Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning

2014 ◽  
Author(s):  
Fahimeh Farahnakian ◽  
Pasi Liljeberg ◽  
Juha Plosila
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Enhanced Virtual Machine Placement in Cloud Data Centers: Combinations of Fuzzy Logic with Reinforcement Learning and Biogeography-Based Optimization (BBO) Algorithms

2022 ◽  
Author(s):  
Arezoo Ghasemi ◽  
Abolfazl Toroghi Haghighat ◽  
Amin Keshavarzi
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Execution Time ◽  
Load Balance ◽  
Data Centers ◽  
Virtual Machines ◽  
Vertical Load ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers

Abstract The process of mapping Virtual Machines (VMs) to Physical Ma- chines (PMs), which is defined as VM placement, affects Cloud Data Centers (DCs) performance. To enhance the performance, optimal placement of VMs regarding conflicting objectives has been proposed in some research, such as Multi-Objective VM reBalance (MOVMrB) and Reinforcement Learning VM reBalance (RLVMrB) in recent years. The MOVMrB algorithm is based on the BBO meta-heuristic algorithm and the RLVMrB algorithm inspired by reinforcement learning, which in both of them the non-dominance method is used to evaluate generated solutions. Although this approach reaches accept- able results, it fails to consider other solutions which are optimal regarding all objectives, when it meets the best solution based on one of these objectives. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy- RLVMrB and Fuzzy-MOVMrB, that are able to consider all objectives when evaluating candidate solutions in solution space. All four algorithms aim to balance the load between VMs in terms of processor, bandwidth, and memory as well as horizontal and vertical load balance. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance and execution time. The simulation results show that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform RLVMrB and MOVMrB algorithms in terms of vertical load balancing and horizontal load balancing. Also, the RLVMrB and Fuzzy-RLVMrB algorithms are better in execution time than the MOVMrB and Fuzzy-MOVMrB algorithms.

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