On Cloud computational models and the heterogeneity challenge

Abstract

Abstract Cloud computing is by far the most cost-effective technology for hosting Internet-scale services and applications. The MapReduce model, in particular, is largely used nowadays in Cloud infrastructures to meet the demand of large-scale data and computation intensive applications. Despite its success, the implications of MapReduce on the management of Cloud workload and cluster resources are still largely unstudied. In this article, we show that dealing with the heterogeneity of workloads and machine capabilities is a key challenge. In today’s cloud environment, workloads can have varied sizes, lengths, resource requirements, and arrival rates. The machines also have varied CPU, memory, I/O speed, and network bandwidth capacities. Jointly they pose difficult challenges pertaining, among others, to job scheduling, task and data placement, resource sharing and resource allocation. We analyze the heterogeneity challenge in these specific problem domains and survey the representative state-of-the-art works that try to address them. We found that although advances are made that partially address some of the outlined challenges, there are even more open challenges yet to be explored, and this topic at large is ripe for scientific contributions.