Horizontal auto-scaling and process migration mechanism for cloud services with skewness algorithm

Cloud services are based upon virtualization technology. When a user requests for resources for computing, the cloud service provider creates a virtual machine and allocate necessary resources such as CPU and RAM sufficient for usages to a user. The user does not need to be aware of the physical machines. When resource requirement increases due to the number of accesses and workload resulting in more allocated resources needed, the cloud service provider must ensure its quality of service (QoS) and the guaranteed response time to the provided services in order to maintain the customer satisfaction level and its reputation. To minimize the monitoring process overhead of the cloud service provider's data center administrators, an efficient auto-scaling mechanism is needed for efficiently managing the data center. A horizontal auto-scaling with process migration mechanism for cloud services with skewness algorithm is then proposed and implemented in a Java-based cloud simulation and tested in two different scenarios: threshold-based auto-scaling without skewness algorithm and process migration and one with skewness algorithm and process migration. The results show that the skewness algorithm and process migration can help smooth out the fluctuation of the number of virtual machines being spawned and then very soon be deleted resulting in the reduction of the overhead of such process.