GreeDi: An energy efficient routing algorithm for big data on cloud

被引:89
作者
Baker, T. [1 ]
Al-Dawsari, B. [1 ]
Tawfik, H. [2 ]
Reid, D. [2 ]
Ngoko, Y. [3 ]
机构
[1] Liverpool John Moores Univ, Sch Comp & Math Sci, Liverpool L3 5UX, Merseyside, England
[2] Liverpool Hope Univ, Dept Math & Comp Sci, Liverpool, Merseyside, England
[3] Inst Gallillee, Lab Informat Paris Nord, Paris, France
关键词
Big data; Cloud computing; Routing algorithm; Data centre; CONSUMPTION;
D O I
10.1016/j.adhoc.2015.06.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:83 / 96
页数:14
相关论文
共 39 条
[1]   Power-aware linear programming based scheduling for heterogeneous computer clusters [J].
Al-Daoud, Hadil ;
Al-Azzoni, Issam ;
Down, Douglas G. .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2012, 28 (05) :745-754
[2]  
[Anonymous], 2010, P 2010 INT C EN EFF
[3]  
[Anonymous], GREEN CLOUD COMPUTIN
[4]  
Baer Paul., 2008, Exploring the 2020 Global Emissions Mitigation Gap Analysis for the Global Climate Network
[5]  
Baker Thar, 2013, Economics of Grids, Clouds, Systems, and Services. 10th International Conference, GECON 2013. Proceedings: LNCS 8193, P212, DOI 10.1007/978-3-319-02414-1_16
[6]   Energy Efficient Cloud Computing Environment Via Autonomic Meta-Director Framework [J].
Baker, Thar ;
Ngoko, Yanik ;
Tolosana-Calasanz, Rafael ;
Rana, Omer F. ;
Randles, Martin .
2013 SIXTH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE), 2014, :198-203
[7]   Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport [J].
Baliga, Jayant ;
Ayre, Robert W. A. ;
Hinton, Kerry ;
Tucker, Rodney S. .
PROCEEDINGS OF THE IEEE, 2011, 99 (01) :149-167
[8]   Energy Consumption in Optical IP Networks [J].
Baliga, Jayant ;
Ayre, Robert ;
Hinton, Kerry ;
Sorin, Wayne V. ;
Tucker, Rodney S. .
JOURNAL OF LIGHTWAVE TECHNOLOGY, 2009, 27 (13) :2391-2403
[9]  
Banerjee S., 2002, MOBIHOC 2002. Proceedings of the Third ACM International Symposium on Mobile Ad Hoc Networking and Computing, P146, DOI 10.1145/513800.513818
[10]  
Barthel C., 2001, P INT CLIM POL IT SE