Energy Aware Clouds Scheduling Using Anti-load Balancing Algorithm - EACAB

Cheikhou Thiam, Georges Da-Costa, Jean-Marc Pierson

Abstract

Cloud computing is a highly scalable and cost-effective infrastructure for running HPC, enterprise and Web applications. However rapid growth of the demand for computational power by scientific, business and web- applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. Hence, energy-efficient solutions are required to minimize their energy consumption. The objective of our approach is to reduce data center’s total energy consumption by controlling cloud applications’ overall resource usage while guarantying service level agreement. This article presents Energy aware clouds scheduling using anti-load balancing algorithm (EACAB). The proposed algorithm works by associating a credit value with each node. The credit of a node depends on its affinity to its jobs, its current workload and its communication behavior. Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. The experiment results show that the cloud application energy consumption and energy efficiency is being improved effectively.

References

  1. Adiga, N. R., Almási, G., Almasi, G. S., Aridor, Y., Barik, R., Beece, D., Bellofatto, R., Bhanot, G., Bickford, R., Blumrich, M., et al. (2002). An overview of the bluegene/l supercomputer. In Supercomputing, ACM/IEEE 2002 Conference, pages 60-60. IEEE.
  2. Beloglazov, A. and Buyya, R. (2010). Adaptive thresholdbased approach for energy-efficient consolidation of virtual machines in cloud data centers. In Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, page 4. ACM.
  3. Buyya, R. and Murshed, M. (2002). Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. volume 14, pages 1175-1220. Wiley Online Library.
  4. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., and Buyya, R. (2011). Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. volume 41, pages 23-50. Wiley Online Library.
  5. Chow, K.-P. and Kwok, Y.-K. (2002). On load balancing for distributed multiagent computing. Parallel and Distributed Systems, IEEE Transactions on, 13(8):787- 801.
  6. Ellis, C. S. (1999). The case for higher-level power management. In Hot Topics in Operating Systems, 1999. Proceedings of the Seventh Workshop on, pages 162- 167. IEEE.
  7. Hermenier, F., Lorca, X., Menaud, J.-M., Muller, G., and Lawall, J. (2009). Entropy: a consolidation manager for clusters. In Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, pages 41-50. ACM.
  8. Jeon, H., Lee, W. H., and Chung, S. W. (2010). Load unbalancing strategy for multicore embedded processors. volume 59, pages 1434-1440. IEEE.
  9. Lawson, B. and Smirni, E. (2005). Power-aware resource allocation in high-end systems via online simulation. In Proceedings of the 19th annual international conference on Supercomputing, ICS 7805, pages 229-238, New York, NY, USA. ACM.
  10. Pierson, J.-M. and Casanova, H. (2011). On the utility of dvfs for power-aware job placement in clusters. In Euro-Par 2011 Parallel Processing, pages 255-266. Springer.
  11. Pinheiro, E., Bianchini, R., Carrera, E. V., and Heath, T. (2001). Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on compilers and operating systems for low power, volume 180, pages 182-195. Barcelona, Spain.
  12. Srikantaiah, S., Kansal, A., and Zhao, F. (2008). Energy aware consolidation for cloud computing. In Proceedings of the 2008 conference on Power aware computing and systems, volume 10. USENIX Association.
  13. Thiam, C. and Da Costa, G. (2011). Anti-Load Balancing to Reduce Energy Consumption (student paper). In Ivnyi, P. and Topping, B. H. V., editors, International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, Ajaccio-CorsicaFrance, 13/01/2011-15/04/2011, page (on line), http://www.civil-comp.com/conf/progp2011.htm. Civil-Comp Proceedings.
  14. Warren, M., Weigle, E., and Feng, W. (2002). High-density computing: A 240-node beowulf in one cubic meter. In Supercomputing 2002.
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Paper Citation


in Harvard Style

Thiam C., Da-Costa G. and Pierson J. (2014). Energy Aware Clouds Scheduling Using Anti-load Balancing Algorithm - EACAB . In Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-025-3, pages 82-89. DOI: 10.5220/0004856600820089


in Bibtex Style

@conference{smartgreens14,
author={Cheikhou Thiam and Georges Da-Costa and Jean-Marc Pierson},
title={Energy Aware Clouds Scheduling Using Anti-load Balancing Algorithm - EACAB},
booktitle={Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},
year={2014},
pages={82-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004856600820089},
isbn={978-989-758-025-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - Energy Aware Clouds Scheduling Using Anti-load Balancing Algorithm - EACAB
SN - 978-989-758-025-3
AU - Thiam C.
AU - Da-Costa G.
AU - Pierson J.
PY - 2014
SP - 82
EP - 89
DO - 10.5220/0004856600820089