Capacity Management Approaches for Compute Clouds

Sammanfattning: Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches.