Optimizing jobs' completion time in cloud systems during virtual machine placement

Cloud computing gives beneficial services to share large scale of information, storage resources, computing resources, and knowledge for research. Cloud users deploy their own applications and related data on a pay-as-you-go basis. virtual machines (VMs) usually host these data-intensive applications. The performance of these applications often depends on workload types I/O data-intensive or I/O computation, workload volume, CPU attributes on computing nodes CNs, the VMs number on the same CN and network status between storage nodes SNs and CNs. Therefore, the application jobs in the workload have different completion times based on the VM placement decision and large data retrieval. To gain high performance for the overall jobs' completion time and maximizing the throughput of cloud links, we propose VMs placement that considers both computation resources and I/O data. The aim of this algorithm is to reduce the overall jobs' completion time (computing time as well as data transferring time). The CloudSim Simulator results show that our algorithm can significantly maximize the overall application performance and reduce the average jobs' completion time compared among VMs placement approaches previously proposed in the literature.