Cloud computing is an on-demand, ubiquitous provisioning of resources. Individual users and many organizations rely on Cloud to support their infrastructure, platform, and applications. To meet these huge demands, Cloud providers have to build enormous Cloud Data Centers (CDCs).CDCs comprise of a lot of resources and these are given to users haphazardly. Arbitrary provisioning of resources on different hosts creates a problem of low usage of host machines. This leads to the problem of inefficient use of resources. More active numbers of hosts contribute to the increase in energy consumption. This will not only increase cloud providers operating cost but also emit a large amount of Carbon Dioxide. Different techniques are used to maximize resource utilization and minimization in energy consumption like VM consolidation, cooling, and scheduling. Server consolidation maximize resource usage and minimize the active number of Physical Machines (PMs) in DCs. Server consolidation includes hosts under-load detection, host overload detection, VM Selection, VM Migration, and VM placement. A number of techniques are proposed by researchers for VM placement which includes Multi-Objective VM placement, Best Fit Decreasing (BFD), Genetic Algorithm Based Approach (GABP), Glow-worn Swarm Optimization, Multi-Objective Ant Colony, and Family Genetic Algorithm (FGA). Most of the proposed techniques consider efficient resource utilization however ignored energy consumption. In some other techniques, energy consumption is considered but they do not consider SLA violation. The proposed technique considers all problems. This research contributes to an Efficient Adaptive Migration Algorithm (EAMA) for dynamic VM migration and placement. The proposed technique minimizes the energy consumption, active number of hosts, maximize the resource utilization and keep a balance in Service Level Agreement (SLA) requirements. For the performance evaluation of the proposed technique CloudSim toolkit is utilized Chapter 1 describes the introduction of the Cloud computing. We have also discussed the aim and objectives of the research. The brief summary of the following chapters is also discussed.