Efficient autonomic cloud computing using online discrete event simulation

Abstract

Interest is growing in open source tools that let organizations build IaaS clouds using their own internal infrastructures, alone or in conjunction with external ones. A key component in such private/hybrid clouds is virtual infrastructure management, i.e., the dynamic orchestration of virtual machines, based on the understanding and prediction of performance at scale, with uncertain workloads and frequent node failures. Part of the research community is trying to solve this and other IaaS problems looking to Autonomic Computing techniques, that can provide, for example, better management of energy consumption, quality of service (QoS), and unpredictable system behaviors. In this context, we first recall the main features of the NAM framework devoted to the design of distributed autonomic systems. Then we illustrate the organization and policies of a NAM-based Workload Manager, focusing on one of its components, the Capacity Planner. We show that, when it is not possible to obtain optimal energy-aware plans analytically, sub-optimal plans can be autonomically obtained using online discrete event simulation. Specifically, the proposed approach allows to cope with a broader range of working conditions and types of workloads.