Energy Efficient Cloud Computing Environment via Autonomic Meta-director Framework

The ever-increasing density in cloud computing users, services, and data centres has led to significant increases in network traffic and the associated energy consumed by its huge infrastructure, e.g. extra servers, switches, routers, which is required to respond quickly and effectively to users requests. Transferring data, via a high bandwidth connection between data centres and cloud users, consumes even larger amounts of energy than just processing and storing the data on a cloud data centre, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring data into a data centre located relatively far from the user's 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 user's requirements, e.g. response time, are met. This paper proposes a high-end autonomic meta-director framework to find the most energy efficient route to the green data centre by utilising the linear programming approach. The framework is, first, formalised by the situation calculus, and then evaluated against shortest path algorithm with minimum number of nodes traversed.

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

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

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

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

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

[6]  Ching-Chi Lin,et al.  Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

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

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

[9]  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.

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

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

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

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