Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing

AbstractCloud computing is an emerging computing technology for large data centers that maintains computational resources through the internet, rather than on local computers. With the increasing popularity of cloud computing, also increase the demand of cloud resources. In Infrastructure-as-a-Service datacenters, the placements of Virtual Machines (VMs) on physical hosts are dynamically optimized in response to resource utilization of the hosts. VM migration provides the capability to balance the load, system maintenance and fault tolerance etc. However, existing migration techniques, used to migrate virtual machines keeping data images of VMs in host and skipping transfer of unchanged data fields to reduce the amount of transfer data during migration, if number of migrations increases, number of data images stored on host are also increased, this causes memory starvation. In this paper, we propose a technique that reduces the size of data image stored on source host before migration. When a Virtual Machine migrates to another host, the data image for that VM is kept in the source host after removing unwanted data according to the probability factor. When the VM migrates back to the original host later, the kept memory image will be “reused”, i.e. data which are identical to the kept data will not be transferred and comparative to existing system the size of memory image is small. To validate this approach, results evaluated using different threshold levels and probability factor of change in data. Proposed system required less memory to store the memory image and allow more VMs to be hosted. Specifically, our proposed work is used to improve resource efficiency throughout by reducing the size of memory image that is stored on source host. Evaluations show that size of memory image reduced 33% (approx) of unnecessary memory consumption