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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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
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)
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)
Quick start guide to increase data center energy efficiency, General Services Administration (GSA) and the Federal Energy Management Program (FEMP). Tech. Rep. (September 2010)
Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Communications Magazine 40(8), 102–114 (2002)
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)
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)
Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman & Co. (1979)
Eiben, A., Smith, J.: Introduction to evolutionary computing. Springer (2003)
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)
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)
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)
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)
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)
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)
Kant, K.: Data center evolution: A tutorial on state of the art, issues, and challenges. Computer Networks 53(17), 2939–2965 (2009)
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)
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)
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)
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)
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)
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)
Kulkarni, V., Forster, A., Venayagamoorthy, G.: Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials (99), 1–29 (2011)
Lindsey, S., Raghavendra, C.: Pegasis: Power-efficient gathering in sensor information systems. In: IEEE Aerospace Conference Proceedings, vol. 3, pp. 3–1125 (2002)
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)
Chang, J., Tassiulas, L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking 12(4), 609–619 (2004)
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)
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)
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)
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)
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)
Mitchell, M.: An introduction to genetic algorithms. The MIT press (1998)
Price, K., Storn, R., Lampinen, J.: Differential evolution: a practical approach to global optimization. Springer (2005)
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)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems, vol. (1). Oxford University Press, USA (1999)
Maniezzo, V., Carbonaro, A.: Ant colony optimization: an overview. In: Essays and Surveys in Metaheuristics, pp. 21–44 (2001)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)