Multi-objective virtual machine placement optimization for cloud computing

Dorterler, Serap; Dörterler, Murat; Özdemir, Suat

doi:10.1109/isncc.2017.8072013

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…The objectives are to increase the number of hosted VMs, decrease resource waste, and lessen the demand for PMs. In a paper that was submitted, [18] solved the VMP issue by optimising CPU utilisation while minimising energy use.…”

Section: Multi-objective Functionsmentioning

confidence: 99%

MOM-VMP: Multi-Objective Mayfly Optimization algorithm for VM Placement supported by Principal Component Analysis(PCA) in Cloud Data Center

Durairaj

Srid

2022

Preprint

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Virtual Machine Placement (VMP) is crucial in a cloud data cen-ter(CDC). It is a critical step carried out as part of the Virtual Machine (VM) placement to allocate the best Physical Machine (PM) to host the VMs. The efficacy of the virtual machine placement strategy has a considerable impact on cloud computing efficiency. The ineffec-tiveness of the VMP approach has a major negative impact on the CDC.Virtualization facilitated VM migration has met the ever-increasing demands of dynamic workload by transferring VMs inside CDC. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided VM placement. As a result, VMP needs to assess characteristics that may impact placement performance and energy efficiency. Most past research has concentrated solely on reducing energy consumption while ignoring SLA (service level agreement) breaches, enhancing the resource usage of PMs, and ignoring the over-commitment issue. MOM-VMP To propose a multiobjective Mayfly VMP algorithm (MOM-VMP) meta-heuristic optimization algorithm with a massive CDC with different and multi-dimensional resources to handle these issues. A multi-objective dynamic VMP strategy is employed to reduce resource wastage, overcom-mitment ratio, migration time, SLA violation and energy consumption at the same time. This paper presents a dynamic multi-objective VMP in CDC based on overcommitment resource allocation to influence VM-PM mapping. We validated our method of conducting a performance evaluation study using the CloudSim tool. The experimental findings show that the suggested study decreases energy consumption, makespan, SLA violations, and PM overloading while enhancing resource utilization.

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Section: Multi-objective Functionsmentioning

confidence: 99%

MOM-VMP: Multi-Objective Mayfly Optimization algorithm for VM Placement supported by Principal Component Analysis(PCA) in Cloud Data Center

Durairaj

Srid

2022

Preprint

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“…The PSO utilizes adaptive movement that transfers a particle position in every iteration. The mathematical formulation used in this research is as follows: 𝑥𝑗(𝑡) = 𝑥𝑗 (𝑡1 − 1) + 𝑣𝑗 (𝑡1) (1) where xj(t1) indicates the particle's current position j at every iteration t1, xj(t1 − 1) represents the particle's current position i at every t1 − i iteration, and vj(t1) represents the particle velocity i at the t1 iteration.…”

Section: Proposed Multi-optimization Pso Algorithmmentioning

confidence: 99%

“…CC can be divided into three sub-categories based on the architecture structure: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). IaaS is considered to be fundamental for the service models developed using this concept [1]. Recently, most of the studies have focused on the development of multi-objective meta-heuristic algorithms such as ACO (ant colony optimization), GA, SA (simulated annealing), and PSO to resolve the workflow scheduling issues and obtain efficient responses.…”

Section: Introductionmentioning

confidence: 99%

Applications of Virtual Machine Using Multi-Objective Optimization Scheduling Algorithm for Improving CPU Utilization and Energy Efficiency in Cloud Computing

Choudhary

Perinpanayagam

2022

Energies

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Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.

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“…Later, a comparison module was added to the platform, which can compare the failure repair rate CDCs, compensation of cloud service providers and the pro t of cloud service providers between different mapping algorithms. At the same time, the method generates corresponding CT parameters for simulation [29][30][31]. Other VM parameters refer to the real VM parameters provided by Amazon.…”

Section: Hrfdc Strategymentioning

confidence: 99%

A HRFDC strategy based on dynamic classification of failed cloud tasks

Liang

Bai

2022

Preprint

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With the continuous development and improvement of cloud computing technology, the major computer giants have deployed their own cloud data center (CDC). At the same time, as user demands continue to expand, competition among cloud service providers is also intensifying. In order to continuously improve its own service quality and user satisfaction, cloud service providers adopting efficient and low-cost fault-tolerant strategy will improve the performance and profit of CDCs. However, the existing rescheduling strategy are mostly at the expense of the completion time of cloud task (CT) or increasing the compensation of cloud service providers, which ultimately leads to a decline in the profit of cloud service providers. More serious will affect the reputation and user experience of the enterprise. This paper systematically analyzes the performance loss caused by virtual machine (VM) failure and the rescheduling process of CDCs fault-tolerant strategy. At the same time, we established a dynamic classification rule of failed cloud task (FCT) according to the deadline for CTs. After that, a high-profit rescheduling fault-tolerant strategy for CDCs based on dynamic classification of FCTs (HRFDC) was proposed. This scheduling strategy maximizes the profitability of cloud service providers by increasing the failure repair rate of CDCs and reducing the compensation of cloud service providers. Finally, this strategy has been tested and verified, and its effect is due to the comparison algorithm.

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Multi-objective virtual machine placement optimization for cloud computing

Cited by 11 publications

References 21 publications

MOM-VMP: Multi-Objective Mayfly Optimization algorithm for VM Placement supported by Principal Component Analysis(PCA) in Cloud Data Center

MOM-VMP: Multi-Objective Mayfly Optimization algorithm for VM Placement supported by Principal Component Analysis(PCA) in Cloud Data Center

Applications of Virtual Machine Using Multi-Objective Optimization Scheduling Algorithm for Improving CPU Utilization and Energy Efficiency in Cloud Computing

A HRFDC strategy based on dynamic classification of failed cloud tasks

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