Skip to main content
SpringerLink
Log in

Evolutionary Green Computing Solutions for Distributed Cyber Physical Systems

  • Chapter
Evolutionary Based Solutions for Green Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 432))

Abstract

Distributed Cyber Physical Systems (DCPSs) are networks of computing systems that utilize information from their physical surroundings to provide important services such as smart health, energy efficient cloud computing, and smart grids. Ensuring their green operation, which includes energy efficiency, thermal safety, and long term uninterrupted operation increases the scalability and sustainability of these infrastructures. Achieving this goal often requires researchers to harness an understanding of the interactions between the computing equipment and its physical surroundings.Modeling these interactions can be computationally challenging with the resources on hand and the operating requirements of such systems. To overcome these computational difficulties researchers have utilized Evolutionary Algorithms (EAs), which employ a randomized search to find a near optimal solution comparatively quickly and with compelling performance compared to heuristics in many domains. In this chapter we review several EA solutions for green DCPSs.We introduce three representative DCPS examples including Data Centers (DCs), Wireless Sensor Networks (WSNs), and Body Sensor Networks (BSN) and discuss several green computing problems and their EA based solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y., Talbi, E., Zomaya, A., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing (2011)

    Google Scholar 

  2. Xue, F., Sanderson, A., Graves, R.: Multi-objective routing in wireless sensor networks with a differential evolution algorithm. In: Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC 2006, pp. 880–885 (2006)

    Google Scholar 

  3. Tang, Q., Tummala, N., Gupta, S., Schwiebert, L.: Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue. IEEE Transactions on Biomedical Engineering 52(7), 1285–1294 (2005)

    Article  Google Scholar 

  4. Mukherjee, T., Banerjee, A., Varsamopoulos, G., Gupta, S.K.S., Rungta, S.: Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers. Computer Networks (June 2009), http://dx.doi.org/10.1016/j.comnet.2009.06.008

  5. Moore, J., Chase, J., Ranganathan, P., Sharma, R.: Making scheduling ”cool”: temperature-aware workload placement in data centers. In: ATEC 2005: Proceedings of the Annual Conference on USENIX Annual Technical Conference, pp. 5–5. USENIX Association, Berkeley (2005)

    Google Scholar 

  6. Liu, Z., Lin, M., Wierman, A., Low, S.H., Andrew, L.L.H.: Greening geographical load balancing. In: Proc. ACM SIGMETRICS. ACM, San Jose (2011)

    Google Scholar 

  7. Quick start guide to increase data center energy efficiency, General Services Administration (GSA) and the Federal Energy Management Program (FEMP). Tech. Rep. (September 2010)

    Google Scholar 

  8. Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Communications Magazine 40(8), 102–114 (2002)

    Article  Google Scholar 

  9. Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, p. 10 (2002)

    Google Scholar 

  10. Kansal, A., Hsu, J., Srivastava, M., Raghunathan, V.: Harvesting aware power management for sensor networks. In: Proceedings of the 43rd annual Design Automation Conference, pp. 651–656. ACM (2006)

    Google Scholar 

  11. Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman & Co. (1979)

    Google Scholar 

  12. Eiben, A., Smith, J.: Introduction to evolutionary computing. Springer (2003)

    Google Scholar 

  13. Islam, O., Hussain, S.: An intelligent multi-hop routing for wireless sensor networks. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2006 Workshops, pp. 239–242 (2006)

    Google Scholar 

  14. Bari, A., Wazed, S., Jaekel, A., Bandyopadhyay, S.: A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks 7(4), 665–676 (2009)

    Article  Google Scholar 

  15. Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)

    Article  Google Scholar 

  16. Shen, G., Zhang, Y.: A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. In: Advances in Swarm Intelligence, pp. 522–529 (2011)

    Google Scholar 

  17. Banerjee, A., Venkatasubramanian, K., Mukherjee, T., Gupta, S.: Ensuring safety, security and sustainability of mission-critical cyber physical systems. In: Proceeding on Special Issue on Cyber-Physical Systems (2011)

    Google Scholar 

  18. Gupta, S.K.S., Mukherjee, T., Varsamopoulos, G., Banerjee, A.: Research directions in energy-sustainable cyber-physical systems. Elsevier Comnets Special Issue in Sustainable Computing (SUSCOM), Invited Paper 1(1), 57–74 (2011)

    Google Scholar 

  19. Kant, K.: Data center evolution: A tutorial on state of the art, issues, and challenges. Computer Networks 53(17), 2939–2965 (2009)

