Description

In our process explains virtual machine (VM) scheduling in a queuing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queuing model that buffers the same type of VM jobs in one virtual queue. The queuing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. We further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. It also achieves good performance in terms of less Execution time and Makespan.