Energy Efficient Server with Dynamic Load Balancing Mechanism for Cloud Computing Environment

被引:5
|
作者
Rajagopal, T. K. P. [1 ]
Venkatesan, M. [2 ]
机构
[1] Hindusthan Coll Engn & Technol, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India
[2] KSR Inst Engn & Technol, Dept Comp Sci & Engn, Tiruchengode, Tamil Nadu, India
关键词
Dynamic load balancing; Computer networks; Distributed system; Cloud computing environment;
D O I
10.1007/s11277-021-09043-5
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Currently the rapid development of technology is taking place in the field of cloud computing. Cloud computing environment is the very demanding. It is easily accessible to provide the computer network service and resources using the Internet. It offers a variety of services such as Software as a Service (SAAS), Platform as a Service (PAAS) and Infrastructure as a Service (IAAS). The development of cloud computing environment primarily refers to the cloud service users and the cloud service provider. Cloud service user can utilize all kind of services provided by the provider and Cloud service provider provide cloud resources to the user. The service provider should use its resources professionally to make earnings, but in cloud environment, the difficult task for them is to improve the cloud resources. This article express the network energy reduction approach through optimizing the load using the network node and reducing the active network node by rearranging techniques.
引用
收藏
页码:3127 / 3136
页数:10
相关论文
共 50 条
  • [31] Agent Based Dynamic Load Balancing in Cloud Computing
    Grover, Jitender
    Katiyar, Shivangi
    2013 INTERNATIONAL CONFERENCE ON HUMAN COMPUTER INTERACTIONS (ICHCI), 2013,
  • [32] Energy-Efficient Web Server Load Balancing
    Lenhardt, Joerg
    Chen, Kai
    Schiffmann, Wolfram
    IEEE SYSTEMS JOURNAL, 2017, 11 (02): : 878 - 888
  • [33] Energy efficient load balancing in web server clusters
    Gebrehiwot, Misikir Eyob
    Aalto, Samuli
    Lassila, Pasi
    2017 29TH INTERNATIONAL TELETRAFFIC CONGRESS (ITC 29), VOL 3, 2017, : 13 - 18
  • [34] Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing
    Zhou, Jincheng
    Lilhore, Umesh Kumar
    Poongodi, M.
    Hai, Tao
    Simaiya, Sarita
    Jawawi, Dayang Norhayati Abang
    Alsekait, Deemamohammed
    Ahuja, Sachin
    Biamba, Cresantus
    Hamdi, Mounir
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2023, 12 (01):
  • [35] Time Efficient Dynamic Threshold-based load balancing technique for cloud computing
    Mishra, Sambit Kumar
    Khan, Md Akram
    Sahoo, Bibhudatta
    Puthal, Deepak
    Obaidat, Mohammad S.
    Hsiao, K. F.
    2017 INTERNATIONAL CONFERENCE ON COMPUTER, INFORMATION AND TELECOMMUNICATION SYSTEMS (IEEE CITS), 2017, : 161 - 165
  • [36] Energy and locality aware load balancing in cloud computing
    Wang, Xiaoli
    Wang, Yuping
    Cui, Yue
    INTEGRATED COMPUTER-AIDED ENGINEERING, 2013, 20 (04) : 361 - 374
  • [37] Greening the Cloud: A Load Balancing Mechanism to Optimize Cloud Computing Networks
    Kumar, Chetan
    Marston, Sean
    Sen, Ravi
    Narisetty, Amar
    JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 2022, 39 (02) : 513 - 541
  • [38] Load Balancing in Cloud Computing Using Dynamic Load Management Algorithm
    Panwar, Reena
    Mallick, Bhawna
    2015 INTERNATIONAL CONFERENCE ON GREEN COMPUTING AND INTERNET OF THINGS (ICGCIOT), 2015, : 773 - 778
  • [39] An Estimation-Based Dynamic Load Balancing Algorithm for Efficient Load Distribution and Balancing in Heterogeneous Grid Computing Environment
    Eng, KaiLun
    Muhammed, Abdullah
    Abdullah, Azizol
    Hussin, Masnida
    Hasan, Sazlinah
    Mohamed, Mohamad Afendee
    JOURNAL OF GRID COMPUTING, 2023, 21 (01)
  • [40] An Estimation-Based Dynamic Load Balancing Algorithm for Efficient Load Distribution and Balancing in Heterogeneous Grid Computing Environment
    KaiLun Eng
    Abdullah Muhammed
    Azizol Abdullah
    Masnida Hussin
    Sazlinah Hasan
    Mohamad Afendee Mohamed
    Journal of Grid Computing, 2023, 21