Online Energy-efficient Resource Allocation in Cloud Computing Data Centers

被引:2
作者
Ben Abdallah, Habib [1 ]
Sanni, Afeez Adewale [1 ]
Thummar, Krunal [1 ]
Halabi, Talal [1 ]
机构
[1] Univ Winnipeg, Appl Comp Sci Dept, Winnipeg, MB, Canada
来源
2021 24TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS (ICIN) | 2021年
关键词
Cloud computing; green computing; power management; resource allocation; energy consumption; energy efficiency; memory management; tabu search; greedy local search; simulated annealing; metaheuristics; knapsack problem;
D O I
10.1109/ICIN51074.2021.9385557
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of non-dominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.
引用
收藏
页数:8
相关论文
共 11 条
[1]  
Acar H, 2016, PROCEEDINGS OF THE 2016 5TH INTERNATIONAL CONFERENCE ON SMART CITIES AND GREEN ICT SYSTEMS (SMARTGREENS 2016), P417
[2]  
[Anonymous], 2014, International Journal of Cloud Computing and Services Science (IJ-CLOSER)
[3]   Minimization of Costs and Energy Consumption in a Data Center by a Workload-Based Capacity Management [J].
Da Costa, Georges ;
Oleksiak, Ariel ;
Piatek, Wojciech ;
Salom, Jaume ;
Siso, Laura .
ENERGY EFFICIENT DATA CENTERS (E2DC 2014), 2015, 8945 :102-119
[4]   A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems [J].
Hameed, Abdul ;
Khoshkbarforoushha, Alireza ;
Ranjan, Rajiv ;
Jayaraman, Prem Prakash ;
Kolodziej, Joanna ;
Balaji, Pavan ;
Zeadally, Sherali ;
Malluhi, Qutaibah Marwan ;
Tziritas, Nikos ;
Vishnu, Abhinav ;
Khan, Samee U. ;
Zomaya, Albert .
COMPUTING, 2016, 98 (07) :751-774
[5]  
Kar I, 2016, 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), P3545, DOI 10.1109/ICEEOT.2016.7755364
[6]  
Karyakin A., 2017, P 13 INT WORKSH DAT
[7]   A Tabu Search Algorithm for the Location of Data Centers and Software Components in Green Cloud Computing Networks [J].
Larumbe, Federico ;
Sanso, Brunilde .
IEEE TRANSACTIONS ON CLOUD COMPUTING, 2013, 1 (01) :22-35
[8]  
Liu X., 2020, IEEE TRANS NETW SERV
[9]   Energy-Efficient Service Allocation Techniques in Cloud: A Survey [J].
Mishra, Sambit Kumar ;
Sahoo, Sampa ;
Sahoo, Bibhudatta ;
Jena, Sanjay Kumar .
IETE TECHNICAL REVIEW, 2020, 37 (04) :339-352
[10]   Tight Evaluation of Real-Time Task Schedulability for Processor's DVS and Nonvolatile Memory Allocation [J].
Nam, Sunhwa A. ;
Cho, Kyungwoon ;
Bahn, Hyokyung .
MICROMACHINES, 2019, 10 (06)