This thesis involves the area of virtualization. We have studied about improving load balancing in data center by using automated live migration techniques. The main idea is to migrate virtual machine(s) automatically from high loaded hosts to less loaded hosts with efficiency. The successful implementation can help administrator of data center maintain a load-balanced environment with less effort than before. For example, such a system can automatically identify hotspots and coldspots in a large data center and also decide which virtual machine to migrate, and which host should the machine be migrated to. We have implemented previously developed Push and Pull strategies on a real testbed for Xen and KVM. A new strategy, Hybrid, which is the combination of Push and Pull, has been created. All scripts applied in the experiments are Python-based for further integration to the orchestration framework OpenStack. By implementing the algorithms on a real testbed, we have solved a node failure problem in the algorithms, which was not detected previously through simulation. The results from simulation and those from testbed are similar. E.g. Push strategy has quick responses when the load is medium to high, while Pull strategy has quick responses when the load is low to medium. The Hybrid strategy behaves similar to Push strategy with high load and to Pull strategy with low load, but with greater number of migration attempts, and it responds quickly regardless to the load. The results also show that our strategies are able to handle different incidents such as burst, drain, or fluctuation of load over time. The comparison of results from different hypervisors, i.e., Xen and KVM, shows that both hypervisors conduct in the same way when applying same strategies in the same environment. It means the strategies are valid for both of them. Xen seems to be faster in improving the System performance. The migration attempts are similar, but KVM has much less Migrations over time than Xen with same scenario.