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

Abstract 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.

[1]  Thar Baker,et al.  Towards Autonomic Cloud Services Engineering via Intention Workflow Model , 2013, GECON.

[2]  P Vetter,et al.  Power Trends in Communication Networks , 2011, IEEE Journal of Selected Topics in Quantum Electronics.

[3]  Archan Misra,et al.  Minimum energy paths for reliable communication in multi-hop wireless networks , 2002, MobiHoc '02.

[4]  Lizhe Wang,et al.  GreenIT Service Level Agreements , 2010 .

[5]  H. T. Mouftah,et al.  Optimal Reconfiguration of the Cloud Network for Maximum Energy Savings , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[6]  Douglas G. Down,et al.  Power-Aware Linear Programming based Scheduling for heterogeneous computer clusters , 2010, International Conference on Green Computing.

[7]  Hector J. Levesque,et al.  Foundations for the Situation Calculus , 1998, Electron. Trans. Artif. Intell..

[8]  Chunming Qiao,et al.  A comprehensive minimum energy routing scheme for wireless ad hoc networks , 2004, IEEE INFOCOM 2004.

[9]  R.S. Tucker,et al.  Energy Consumption in Optical IP Networks , 2009, Journal of Lightwave Technology.

[10]  Minghua Chen,et al.  Moving Big Data to The Cloud: An Online Cost-Minimizing Approach , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Rodney S. Tucker,et al.  Power consumption and energy efficiency in the internet , 2011, IEEE Network.

[12]  María Blanca Caminero,et al.  Characterising the Power Consumption of Hadoop Clouds - A Social Media Analysis Case Study , 2013, CLOSER.

[13]  Minghua Chen,et al.  Moving big data to the cloud , 2013, 2013 Proceedings IEEE INFOCOM.

[14]  R. Buyya,et al.  Green Cloud Computing and Environmental Sustainability , 2012 .

[15]  Massimo Tornatore,et al.  Low-carbon routing algorithms for cloud computing services in IP-over-WDM networks , 2012, 2012 IEEE International Conference on Communications (ICC).

[16]  Teresa H. Meng,et al.  Minimum energy mobile wireless networks , 1998, ICC '98. 1998 IEEE International Conference on Communications. Conference Record. Affiliated with SUPERCOMM'98 (Cat. No.98CH36220).

[17]  Reuven Cohen,et al.  Efficient immunization strategies for computer networks and populations. , 2002, Physical review letters.

[18]  Ching-Chi Lin,et al.  Energy-efficient Virtual Machine Provision Algorithms for Cloud Systems , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[19]  Jean-Marc Menaud,et al.  Performance and Power Management for Cloud Infrastructures , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[20]  R. Buyya,et al.  Green Cloud Computing and Environmental Sustainability , 2012 .

[21]  K. Scott,et al.  Routing and channel assignment for low power transmission in PCS , 1996, Proceedings of ICUPC - 5th International Conference on Universal Personal Communications.

[22]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[23]  Bruce Nordman,et al.  Data network equipment energy use and savings potential in buildings , 2012 .

[24]  Fumiko Satoh,et al.  Total Energy Management System for Cloud Computing , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[25]  Erol Gelenbe,et al.  Energy-Efficient Cloud Computing , 2010, Comput. J..

[26]  Richard E. Brown,et al.  Electricity used by office equipment and network equipment in the U.S.: Detailed report and appendices , 2001 .

[27]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.

[28]  Marco Mellia,et al.  Energy saving and network performance: a trade-off approach , 2010, e-Energy.

[29]  Thar Baker,et al.  Energy Efficient Cloud Computing Environment via Autonomic Meta-director Framework , 2013, 2013 Sixth International Conference on Developments in eSystems Engineering.

[30]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[31]  Jinhua Zhu,et al.  Model and Protocol for Energy-Efficient Routing over Mobile Ad Hoc Networks , 2011, IEEE Transactions on Mobile Computing.