Skip to main content
Springer Nature Link
Log in

Energy and Security Awareness in Evolutionary-Driven Grid Scheduling

  • Chapter
Evolutionary Based Solutions for Green Computing

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

  • 880 Accesses

Abstract

Ensuring the energy efficiency, thermal safety, and security-awareness in today’s large-scale distributed computing systems is one of the key research issues that leads to the improvement of the system scalability and requires researchers to harness an understanding of the interactions between the system external users and the internal service and resource providers. Modeling these interactions can be computationally challenging especially in the infrastructures with different local access and management policies such as computational grids and clouds. In this chapter, we approach the independent batch scheduling in Computational Grid (CG) as a three-objective minimization problem with Makespan, Flowtime and energy consumption in risky and security scenarios. Each physical resource in the system is equipped with Dynamic Voltage Scaling (DVS) module for optimizing the cumulative power energy utilized by the system. The effectiveness of six genetic-based single- and multi-population grid schedulers has been justified in comprehensive empirical analysis.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

Similar content being viewed by others

References

  1. Abawajy, J.: An efficient adaptive scheduling policy for high performance computing. Future Generation Computer Systems 25(3), 364–370 (2009)

    Article  Google Scholar 

  2. Abraham, A., Buyya, R., Nath, B.: Nature’s heuristics for scheduling jobs on computational grids. In: Proc. of the 8th IEEE International Conference on Advanced Computing and Communications, India, pp. 45–52 (2000)

    Google Scholar 

  3. Aguirre, H., Tanaka, K.: Working principles, behavior, and performance of moeas on mnk-landscapes. European Journal of Operational Research 181, 1670–1690 (2007)

    Article  MATH  Google Scholar 

  4. Ali, S., Siegel, H., Maheswaran, M., Ali, S., Hensgen, D.: Task execution time modeling for heterogeneous computing systems. In: Proc. of the Workshop on Heterogeneous Computing, pp. 185–199 (2000)

    Google Scholar 

  5. Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Evolutionary Computation, Part 1 and 2. IOP Publishing Ltd (2000)

    Google Scholar 

  6. Baker, R.J.: CMOS: circuit design, layout, and simulation, 2nd edn. Wiley (2008)

    Google Scholar 

  7. Bartschi Wall, M.:: A Genetic Algorithm for Resource-Constrained Scheduling. PhD Thesis, Massachusetts Institute of Technology, MA (1996)

    Google Scholar 

  8. Beloglazov, A., Buyya, R., Lee, Y., Zomaya, A.Y.: A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers 82, 47–111 (2011)

    Article  Google Scholar 

  9. Brucker, P.: Scheduling Algorithms. Springer (2007)

    Google Scholar 

  10. Buyya, R., Murshed, M.: Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience 14(13-15), 1175–1220 (2002)

    Article  MATH  Google Scholar 

  11. Carretero, J., Xhafa, F.: Using Genetic Algorithms for Scheduling Jobs in Large Scale Grid Applications. Journal of Technological and Economic Development –A Research Journal of Vilnius Gediminas Technical University 12(1), 11–17 (2006)

    Google Scholar 

  12. Davis, L. (ed.): Handbook of Genetic Algoriithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  13. Fibich, P., Matyska, L., Rudová, H.: Model of grid scheduling problem. In: Proc. of the AAAI 2005 Workshop on Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing (2005)

    Google Scholar 

  14. Garg, S., Buyya, R., Segel, H.: Scheduling parallel aplications on utility grids: Time and cost trade-off management. In: Mans, B. (ed.) Proc. of the 32nd ACSC, Wellington, Australia. CRPIT, vol. 91 (2009)

    Google Scholar 

  15. Goldberg, D.E., Lingle Jr., R.: Alleles, loci and the travelling salesman problem. In: Proc. of the ICA 1985, Pittsburgh (1985)

    Google Scholar 

  16. Gosh, P., Das, S.: Mobility-aware cost-efficient job scheduling for single-class grid jobs in a generic mobile grid architecture. Future Generation Computer Systems 26(8), 1356–1367 (2010)

    Article  Google Scholar 

  17. Graham, R., Lawler, E., Lenstra, J., Rinnooy Kan, A.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics 5, 287–326 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  18. Grueninger, T.: Multimodal optimization using genetic algorithms. Technical report, Department of Mechanical Engineering. MIT, Cambridge (1997)

    Google Scholar 

  19. Guzek, K., Pecero, J.E., Dorrosoro, B., Bouvry, P., Khan, S.U.: A cellular genetic algorithm for scheduling applications and energy-aware communication optimization. In: Proc. of the ACM/IEEE/IFIP Int. Conf. on High Performance Computing and Simulation (HPCS), Caen, France, pp. 241–248 (2010)

    Google Scholar 

  20. Humphrey, M., Thompson, M.: Security implications of typical grid computing usage scenarios. In: Proc. of the Conf. on High Performance Distributed Computing (2001)

    Google Scholar 

  21. Hwang, S., Kesselman, C.: A flexible framework for fault tolerance in the grid. J. of Grid Computing 1(3), 251–272 (2003)

    Article  MATH  Google Scholar 

  22. Kessaci, Y., Mezmaz, M., Melab, N., Talbi, E.-G., Tuyttens, D.: Parallel Evolutionary Algorithms for Energy Aware Scheduling. In: Bouvry, P., González-Vélez, H., Kołodziej, J. (eds.) Intelligent Decision Systems in Large-Scale Distributed Environments. SCI, vol. 362, pp. 75–100. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Khan, S.: A goal programming approach for the joint optimization of energy consumption and response time in computational grids. In: Proc. of the 28th IEEE International Performance Computing and Communications Conference (IPCCC), Phoenix, AZ, USA, pp. 410–417 (2009a)

    Google Scholar 

  24. Khan, S.: A self-adaptive weighted sum technique for the joint optimization of performance and power consumption in data centers. In: Proc. of the 22nd International Conference on Parallel and Distributed Computing and Communication Systems (PDCCS), USA, pp. 13–18 (2009b)

    Google Scholar 

  25. Khan, S.U., Ahmad, I.: A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids. IEEE Tran. on Parallel and Distributed Systems 20(3), 346–360 (2009)

    Article  MathSciNet  Google Scholar 

  26. Kliazovich, D., Bouvry, P., Khan, S.U.: Dens: Data center energy-efficient network-aware scheduling. In: Proc. of ACM/IEEE International Conference on Green Computing and Communications (GreenCom), Hangzhou, China, pp. 69–75 (December 2010)

    Google Scholar 

  27. Klusaček, D., Rudová, H.: Efficient grid scheduling through the incremental schedule-based approach. Computational Intelligence 27(1), 4–22 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. Kołodziej, J.: Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems. SCI, vol. 419. Springer, Heidelberg (2012)

    Book  Google Scholar 

  29. Kołodziej, J., Xhafa, F.: Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population. Future Generation Computer Systems 27, 1035–1046 (2011)

    Article  Google Scholar 

  30. Kołodziej, J., Xhafa, F.: Integration of task abortion and security requirements in ga-based meta-heuristics for independent batch grid scheduling. Computers and Mathematics with Applications 63, 350–364 (2011)

    Article  Google Scholar 

  31. Kołodziej, J., Xhafa, F.: Meeting security and user behaviour requirements in grid scheduling. Simulation Modelling Practice and Theory 19(1), 213–226 (2011)

    Article  Google Scholar 

  32. Kołodziej, J., Xhafa, F.: Modern approaches to modelling user requirements on resource and task allocation in hierarchical computational grids. Int. J. on Appled Mathematics and Computer Science 21(2), 243–257 (2011)

    Article  Google Scholar 

  33. Kwok, Y.-K., Hwang, K., Song, S.: Selfish grids: Game-theoretic modeling and nas/psa benchmark evaluation. IEEE Tran. on Parallel and Distributing Systems 18(5), 1–16 (2007)

    Article  Google Scholar 

  34. Lapin, L.: Probability and Statistics for Modern Engineering, 2nd edn. (1998)

    Google Scholar 

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

    Google Scholar 

  36. Mann, P.S.: Introductory Statistics, 7th edn. Wiley (2010)

    Google Scholar 

  37. Mejia-Alvarez, P., Levner, E., Mossé, D.: Adaptive scheduling server for power-aware real-time tasks. ACM Trans. Embed. Comput. Syst. 3(2), 284–306 (2004)

