A Hybrid Technique for Server Consolidation in Cloud Computing Environment

Vijaya, C.; Srinivasan, Padmini

doi:10.2478/cait-2020-0003

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…They implemented the algorithm using cuckoo search optimization (CSO) and proved that the social welfare of users reaches a maximum value called the Pareto optimum. A similar study was conducted by Vijaya et al 39 using the fuzzy heuristic-assisted genetic algorithm (HAGA). Another study using the genetic algorithm (GA) was conducted by Abohamama and Hamouda, 40 with almost similar purposes.…”

Section: Metaheuristic Optimization Methodsmentioning

confidence: 92%

Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information

Mousavi,

Shankar,

Rezvani

et al. 2023

Concurrency and Computation

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SummaryOne of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption.

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Section: Metaheuristic Optimization Methodsmentioning

confidence: 92%

Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information

Mousavi,

Shankar,

Rezvani

et al. 2023

Concurrency and Computation

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“…The results show that the virtual machines provide a greater response concerning the cloud resource utilization in the cloud datacenter when compared to the other swarm intelligence algorithms. Server consolidation is widely used to cut down the energy consumption [8]. B r a i k i and Y o u s s e f [9] have developed a Multi-objective Virtual Machine placement algorithm by using PSO where authors address the problem of maximizing the VM-PM mapping ratio by minimizing the energy utilization, explain how to pack the computing resources efficiently in the cloud datacenter by using a lower number of PMs thereby reducing energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Madhumala

Tiwari²,

2021

Cybernetics and Information Technologies

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Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

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“…As virtualization allows us to create virtual instances, whether virtual machines (VM) or a container, the problem of virtual instances placement has become an important research subject in cloud computing. placement involves finding an optimal method for placing virtual instances on physical servers in order to efficiently use cloud resources [14,15]. To maintain the servers in the data center, a lot of energy is consumed and the cost of cooling the installations is very high, which can translate to a very high cost [16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Smimite¹,

Afdel²

2020

IJACSA

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Currently, data centers energy consumption in the cloud is attracting a lot of interest. One of the most approaches to optimize energy and cost in data centers is virtualization. Recently, a new type of container-based virtualization has appeared, containers are considered very light and modular virtual machines, they offer great flexibility and the possibility of migration from one environment to another, which allows optimizing applications for the cloud. Another approach to saving energy is to consolidate the workload, which is the amount of processing that the computer has to perform at any given time. In this article, we will study the container placement algorithm that takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, we proposed a Hybrid approach for managing resources and workload based on ant colony optimization (ACO) and the first-fit decreasing (FFD) algorithm to avoid unnecessary power consumption. The results of the experiment indicate that using the first-fit decreasing algorithm (FFD) for container placement is better than ant colony optimization especially in a homogeneous systems. On the other hand the ant colony optimization shows very satisfying results in the case of workload management.

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A Hybrid Technique for Server Consolidation in Cloud Computing Environment

Cited by 6 publications

References 28 publications

Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information

Entropy‐aware energy‐efficient virtual machine placement in cloud environments using type information

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

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