A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center

IEEE Systems Journal, Journal Year: 2021, Volume and Issue: 16(2), P. 3163 - 3174

Published: July 20, 2021

To facilitate cost-effective and elastic computing benefits to the cloud users, energy-efficient secure allocation of virtual machines (VMs) plays a significant role at data centre. The inefficient VM Placement (VMP) sharing common physical among multiple users leads resource wastage, excessive power consumption, increased inter-communication cost security breaches. address aforementioned challenges, novel multi-objective machine placement (SM-VMP) framework is proposed with an efficient migration. ensures distribution resources VMs that emphasizes timely execution user application by reducing delay. VMP carried out applying Whale Optimization Genetic Algorithm (WOGA), inspired whale evolutionary optimization non-dominated sorting based genetic algorithms. performance evaluation for static dynamic comparison recent state-of-the-arts observed notable reduction in shared servers, cost, consumption time up 28.81%, 25.7%, 35.9% 82.21%, respectively utilization 30.21%.

Language: Английский

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OP-MLB: An Online VM Prediction-Based Multi-Objective Load Balancing Framework for Resource Management at Cloud Data Center

IEEE Transactions on Cloud Computing, Journal Year: 2021, Volume and Issue: 10(4), P. 2804 - 2816

Published: Feb. 12, 2021

The elasticity of cloud resources allows clients to expand and shrink their demand for dynamically over time. However, fluctuations in the resource demands pre-defined size virtual machines (VMs) lead lack utilization, load imbalance, excessive power consumption. To address these issues improve performance data center, an efficient management framework is proposed, which anticipates utilization servers balances accordingly. It facilitates saving, by minimizing number active servers, VM migrations, maximizing utilization. An online prediction system, developed deployed at each minimize risk Service Level Agreement (SLA) violations degradation due under/overloaded servers. In addition, multi-objective placement migration algorithms are proposed reduce network traffic consumption within center. evaluated executing experiments on three real world workload datasets namely, Google Cluster dataset, Planet Lab, Bitbrains traces. comparison with state-of-the-art approaches reveals its superiority terms different metrics. improvement saving achieved OP-MLB upto 85.3 percent Best-Fit approach.

Language: Английский

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A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

IEEE Transactions on Parallel and Distributed Systems, Journal Year: 2021, Volume and Issue: 32(12), P. 2893 - 2905

Published: May 11, 2021

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding information into qubits and propagating this through network to estimate or resource demands with enhanced accuracy proactively. The rotation reverse effects Controlled-NOT (C-NOT) gate serve activation function at hidden output layers adjust qubit weights. In addition, Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed optimize EQNN extensively evaluated compared seven state-of-the-art methods using eight real world benchmark datasets three different categories. Experimental results reveal that use approach evolutionary neural substantially improves up 91.6 percent over existing approaches.

Language: Английский

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A Review of Data Centers Energy Consumption and Reliability Modeling

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 152536 - 152563

Published: Jan. 1, 2021

Enhancing the efficiency and reliability of data center are technical challenges for maintaining quality services end-users in operation. The energy consumption models components pivotal ensuring optimal design internal facilities limiting center. modeling is also important since end-user’s satisfaction depends on availability services. In this review, state-of-the-art research gaps identified, which could be beneficial future design, planning, major load sections i.e., information technology (IT), power conditioning system (IPCS), cooling section systematically reviewed classified, reveals advantages disadvantages different applications. Based analysis related findings it concluded that model parameters variables more than accuracy, often necessary studies. Additionally, lack IPCS while losses cause issues should considered with importance designing absence a review identified leads paper to assessment aspects, needed adaptation new technologies equipment indices, models, methodologies first time, where divided into two groups analytical simulation-based approaches. There components’ failure data, as gaps. addition, dependency included shows service impacted by section.

Language: Английский

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A Fault Tolerant Elastic Resource Management Framework Toward High Availability of Cloud Services

IEEE Transactions on Network and Service Management, Journal Year: 2022, Volume and Issue: 19(3), P. 3048 - 3061

Published: April 26, 2022

Cloud computing has become inevitable for every digital service which exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud running multiple replicas of virtual machine (VM) accounts high operational-cost. This paper proposes Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned from different perspective inducing high-availability servers VMs. Specifically, (1) an online failure predictor is developed anticipate failure-prone VMs based on predicted contention; (2) operational status server monitored with help power analyser, estimator thermal analyser identify any due overloading overheating proactively; (3) are assigned proposed fault-tolerance unit composed decision matrix safe box trigger VM migration handle outage beforehand while maintaining desired level users. evaluated compared against state-of-the-arts executing experiments two real-world datasets. FT-ERM improved services up 34.47% scales down VM-migration power-consumption 88.6% 62.4%, respectively over without approach.

Language: Английский

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Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

IEEE Transactions on Parallel and Distributed Systems, Journal Year: 2023, Volume and Issue: 34(4), P. 1313 - 1330

Published: Jan. 30, 2023

The precise estimation of resource usage is a complex and challenging issue due to the high variability dimensionality heterogeneous service types dynamic workloads. Over last few years, prediction traffic has received ample attention from research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power learning capabilities. This paper presents first systematic survey cum performance analysis-based comparative study diversified learning-driven cloud models. discussion initiates with significance predictive management followed schematic description, operational design, motivation, challenges concerning these Classification taxonomy different approaches into five distinct categories are presented focusing on theoretical concepts mathematical functioning existing state-of-the-art methods. most prominent belonging class thoroughly surveyed compared. All classified implemented common platform for investigation comparison using three benchmark traces via experimental analysis. essential key indicators evaluated concluded discussing trade-offs notable remarks.

Language: Английский

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A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management

IEEE Systems Journal, Journal Year: 2023, Volume and Issue: 17(3), P. 3894 - 3905

Published: March 13, 2023

Virtual machine placement (VMP) is the process of selecting most appropriate physical (PM) to place users' requested virtual (VM) in large cloud data centers. Several methods have been framed deal with this problem. However, current solutions only consider limited resource types, resulting an unbalanced load that activates unnecessary PMs inside center. In article, we suggest a flower pollination-based nondominated sorting optimization (FP-NSO) algorithm maximizes usage and minimizes energy consumption carbon emission Multiple resource-constraint metrics are associated our assists finding suitable for deploying VMs environment. The VMP carried out by employing combined concept pollination technique-based genetic (NSGA-II). evaluated using Google cluster dataset. performance like utilization, power consumption, values computed static dynamic scenarios. obtained results compared existing approaches. There significant reduction emission, execution time up 16.69%, 48.60%, 75.87%, respectively, improvement utilization 78.18%.

Language: Английский

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Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey

Computer Communications, Journal Year: 2023, Volume and Issue: 209, P. 120 - 150

Published: June 19, 2023

Language: Английский

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A systematic review of green-aware management techniques for sustainable data center

Sustainable Computing Informatics and Systems, Journal Year: 2024, Volume and Issue: 42, P. 100989 - 100989

Published: April 1, 2024

Language: Английский

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Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism

The Journal of Supercomputing, Journal Year: 2022, Volume and Issue: 79(3), P. 3437 - 3470

Published: Sept. 4, 2022

Language: Английский

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