Probabilistic provisioning and scheduling in uncertain Cloud environments

Abstract

Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.