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  • Open Access


    A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

    Xialin Liu1,2,3,*, Junsheng Wu4, Lijun Chen2,3, Jiyuan Hu5

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

    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 >

  • Open Access


    Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

    Mohammad Aldossary1,*, Hatem A. Alharbi2, Nasir Ayub3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

    Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

  • Open Access


    Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini3, Sahil Verma3, Kavita3, Ruba Abu Khurma4,5, Pedro A. Castillo6,*

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

    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 >

  • Open Access


    A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini2, Sahil Verma3, Abdulatif Alabdulatif4, Ruba Abu Khurma5,7, Maribel Garcia-Arenas6, Pedro A. Castillo6,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 571-608, 2024, DOI:10.32604/csse.2024.042690

    Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The… More >

  • Open Access



    Peter Kastena, Severin Zimmermanna, Manish K. Tiwaria, Bruno Michelb, Dimos Poulikakosa,*

    Frontiers in Heat and Mass Transfer, Vol.1, No.2, pp. 1-10, 2010, DOI:10.5098/hmt.v1.2.3006

    Abstract Electronic data center cooling using hot water is proposed for high system exergetic utility. The proof-of-principle is provided by numerically modeling a manifold micro-channel heat sink for cooling microprocessors of a data center. An easily achievable 0.5l/min per chip water flow, with 60°C inlet water temperature, is found sufficient to address the typical data center thermal loads. A maximum temperature difference of ~8°C was found between the solid and liquid, confirming small exergetic destruction due to heat transport across a temperature differential. The high water outlet temperature from the heat sink opens the possibility of More >

  • Open Access


    Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

    Shiyan Zhang1,*, Ruohan Xu2, Zhangbo Xu3, Cenhua Yu1, Yuyang Jiang1, Yuting Zhao4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2287-2308, 2024, DOI:10.32604/cmc.2023.046550

    Abstract The 6th generation mobile networks (6G) network is a kind of multi-network interconnection and multi-scenario coexistence network, where multiple network domains break the original fixed boundaries to form connections and convergence. In this paper, with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness, this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration. Due to the conflict between the utility of different flows, the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward… More >

  • Open Access


    Performance Prediction Based Workload Scheduling in Co-Located Cluster

    Dongyang Ou, Yongjian Ren, Congfeng Jiang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2043-2067, 2024, DOI:10.32604/cmes.2023.029987

    Abstract Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based… More >

  • Open Access



    Randeep Singh* , Masataka Mochizuki, Koichi Mashiko, Thang Nguyen*

    Frontiers in Heat and Mass Transfer, Vol.13, pp. 1-6, 2019, DOI:10.5098/hmt.13.24

    Abstract In the present paper, data center energy conservation systems based on the heat pipe heat exchanger (HPHE) pre-cooler to downsize the chiller capacity and working time has analysed, designed and discussed. The proposed system utilizes thermal diode character of heat pipe to transfer waste heat from source (pre-cooler coolant) to ambient and have been analyzed as per metrological conditions in New York. HPHE Pre cooler with 118 heat pipes and designed for 30 °C ambient temperature has been designed to effectively dissipate 30 kW or more datacenter heat throughout the year. The payback period of More >

  • Open Access


    Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

    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… More >

  • Open Access


    Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

    Jiangyuan Yao1, Weiping Yang1, Shuhua Weng1, Minrui Wang1, Zheng Jiang2, Deshun Li1,*, Yahui Li3, Xingcan Cao4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 741-755, 2023, DOI:10.32604/csse.2023.031531

    Abstract Software-defined networking (SDN) is widely used in multiple types of data center networks, and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers. However, the network topology of each control domain of SDN will affect the performance of the multi-domain network, so performance evaluation is required before the deployment of the multi-domain SDN. Besides, there is a high cost to build real multi-domain SDN networks with different topologies, so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network. As… More >

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