapReduce framework has been one of the most prominent ways for efficient processing large amount of data requiring huge computational capacity. On-demand computing resources of Public Clouds have become a natural host for these MapReduce applications. However, the decision of what type and in what amount computing and storage resources should be rented is still a user’s responsibility. This is not a trivial task particularly when users may have performance constraints such as deadline and have several Cloud product types to choose with the intention of not spending much money. Even though there are several existing scheduling systems, however, most of them are not developed to manage the scheduling of MapReduce applications. That is, they do not consider things such as number of map and reduce tasks that are needed to be scheduled and heterogeneity of Virtual Machines (VMs) available. This paper proposes a novel greedy-based MapReduce application scheduling algorithm (MASA) that considers the user’s constraints in order to minimize cost of renting Cloud resources while considering Service Level Agreements (SLA) in terms of the user given budget and deadline constraints. The simulation results show that MASA can achieve 25–50% cost reduction in comparison to current SLA agnostic methods and there is only 10% performance disparity between MASA and an exhaustive search algorithm.