Minimizing Cost of Virtual Machines for Deadline-Constrained MapReduce Applications in the Cloud

As Cloud computing provides Anything as a Service (XaaS), many applications can be developed and run on the Cloud without concerns of platforms. Data-incentive applications are also easily developed on virtual machines provided by the Cloud. In this work, we investigate cost-effective resource provisioning for MapReduce applications with deadline constraints, as the MapReduce programming model is useful and powerful in developing data-incentive applications. When users want to run MapReduce applications, they submit jobs to a Cloud resource broker which allocates appropriate virtual machines with consideration of SLAs (Service-Level Agreements). The goal of resource provisioning in this paper is to minimize the cost of virtual machines for executing MapReduce applications without violating their deadlines to be finished by. We propose two resource provisioning approaches: one based on listed pricing policies and the other based on deadline-aware tasks packing. Throughout simulations, we evaluate and analyze them in various ways.