Resource Management Mechanisms To Support Slas In Iaas Clouds

In this chapter we focus on the Infrastructure as a Service (IaaS) model that epitomizes many of the generic resource management problems arising in cloud computing.The authors consider elastic multi-VM workloads corresponding to multi-tier application and study the fundamental problems of VM placement optimization, subject to policy constraints, elasticity requirements, and performance SLAs. Numerous algorithmic and architecture proposals appeared recently in the area of resource provisioning in IaaS. The chapter provides a comprehensive review of related work in this field and presents the authors' recent scientific findings in this area obtained in the framework of an EU funded project, RESERVOIR. The chapter discusses horizontal elasticity support in IaaS, its relationship to SLA protection, VM placement optimization and efficient capacity management to improve cost-efficiency of cloud providers. Elastic services comprise multiple virtualized resources that can be added and deleted on demand to match variability in the workload. A Service owner profiles the service to determine its most appropriate sizing under different workload conditions. This variable sizing is formalized through a service level agreement (SLA) between the service owner and the cloud provider. The Cloud provider obtains maximum benefit when it succeeds to fully allocate the resource set demanded by the elastic service subject to its SLA. Failure to do so may result in SLA breach and financial losses to the provider. The chapter defines a novel combinatorial optimization problem called elastic services placement problem to maximize the provider's benefit from SLA compliant placement.It demonstrates the feasibility of our approach through a simulation study, showing that we are capable of consistently obtaining good solutions in a time efficient manner. In addition, we discuss how resource utilization level can be improved through an advanced capacity management leveraging elastic workload resource consumption variability.