Adaptive thresholds determination for saving cloud energy using three-way decisions

Jiang, Chunmao; Wu, Junwei; Zhi-cong, LI

doi:10.1007/s10586-018-1879-7

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…Under utilization or over utilization ultimately leads to degrading QoS. To overcome these problems, a technique based on adaptive thresholds is proposed in [ 35 ]. This technique is based on a three-way decision in VMs migration.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Alourani,

Khalid,

Tahir

et al. 2024

PLoS ONE

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Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

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Section: Literature Reviewmentioning

confidence: 99%

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Alourani,

Khalid,

Tahir

et al. 2024

PLoS ONE

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“…For example, according to resource usage, the VMs could be divided into idle state nodes, the normal nodes and overload nodes. Jiang et al [28] proposed an adaptive thresholds determination for saving cloud energy using three-way decisions. The mobile cloud task offloading VOLUME 4, 2016 method could be divided into immediate offloading, delayed offloading, and no offloading.…”

Section: The Three-way Decisionmentioning

confidence: 99%

Three-Way Ensemble Prediction for Workload in the Data Center

Shi

Jiang

2022

IEEE Access

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Accurate prediction of the cloud data center workload used to improve resource utilization and reduce energy consumption, is a vital methodology and technology in cloud computing. However, the workload presents a quasi-volatile, is challenging to obtain accurate results in cloud resource management. In this paper, the three-way ensemble prediction for workload in the data center is first proposed to improve the accuracy of the prediction. Moreover, we first defined the workload as the stable period, the volatility period, and the jitter period and adopted a simulated annealing algorithm to learn the optimal threshold to divide the workload. Then, according to the basic idea of the three-way decision model (i.e., TAO model), we assigned various prediction models based on workload characteristics and a priori error prediction to improve the prediction accuracy further. Finally, all the experimental results carried on the CPU load monitoring logs from Google cluster trace and compared with ARIMA, NN, and DMASVR-3WD, TWD-RCPM improve the accuracy of workload prediction by 69.0%, 68.6% and 72.6%, respectively.INDEX TERMS three-way decision, three-way division, cost assessment, load prediction, cloud energy consumption I. INTRODUCTION

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“…The smart elastic and adaptive worst fit decreasing method reduced the migration overhead. An adaptive threshold for VM migration presented by [75] was used to identify the upper and lower thresholds where the VMs were classified to three different modes based on the status of the VM resource utilization: the VM normal-load, VM overloaded, and VM underloaded. Afterwards the choice of target host was optimized based on the balance of resource usage and transmission overhead.…”

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

confidence: 99%

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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Adaptive thresholds determination for saving cloud energy using three-way decisions

Cited by 19 publications

References 22 publications

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Three-Way Ensemble Prediction for Workload in the Data Center

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

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