Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues

• Published in: Journal of grid computing Vol. 18; no. 4; pp. 727 - 759
• Main Authors: Masdari, Mohammad, Zangakani, Mehran
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Cloud computing; Computer Science; Computer simulation; Energy conservation; Energy management; Forecasting; Management of Computing and Information Systems; Placement; Power management; Predictions; Processor Architectures; User Interfaces and Human Computer Interaction; Virtual environments; Workload; Data center; Queuing theory; Markov model; ARIMA; VM placement
• ISSN: 1570-7873; 1572-9184
• DOI: 10.1007/s10723-019-09489-9

Efficient VM management is very crucial for energy saving, increasing profit, and preventing SLA violations. VM placement schemes can be classified into reactive and proactive/predictive schemes which try to improve the VM placement results, by forecasting future workloads or resource demands using various prediction techniques. This paper puts forward an extensive survey of the proactive VM placement approaches and categorizes them according to their applied forecasting methods. It describes how each scheme has applied the prediction algorithms to conduct more effective and low overhead VM placement. Moreover, in each category, factors such as evaluation parameters, simulation software, workload data, power management method, and prediction factors are compared to illuminate more details about the investigated VM placement approaches. At last, the concluding issues and open future studies trends and area are highlighted.