The area of sustainable green smart computing highlights key challenges to wards 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.
- Virtual MachineScheduling AlgorithmEnergy EfficiencyGravitation EffectCloud Computing
- Cloud computing
- Gravitation effect
- Virtual machine
- Energy efficiency
- Scheduling algorithm
Xu, X., Zhang, Q., Maneas, S., Sotiriadis, S., Gavan, C., & Bessis, N. (2019). VMSAGE: A Virtual Machine Scheduling Algorithm based on the Gravitational Eﬀect for Green Cloud Computing. Simulation Modelling Practice and Theory, 93, 87-103. https://doi.org/10.1016/j.simpat.2018.10.006