Autonomic and energy-aware resource allocation for efficient management of cloud data centre

Shelar, Madhukar; Sane, S. S.; Kharat, Vilas; Jadhav, Rushikesh

doi:10.1109/ipact.2017.8244944

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…To achieve this, virtualization software like VMware or Hyper-V can be used to create and manage virtual computers on a physical host [21]. VM consolidation has several benefits which include [22]:  Lower hardware costs by reducing the number of physical servers.…”

Section: Virtual Machine Consolidationmentioning

confidence: 99%

“…It starts searching from the last allocated PM, which can be useful for sequential allocation patterns or when dealing with real-time workloads.  Random Fit (RF) [21]: It randomly selects a PM for the VM allocation. While it is simple to implement, it may not be suitable for situations that require careful resource balancing or workload optimization.…”

Section: ____________________________________________________________...mentioning

confidence: 99%

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A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

Singh,

Walia

2023

IEEE Access

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In the last decade, users can access their applications, data, and services via the cloud from any location with an internet connection. The scale of heterogeneous cloud environments is continuously growing due to the development of computing-intensive smart devices. A data center is the central processing unit of any cloud environment, and it is made up of hardware-oriented machines known as Physical Machines (PMs) or server and software-oriented machines known as Virtual Machines (VMs). The deployment of a huge number of physical servers as a result of the exponential development in demand for cloud services has resulted in high energy consumption and ineffective resource usage. Efficient resource utilization and minimizing power consumption by any data center have become crucial challenges. Virtual machine consolidation(VMC) is a method of optimizing computing resources by consolidating multiple VMs onto a reduced number of PMs. By consolidating VMs and running fewer physical servers, VM consolidation can lead to reducing power consumption and efficient resource utilization. This review paper presents a comprehensive analysis of cloud computing virtual machine consolidation, exploring various strategies, benefits, challenges, and future trends in this domain. By examining a wide range of literature from the year 2015 to 2023, this review attempts to provide insight into the current state of VM consolidation and its possible effects on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics while the emphasis should have been on improving energy efficiency and quality of service for cloud systems. Future research can be aimed at developing a multiobjective system that emphasizes minimizing cloud energy usage without sacrificing service quality, preventing service level agreements with cloud users from being compromised.

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Section: Virtual Machine Consolidationmentioning

confidence: 99%

Section: ____________________________________________________________...mentioning

confidence: 99%

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

Singh,

Walia

2023

IEEE Access

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“…The authors [9] proposed an algorithm for optimizing VM placement across different levels of data center isolation to enhance performance and service availability. Their approach optimizes VM placement based on traffic patterns and assigns VMs with high mutual bandwidth to the same physical or nearby hosts.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Neural Network Metaheuristic Approach for Optimizing Virtual Machine Deployment in Cloud Data Centers

Salehi,

Fartash,

Torkestani

2024

Preprint

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In recent years, the optimization of virtual machine placement (VMP) in cloud data centers has emerged as a crucial and complex challenge, garnering considerable attention from researchers. Due to its NP-Hard nature, metaheuristic algorithms have become a popular approach in addressing this problem. These algorithms typically initiate with the random generation of initial solutions, which are then iteratively refined using specific operators to find the optimal solution. Among these algorithms, the Neural Network Algorithm (NNA) stands out as a promising swarm optimization technique known for its strong global search capability, making it well-suited for tackling various intricate optimization tasks. However, NNA often suffers from slow convergence due to its limited exploitation capability, thereby hindering its practical utility in optimization problem-solving. To address this limitation, this study proposes an approach to enhance the convergence speed of NNA while achieving a better balance between exploitation and exploration. Specifically, we adopt a heuristic algorithm to generate initial solutions in a more suitable and less random manner for the VMP problem. Additionally, the search operators of the algorithm are optimized using Cauchy and Lévy distributions. We evaluate the performance of the Improved Biogeography-based Neural Network Algorithm (IBNNA) on synthetic datasets of varying dimensions and compare the results with existing similar algorithms in the literature. The experimental findings demonstrate that the proposed algorithm outperforms other methods in terms of physical machine utilization, convergence rate, and power consumption across most experimental scenarios.

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