    Article  Google Scholar 

  20. Chen, G., Malkowski, K., Kandemir, M., Raghavan, P.: Reducing power with performance constraints for parallel sparse applications. In: 19th IEEE International on Parallel and Distributed Processing Symposium, 2005. Proceedings, p. 8 (2005)

    Google Scholar 

  21. Kimura, H., Sato, M., Hotta, Y., Boku, T., Takahashi, D.: Emprical study on reducing energy of parallel programs using slack reclamation by dvfs in a power-scalable high performance cluster. In: 2006 IEEE International Conference on Cluster Computing, pp. 1–10 (2006)

    Google Scholar 

  22. Wang, L., von Laszewski, G., Dayal, J., Wang, F.: Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 368–377 (2010)

    Google Scholar 

  23. Freeh, V., Lowenthal, D.: Using multiple energy gears in mpi programs on a power-scalable cluster. In: Proceedings of the tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 164–173 (2005)

    Google Scholar 

  24. Lim, M., Freeh, V., Lowenthal, D.: Adaptive, transparent frequency and voltage scaling of communication phases in mpi programs. In: Proceedings of the ACM/IEEE, 2006 Conference, SC 2006, pp. 14–14 (2006)

    Google Scholar 

  25. Gruian, F., Kuchcinski, K.: Lenes: task scheduling for low-energy systems using variable supply voltage processors. In: Proceedings of the 2001 Asia and South Pacific Design Automation Conference, pp. 449–455 (2001)

    Google Scholar 

  26. Kulkarni, V., Forster, A., Venayagamoorthy, G.: Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials (99), 1–29 (2011)

    Google Scholar 

  27. Lindsey, S., Raghavendra, C.: Pegasis: Power-efficient gathering in sensor information systems. In: IEEE Aerospace Conference Proceedings, vol. 3, pp. 3–1125 (2002)

    Google Scholar 

  28. Shah, R., Rabaey, J.: Energy aware routing for low energy ad hoc sensor networks. In: 2002 IEEE Wireless Communications and Networking Conference, WCNC 2002, vol. 1, pp. 350–355 (2002)

    Google Scholar 

  29. Chang, J., Tassiulas, L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking 12(4), 609–619 (2004)

    Article  Google Scholar 

  30. Rao, L., Liu, X., Xie, L., Liu, W.: Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: 2010 IEEE Proceedings of INFOCOM, pp. 1–9 (2010)

    Google Scholar 

  31. Yu, G., Kouvels, P.: On min-max optimization of a collection of classical discrete optimization problems. Optimization Theory and Applications 98(1), 221–242 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  32. Le, K., Bilgir, O., Bianchini, R., Martonosi, M., Nguyen, T.: Managing the cost, energy consumption, and carbon footprint of Internet services. SIGMETRICS Perform. Eval. Rev. 38(1), 357–358 (2010)

    Article  Google Scholar 

  33. Abbasi, Z., Mukherjee, T., Varsamopoulos, G., Gupta, S.K.: Dynamic hosting management of web based applications over clouds. In: International Conference on High Performance Computing Conference(HiPC 2011) (December 2011)

    Google Scholar 

  34. Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for Internet-scale systems. In: Proc. ACM SIGCOMM, pp. 123–134 (2009)

    Google Scholar 

  35. Mitchell, M.: An introduction to genetic algorithms. The MIT press (1998)

    Google Scholar 

  36. Price, K., Storn, R., Lampinen, J.: Differential evolution: a practical approach to global optimization. Springer (2005)

    Google Scholar 

  37. Schaffer, J.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum Associates Inc. (1985)

    Google Scholar 

  38. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems, vol. (1). Oxford University Press, USA (1999)

    MATH  Google Scholar 

  39. Maniezzo, V., Carbonaro, A.: Ant colony optimization: an overview. In: Essays and Surveys in Metaheuristics, pp. 21–44 (2001)

    Google Scholar 

  40. Zhang, L., Li, K., Zhang, Y.: Green task scheduling algorithms with speeds optimization on heterogeneous cloud servers. In: Proceedings of the 2010 Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pp. 76–80. IEEE Computer Society (2010)

    Google Scholar 

  41. Lee, Y., Zomaya, A.: Minimizing energy consumption for precedence-constrained applications using dynamic voltage scaling. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 92–99 (2009)

    Google Scholar 

  42. Chen, W., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various qos requirements. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(1), 29–43 (2009)

