Cloud computing is an emerging trend where clients are billed for services on a pay-per-use basis. Service level agreements define the formal negotiations between the clients and the service providers on common metrics such as processing power, memory and bandwidth. In the case of service level agreement violations, the service provider is penalised. From service provider's point of view, providing cloud services efficiently within the negotiated metrics is an important problem. Particularly, in large-scale data center settings, manual administration for resource allocation is not a feasible option. Service providers aim to maximize resource utilization in the data center, as well as, avoiding service level agreement violations. On the other hand, from the client's point of view, the cloud must continuously ensure enough resources to the changing workloads of hosted application environments and services. Therefore, an autonomous cloud manager that is capable of dynamically allocating resources in order to satisfy both the client and the service provider's requirements emerges as a necessity. In this thesis, we focus on the autonomous resource allocation in cloud computing environments. A distributed resource consolidation manager for clouds, called IMPROMPTU, was introduced in our previous studies. IMPROMPTU adopts a threshold based reactive design where each unique physical machine is coupled with an autonomous node agent that manages resource consolidation independently from the rest of the autonomous node agents. In our previous studies, IMPROMPTU demonstrated the viability of Multiple Criteria Decision Analysis (MCDA) to provide resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and service level agreement violations under the management of three well-known outranking-based methods called PROMETHEE II, ELECTRE III and PAMSSEM II. The interesting question of whether more efficient single synthesizing criterion and outranking based MCDA methods exist was left open for research. This thesis addresses these limitations by analysing the capabilities of IMPROMPTU using a comprehensive set of single synthesizing criterion and outranking based MCDA methods in the context of dynamic resource allocation. The performances of PROMETHEE II, ELECTRE III, PAMSSEM II, REGIME, ORESTE, QUALIFEX, AHP and SMART are investigated by in-depth analysis of simulation results. Most importantly, the question of what denotes the properties of good MCDA methods for this problem domain is answered. / Graduate / 0984