Jinlin Xu^1,2, Wansu Pan^1,*, Haibo Tan^1,2, Longle Cheng¹, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2709-2726, 2024, DOI:10.32604/cmc.2024.056925 - 18 November 2024

Abstract The traffic within data centers exhibits bursts and unpredictable patterns. This rapid growth in network traffic has two consequences: it surpasses the inherent capacity of the network’s link bandwidth and creates an imbalanced network load. Consequently, persistent overload situations eventually result in network congestion. The Software Defined Network (SDN) technology is employed in data centers as a network architecture to enhance performance. This paper introduces an adaptive congestion control strategy, named DA-DCTCP, for SDN-based Data Centers. It incorporates Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) to establish congestion awareness and an ECN marking model.… More >

Xialin Liu^1,2,3,*, Junsheng Wu⁴, Lijun Chen^2,3, Jiyuan Hu⁵

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1601-1631, 2024, DOI:10.32604/cmc.2024.050626 - 18 July 2024

Abstract Virtual machine (VM) consolidation aims to run VMs on the least number of physical machines (PMs). The optimal consolidation significantly reduces energy consumption (EC), quality of service (QoS) in applications, and resource utilization. This paper proposes a prediction-based multi-objective VM consolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value. We use a hybrid model based on Auto-Regressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) (HPAS) as a prediction model and consolidate VMs to PMs based on prediction results by HPAS, aiming at minimizing the More >

Nidhika Chauhan¹, Navneet Kaur², Kamaljit Singh Saini³, Sahil Verma³, Kavita³, Ruba Abu Khurma^4,5, Pedro A. Castillo^6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3757-3782, 2024, DOI:10.32604/cmc.2024.046516 - 20 June 2024

Abstract Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of… More >

Pingping Li^*, Jiuxin Cao

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 81-105, 2023, DOI:10.32604/cmc.2023.039076 - 08 June 2023

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… 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 - 29 April 2023

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.… 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 - 17 August 2022

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,

… 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 - 29 March 2022

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… 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 - 30 December 2021

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 More >

R. Jayamala^*, A. Valarmathi

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 347-358, 2021, DOI:10.32604/iasc.2021.012401 - 18 January 2021

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 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… More >