    Article  Google Scholar 

  43. Liefooghe, A., Jourdan, L., Talbi, E.: A unified model for evolutionary multi-objective optimization and its implementation in a general purpose software framework. In: IEEE Symposium on Computational Intelligence in Miulti-Criteria Decision-Making, MCDM 2009, pp. 88–95. IEEE (2009)

    Google Scholar 

  44. Lee, Y., Zomaya, A.: A novel state transition method for metaheuristic-based scheduling in heterogeneous computing systems. IEEE Transactions on Parallel and Distributed Systems, 1215–1223 (2008)

    Google Scholar 

  45. Cohoon, J., Hegde, S., Martin, W., Richards, D.: Punctuated equilibria: a parallel genetic algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and their Application, pp. 148–154. L. Erlbaum Associates Inc. (1987)

    Google Scholar 

  46. Yu, J., Buyya, R., Tham, C.: Cost-based scheduling of scientific workflow application on utility grids. In: Proceedings of the First International Conference on e-Science and Grid Computing, pp. 140–147. IEEE Computer Society (2005)

    Google Scholar 

  47. Gupta, G., Younis, M.: Load-balanced clustering of wireless sensor networks. In: IEEE International Conference on Communications, ICC 2003, vol. 3, pp. 1848–1852 (2003)

    Google Scholar 

  48. Bashyal, S., Venayagamoorthy, G.: Collaborative routing algorithm for wireless sensor network longevity. In: 3rd IEEE International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, pp. 515–520 (2007)

    Google Scholar 

  49. Camilo, T., Carreto, C., Silva, J., Boavida, F.: An energy-efficient ant-based routing algorithm for wireless sensor networks. Ant Colony Optimization and Swarm Intelligence, 49–59 (2006)

    Google Scholar 

  50. Huang, R., Chen, Z., Xu, G.: Energy-aware routing algorithm in wsn using predication-mode. In: 2010 IEEE International Conference on Communications, Circuits and Systems (ICCCAS), pp. 103–107 (2010)

    Google Scholar 

  51. Moore, J., Chase, J., Ranganathan, P.: Weatherman: Automated, online, and predictive thermal mapping and management for data centers. In: IEEE International Conference on Autonomic Computing (ICAC), pp. 155–164 (June 2006)

    Google Scholar 

  52. Bash, C., Forman, G.: Cool job allocation: Measuring the power savings of placing jobs at cooling-efficient locations in the data center. HP Laboratories Palo Alto, Tech. Rep. HPL-2007-62 (August 2007)

    Google Scholar 

  53. Sharma, R., Bash, C., Patel, C., Friedrich, R., Chase, J.: Balance of power: Dynamic thermal management for internet data centers. IEEE Internet Computing, 42–49 (2005)

    Google Scholar 

  54. Rao, L., Liu, X., Ilic, M., Liu, J.: Mec-idc: joint load balancing and power control for distributed internet data centers. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 188–197. ACM (2010)

    Google Scholar 

  55. Buchbinder, N., Jain, N., Menache, I.: Online Job-Migration for Reducing the Electricity Bill in the Cloud. In: Domingo-Pascual, J., Manzoni, P., Palazzo, S., Pont, A., Scoglio, C. (eds.) NETWORKING 2011, Part I. LNCS, vol. 6640, pp. 172–185. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Arizona State University, Phoenix, USA

    Zahra Abbasi, Michael Jonas, Ayan Banerjee, Sandeep Gupta & Georgios Varsamopoulos

Authors
  1. Zahra Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Jonas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ayan Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sandeep Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georgios Varsamopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zahra Abbasi .

Editor information

Editors and Affiliations

  1. , Department of Electrical and Computer, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Samee Ullah Khan

  2. Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, Cracow, 31-155, Poland

    Joanna Kołodziej

  3. , Department of Computer Science, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Juan Li

  4. School of Information Technologies, University of Sydney, Sydney, 2006, New South Wales, Australia

    Albert Y. Zomaya

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Abbasi, Z., Jonas, M., Banerjee, A., Gupta, S., Varsamopoulos, G. (2013). Evolutionary Green Computing Solutions for Distributed Cyber Physical Systems. In: Khan, S., Kołodziej, J., Li, J., Zomaya, A. (eds) Evolutionary Based Solutions for Green Computing. Studies in Computational Intelligence, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30659-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30659-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30658-7

  • Online ISBN: 978-3-642-30659-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation