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In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.
In IaaS Cloud, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, so how to place VMs on PMs to reduce energy consumption is one of the major concerns for cloud providers. Existing VM Scheduling schemes proposes optimize PMs or network resources utilization, but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously. This paper proposes VM scheduling scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to reduce both of the numbers of active PMs and network elements so to finally reduce energy consumption. Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem. Accredially, we design a twostage heuristic algorithm to solve the issue, and th e simulations show that our solution outperforms the existing PM- or network-only optimization solutions.