    Article  Google Scholar 

  38. Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y., Talbi, E.-G., Zomaya, A., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. (2011), doi:10.1016/j.jpdc.2011.04.007

    Google Scholar 

  39. Miao, L., Qi, Y., Hou, D., Dai, Y., Shi, Y.: A multi-objective hybrid genetic algorithm for energy saving task scheduling in cmp system. In: Proc. of IEEE Int. Conf. on Systems, Man and Cybernetics (ICSMC 2008), pp. 197–201 (2008)

    Google Scholar 

  40. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer (1992)

    Google Scholar 

  41. Muhammad, R., Hussain, F., Hussain, O.: Neural network-based approach for predicting trust values based on non-uniform input in mobile applications. The Computer Journal Online (2011)

    Google Scholar 

  42. Olivier, I., Smith, D., Holland, J.: A study of permutation crossover operators on the travelling salesman problem. In: Proc. of the ICGA 1987, Cambridge, MA, pp. 224–230 (1987)

    Google Scholar 

  43. Pinel, F., Pecero, J., Bouvry, P., Khan, S.U.: Memory-aware green scheduling on multi-core processors. In: Proc. of the 39th IEEE International Conference on Parallel Processing (ICPP), pp. 485–488 (2010)

    Google Scholar 

  44. Shen, G., Zhang, Y.: A new evolutionary algorithm using shadow price guided operators. Applied Soft Computing 11(2), 1983–1992 (2011)

    Article  Google Scholar 

  45. Shen, G., Zhang, Y.: A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. In: Proc. of the 3rd International Conference on Swarm Intelligence - (ICSI 2011), pp. 522–529 (2011)

    Google Scholar 

  46. Song, S., Hwang, K., Kwok, Y.: Trusted grid computing with security binding and trust integration. J. of Grid Computing 3(1-2), 53–73 (2005)

    Article  Google Scholar 

  47. Song, S., Hwang, K., Kwok, Y.: Risk-resilient heuristics and genetic algorithms for security- assured grid job scheduling. IEEE Tran. on Computers 55(6), 703–719 (2006)

    Article  Google Scholar 

  48. Whitley, D., Rana, S., Heckendorn, R.: The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7, 33–47 (1998)

    Google Scholar 

  49. Wu, C.-C., Sun, R.-Y.: An integrated security-aware job scheduling strategy for large-scale computational grids. Future Generation Computer Systems 26(2), 198–206 (2010)

    Article  Google Scholar 

  50. Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Generation Computer Systems 26, 608–621 (2010)

    Article  Google Scholar 

  51. Xhafa, F., Carretero, J., Abraham, A.: Genetic algorithm based schedulers for grid computing systems. Int. J. of Innovative Computing, Information and Control 3(5), 1053–1071 (2007)

    Google Scholar 

  52. Xhafa, F., Carretero, J., Barolli, L., Durresi, A.: Requirements for an event-based simulation package for grid systems. Journal of Interconnection Networks 8(2), 163–178 (2007)

    Article  Google Scholar 

  53. Zomaya, A.Y.: Energy-aware scheduling and resource allocation for large-scale distributed systems. In: 11th IEEE International Conference on High Performance Computing and Communications (HPCC), Seoul, Korea (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Joanna Kołodziej

  2. Department of Electrical and Computer Engineering, North Dakota State University, ND, 58108, Fargo, USA

    Samee U. Khan

  3. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China

    Lizhe Wang

  4. China University of Geosciences, Wuhan, China

    Dan Chen

  5. School of Information Technologies, University of Sydney, Sydney, NSW, 2006, Australia

    Albert Y. Zomaya

Authors
  1. Joanna Kołodziej
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samee U. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lizhe Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Albert Y. Zomaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joanna Kołodziej .

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

Kołodziej, J., Khan, S.U., Wang, L., Chen, D., Zomaya, A.Y. (2013). Energy and Security Awareness in Evolutionary-Driven Grid Scheduling. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30659-4_4

  • 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