A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Gharehpasha, Sasan; Màsdàrí, Mohàmmàd

doi:10.1007/s12652-020-02645-0

Cited by 23 publications

(14 citation statements)

References 60 publications

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“…In [12], Guo et al designed a shadow routing based VM placement algorithm by enabling the VMs to share the CPU/memory resources on the same PM in large data centers within a network cloud. To improve the energy efficiency of cloud data centers, Gharehpasha and Masdari in [13] combined the sine cosine algorithm and ant lion optimizer as discrete multi-objective and chaotic functions to obtain an optimal VM assignment by minimizing the power consumption for the number of active PMs. In [14], Wei et al presented a joint bin-packing heuristic and genetic algorithm to balance the use of multiple physical resources in cloud data centers, aiming to reduce the fragmentation of resources and to maximize the service rate of VM placement.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

Zhang¹,

Zhuge²,

Wang³

et al. 2021

Preprint

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By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for the smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very complex to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the many-to-many matching problem. Simulation results are presented to demonstrate the effectiveness of our proposed matching algorithm, and the normalization factor is of great significance to obtain lower total cost.

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Section: Related Work and Motivationmentioning

confidence: 99%

“…end for 12: end while13: Terminate with matching  .We then analyze the computational complexity of the proposed two-sided matching algorithm. The computational complexity of Algorithm 1 mainly resides in the iteration for determination of the most appropriate VM instance from preference list as well as the total number of the PMs for all theiterations.…”

mentioning

confidence: 99%

Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

Zhang¹,

Zhuge²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Currently, numerous research efforts have dealt with the problem of VM placement for data centers, and most of them focused on the scenario of cloud computing (Liu et al 2018;Dai et al 2016;Rampersaud and Grosu 2017;Guo et al 2018;Gharehpasha and Masdari 2020;Wei et al 2020). In Liu et al (2018), an ant colony system based approach was proposed to achieve the VM placement by effectively minimizing the number of active physical servers to reduce the energy consumption for cloud computing.…”

Section: Related Workmentioning

confidence: 99%

“…In Guo et al (2018), a shadow routing based VM placement algorithm was designed by enabling the VMs to share the CPU/memory resources on the same PM in large data centers within the network cloud scenario. To improve the energy efficiency of cloud data centers, Gharehpasha and Masdari (2020) combined the sine cosine algorithm and ant lion optimizer as the discrete multi-objective and chaotic functions to obtain an optimal VM assignment by minimizing the power consumption for the number of active PMs. In Wei et al (2020), a joint binpacking heuristic and genetic algorithm was presented to balance the use of multiple physical resources in cloud data centers, aiming to reduce the fragmentation of resources and to maximize the service rate of VM placement.…”

Section: Related Workmentioning

confidence: 99%

“…In Wei et al (2020), a joint binpacking heuristic and genetic algorithm was presented to balance the use of multiple physical resources in cloud data centers, aiming to reduce the fragmentation of resources and to maximize the service rate of VM placement. All the above works in (Liu et al 2018;Dai et al 2016;Rampersaud and Grosu 2017;Guo et al 2018;Gharehpasha and Masdari 2020;Wei et al 2020), even though they provide valuable insights on the potential of efficient VM placement on top of the physical servers towards the overall performance improvement for cloud data centers, cannot be directly applied to the virtualized MEC scenario by integrating the NFV and the MEC framework.…”

Section: Related Workmentioning

confidence: 99%

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Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach

Zhang

Zhuge

Wang

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce the maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very difficult to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is then converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the matching problem. Simulation results are presented to show the effectiveness of our proposed algorithm, and the normalization factor is of great significance to obtain the lower total cost.

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Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

Henry

Anbuananth

Kalarani

2022

Concurrency and Computation

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Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta-heuristic concept. A new meta-heuristic algorithm named DJ-HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi-objective function concerning energy consumption and makespan using the same hybrid DJ-HA.From the result analysis, the energy consumption of the DJ-HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ-HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ-HA is enriched than the other conventional algorithms.

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A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Cited by 23 publications

References 60 publications

Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

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