The area of sustainable green smart computing highlights key challenges towards reducing cost and carbon dioxide emissions due to the high-energy consumption of Cloud data centres. Here, we focus on the Cloud virtual machine (VM) scheduling that is usually based on simple algorithms, e.g. VM placement on nodes with low memory usage. This approach fails to consider the actual configuration of nodes inside the server rack resulting in local overheating of Cloud data centres. To solve this, we propose a VM scheduling algorithm based on the gravitational effect, called VMSAGE, to optimize energy efficiency of Cloud computing systems. Inspired by the physical gravitation model, we define the thermal repulsion and logical gravitation factors between physical nodes and VMs. To achieve optimized VM scheduling, we propose a gravitation function that refers to the calculation of the logical quality of each VM, host and rack through the algorithm, so as to draw the attractiveness between them. Based on the concept of dimension reduction, VMSAGE conducts the two-dimensional plane target selection twice to reduce the computational cost. Additionally, VMSAGE evaluates attributes of the computer room to carry out the VM deployment. To demonstrate the effectiveness of our solution, we compare it with the Best Fit Heuristic (BFH) and the dynamic voltage and frequency scaling (DVFS) algorithms. The results indicate that our algorithm achieves 10% and 20% optimized energy consumption respectively. The experimental results highlight our contribution, in where VMSAGE can significantly reduce energy consumption rates and VM migration times.

Introduction

Cloud computing enables an economically promising paradigm of computation outsourcing [1]. Immense computation power and storage capacity of computing systems enable everyday Internet users to store and process largescale data on a “pay as you go” model [2]. As more users move their activities to the Cloud, the number of data centre nodes increases as well. The global data center market is estimated to reach revenues of around 174 billion by 2023, growing at a Compound Annual Growth Rate (CAGR) of approximately 4% during the forecast period1 . The demand for data centres processing capacity is expected to increase by 7 to 10 times in the next 5 years [3]. However, as the scale of Cloud data centres increases, the physical servers cause high power consumption and environ ment problems. Today, the annual power consumption of global data centres is about 3,000 tw.h, equivalent to the total power generation of 300 nuclear power plants [4]. For example, the annual power consumption of Google’s Cloud data centres is up to nearly 203,000,000 kw.h [4]. The inefficient utilization of resources causes unnecessary waste of energy. Kurnik et al. [35] show that the current under-utilization rates of many servers in data centres are around 90%. The effective virtualization of resources can be used to solve the low energy efficiency problem.