Cloud computing has provided the convenience for many IT-related and traditional industries to use feature-rich services to process complex requests. Various services are deployed in the cloud and they interact with each other to deliver the required results. How to effectively manage these services, the number of which is ever increasing, within the cloud has unavoidably become a critical issue for both tenants and service providers of the cloud. In this thesis, we develop the novel resource provision frameworks to determine resources provision for interactive services. Next, we propose the algorithms for mapping Virtual Machines (VMs) to Physical Machines (PMs) under different constraints, aiming to achieve the desired Quality-of-Services (QoS) while optimizing the provisions in both computing resources and communication bandwidth. Finally, job scheduling may become a performance bottleneck itself in such a large scale cloud. In order to address this issue, the distributed job scheduling framework has been proposed in the literature. However, such distributed job scheduling may cause resource conflict among distributed job schedulers due to the fact that individual job schedulers make their job scheduling decisions independently. In this thesis, we investigate the methods for reducing resource conflict. We apply the game theoretical methodology to capture the behaviour of the distributed schedulers in the cloud. The frameworks and methods developed in this thesis have been evaluated with a simulated workload, a large-scale workload trace and a real cloud testbed.