BrownoutCon: A software system based on brownout and containers for energy-efficient cloud computing

被引:24
|
作者
Xu, Minxian [1 ,2 ]
Buyya, Rajkumar [2 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China
[2] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst CLOUDS Lab, Melbourne, Vic, Australia
关键词
Cloud data centers; Energy efficiency; Quality of service; Containers; Microservices; Brownout; VIRTUAL MACHINES; CONSOLIDATION; DOCKER;
D O I
10.1016/j.jss.2019.05.031
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
VM consolidation and Dynamic Voltage Frequency Scaling approaches have been proved to be efficient to reduce energy consumption in cloud data centers. However, the existing approaches cannot function efficiently when the whole data center is overloaded. An approach called brownout has been proposed to solve the limitation, which dynamically deactivates or activates optional microservices or containers. In this paper, we propose a brownout-based software system for container-based clouds to handle overloads and reduce power consumption. We present its design and implementation based on Docker Swarm containers. The proposed system is integrated with existing Docker Swarm without the modification of their configurations. To demonstrate the potential of BrownoutCon software in offering energy-efficient services in brownout situation, we implemented several policies to manage containers and conducted experiments on French Grid'5000 cloud infrastructure. The results show the currently implemented policies in our software system can save about 10%-40% energy than the existing baselines while ensuring quality of services. (C) 2019 Elsevier Inc. All rights reserved.
引用
下载
收藏
页码:91 / 103
页数:13
相关论文
共 50 条
  • [1] Energy-Efficient Cloud Computing
    Berl, Andreas
    Gelenbe, Erol
    Di Girolamo, Marco
    Giuliani, Giovanni
    De Meer, Hermann
    Dang, Minh Quan
    Pentikousis, Kostas
    COMPUTER JOURNAL, 2010, 53 (07): : 1045 - 1051
  • [2] Energy-efficient approaches to Cloud Computing
    Asha, N.
    Rao, G. Raghavendra
    2014 INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), 2014, : 337 - 342
  • [3] Recent Trends in Energy-Efficient Cloud Computing
    Mastelic, Toni
    Brandic, Ivona
    IEEE CLOUD COMPUTING, 2015, 2 (01): : 40 - 47
  • [4] Energy-Efficient Cloud Computing for Smart Phones
    Arya, Nancy
    Chaudhary, Sunita
    Taruna, S.
    EMERGING TRENDS IN EXPERT APPLICATIONS AND SECURITY, 2019, 841 : 111 - 115
  • [5] Machine Learning based Thermal Prediction for Energy-efficient Cloud Computing
    Nisce, Icess
    Jiang, Xunfei
    Vishnu, Sai Pilla
    2023 IEEE 20TH CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE, CCNC, 2023,
  • [6] An Energy-efficient Approach based on Learning Automata in Mobile Cloud Computing
    Arani, Mostafa Ghobaei
    Moghadasi, Najmeh
    INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2015, 8 (04): : 47 - 58
  • [7] Energy-efficient data replication in cloud computing datacenters
    Boru, Dejene
    Kliazovich, Dzmitry
    Granelli, Fabrizio
    Bouvry, Pascal
    Zomaya, Albert Y.
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2015, 18 (01): : 385 - 402
  • [8] Energy-Efficient Hybrid Framework for Green Cloud Computing
    Alarifi, Abdulaziz
    Dubey, Kalka
    Amoon, Mohammed
    Altameem, Torki
    Abd El-Samie, Fathi E.
    Altameem, Ayman
    Sharma, S. C.
    Nasr, Aida A.
    IEEE ACCESS, 2020, 8 (08): : 115356 - 115369
  • [9] Holistic Management for a more Energy-Efficient Cloud Computing
    Ayguade, Eduard
    Torres, Jordi
    ERCIM NEWS, 2010, (83): : 29 - 30
  • [10] Energy-Efficient Resource Management in Mobile Cloud Computing
    Jin, Xiaomin
    Liu, Yuanan
    Fan, Wenhao
    Wu, Fan
    Tang, Bihua
    IEICE TRANSACTIONS ON COMMUNICATIONS, 2018, E101B (04) : 1010 - 1020