A hybrid energy–Aware virtual machine placement algorithm for cloud environments. (15th July 2020)

• Record Type: Journal Article
• Authors: Abohamama, A.S.; Hamouda, Eslam
• Is Part Of: Expert systems with applications. Volume 150(2020)
• Publication Date: 2020-07-15
• Subjects: Cloud computing -- Server consolidation -- Virtual machine placement -- Permutation-based optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113306
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An improved genetic algorithm is proposed for permutation-based problems. A hybrid virtual machine placement algorithm is proposed for cloud data centers. The proposed hybrid algorithm significantly reduces the power consumption. The proposed hybrid algorithm achieves high resources utilization rates. Abstract: The high energy consumption of cloud data centers presents a significant challenge from both economic and environmental perspectives. Server consolidation using virtualization technology is widely used to reduce the energy consumption rates of data centers. Efficient Virtual Machine Placement (VMP) plays an important role in server consolidation technology. VMP is an NP-hard problem for which optimal solutions are not possible, even for small-scale data centers. In this paper, a hybrid VMP algorithm is proposed based on another proposed improved permutation-based genetic algorithm and multidimensional resource-aware best fit allocation strategy. The proposed VMP algorithm aims to improve the energy consumption rate of cloud data centers through minimizing the number of active servers that host Virtual Machines (VMs). Additionally, the proposed VMP algorithm attempts to achieve balanced usage of the multidimensional resources (CPU, RAM, and Bandwidth) of active servers, which in turn, reduces resource wastage. The performance of both proposed algorithms are validated through intensive experiments. The obtained results show that the proposed improved permutation-based genetic algorithm outperforms several other permutation-based algorithms on two classical problems (the Traveling Salesman Problem and the Flow Shop Scheduling Problem) using various standard datasets. Additionally, this study shows that the proposed hybrid VMP algorithm has promising energy saving and resource wastage performance compared to other heuristics and metaheuristics. Moreover, this study reveals that the proposed VMP algorithm achieves a balanced usage of the multidimensional resources of active servers while others cannot.