Multi-objective VM Placement Algorithms for Green Cloud Data Centers: An Overview

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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Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

2013 IEEE 5th International Conference on Cloud Computing Technology and Science ◽

10.1109/cloudcom.2013.36 ◽

2013 ◽

Cited By ~ 11

Author(s):

Aissan Dalvandi ◽

Mohan Gurusamy ◽

Kee Chaing Chua

Keyword(s):

Data Centers ◽

Vm Placement ◽

Cloud Data ◽

Placement And Routing ◽

Cloud Data Centers ◽

Green Cloud ◽

Bandwidth Guarantees ◽

Time Aware

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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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