Pingping Li^*, Jiuxin Cao

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 81-105, 2023, DOI:10.32604/cmc.2023.039076

Abstract Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear… More >

Yogesh Jaluria^a,*, Arvindh Sunder^a, Jingru Z. Benner^b

Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-12, 2020, DOI:10.5098/hmt.15.12

Abstract Data centers are of crucial importance today in the storage and retrieval of large amounts of data. Most organizations and firms, ranging from banks and online retailers to government departments and internet companies, use data centers to store information that can be recovered efficiently and rapidly. As the deployment of data centers has increased, along with their capacity for data storage, the demands on thermal management have also increased. It is necessary to remove the energy dissipated by the electronic circuitry since the temperature of the components must not rise beyond acceptable levels that could damage them or affect their… More >

Muhammad Imran Khan Khalil¹, Syed Adeel Ali Shah¹, Izaz Ahmad Khan², Mohammad Hijji³, Muhammad Shiraz⁴, Qaisar Shaheen^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6305-6322, 2023, DOI:10.32604/cmc.2023.038163

Abstract Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers string-matching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this… More >

N. Venkata Subramanian¹, V. S. Shankar Sriram^2,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3279-3294, 2023, DOI:10.32604/iasc.2023.023700

Abstract

Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers’ (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3175-3193, 2022, DOI:10.32604/cmc.2022.026041

Abstract Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve net-zero carbon by 2060, NEOM city has been proposed to be the “Saudi hub” for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global… More >

Denghui Zhang^1,*, Guocai Yin²

Journal on Internet of Things, Vol.3, No.4, pp. 149-157, 2021, DOI:10.32604/jiot.2021.016936

Abstract Cloud data centers face the largest energy consumption. In order to save energy consumption in cloud data centers, cloud service providers adopt a virtual machine migration strategy. In this paper, we propose an efficient virtual machine placement strategy (VMP-SI) based on virtual machine selection and integration. Our proposed VMP-SI strategy divides the migration process into three phases: physical host state detection, virtual machine selection and virtual machine placement. The local regression robust (LRR) algorithm and minimum migration time (MMT) policy are individual used in the first and section phase, respectively. Then we design a virtual machine migration strategy that integrates… More >

R. Jayamala^*, A. Valarmathi

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 347-358, 2021, DOI:10.32604/iasc.2021.012401

Abstract Cloud computing is an increasingly important technology to deliver pay-as-you-go online computing services. In this study, the cloud service provider permits the cloud user to pay according to the user’s needs. Various methods have been used to reduce energy utilization in the cloud. The rapid increase of cloud users has led to increased energy consumption and higher operating costs for cloud providers. A key issue in cloud data centers is their massive energy consumption to operate and maintain computing services. Virtual machine (VM) migration is a method to reduce energy consumption. This study proposes enhanced decentralized virtual machine migration (EDVMM),… More >

Rizwan Munir^1,*, Yifei Wei¹, Rahim Ullah², Iftikhar Hussain³, Kaleem Arshid⁴, Umair Tariq¹

Journal of Quantum Computing, Vol.2, No.4, pp. 193-202, 2020, DOI:10.32604/jqc.2020.016151

Abstract A smart grid is the evolved form of the power grid with the integration of sensing, communication, computing, monitoring, and control technologies. These technologies make the power grid reliable, efficient, and economical. However, the smartness boosts the volume of data in the smart grid. To obligate full benefits, big data has attractive techniques to process and analyze smart grid data. This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid. The offered framework comprises of the following four layers: (i) Data source layer, (ii) Data transmission layer, (iii) Data… More >

Yang Yang^1,*, Jing Dong¹, Chao Fang², Ping Xie³, Na An³

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1015-1031, 2020, DOI:10.32604/cmes.2020.09404

Abstract The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services. Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service. Failure prediction is an important means of ensuring service availability. Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics, and the distribution imbalance between the failure sample and the normal sample is widespread, resulting in inaccurate failure prediction. Targeting these challenges, this paper proposes a novel failure prediction method FP-STE (Failure Prediction… More >