Multi objective consolidation of virtual machines for green computing in 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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An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Electronics ◽

10.3390/electronics8020218 ◽

2019 ◽

Vol 8 (2) ◽

pp. 218 ◽

Cited By ~ 14

Author(s):

Aisha Fatima ◽

Nadeem Javaid ◽

Ayesha Anjum Butt ◽

Tanzeela Sultana ◽

Waqar Hussain ◽

...

Keyword(s):

Data Centers ◽

Virtual Machines ◽

Service Providers ◽

Utilization Rate ◽

Gray Wolf ◽

Vm Placement ◽

Cloud Data ◽

Multi Objective ◽

Cloud Data Centers ◽

Better Than

Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

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

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2017.63431 ◽

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

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Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽

10.3390/electronics7120389 ◽

2018 ◽

Vol 7 (12) ◽

pp. 389 ◽

Cited By ~ 6

Author(s):

Aisha Fatima ◽

Nadeem Javaid ◽

Tanzeela Sultana ◽

Waqar Hussain ◽

Muhammad Bilal ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Particle Swarm ◽

Vm Placement ◽

Swarm Optimization ◽

Cloud Data ◽

Cloud Data Centers ◽

Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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Multidimensional Resource Scheduling in Cloud Data Centers

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1513 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 1513-1516

Author(s):

Hai Na Song ◽

Xiao Qing Zhang ◽

Zhong Tang He

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Three Dimensional ◽

Resource Scheduling ◽

Computing Environment ◽

Cloud Computing Environment ◽

Cloud Data ◽

Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

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