Advertisement

The Journal of Supercomputing

, Volume 71, Issue 3, pp 1143–1162 | Cite as

A novel metaheuristic algorithm and utility function for QoS based scheduling in user-centric grid systems

  • K. Kianfar
  • G. Moslehi
  • R. Yahyapour
Article

Abstract

Scheduling dynamically arriving parallel jobs on a grid system is one of the most challenging problems in supercomputer centers. Response time guarantee is one aspect of providing quality of service (QoS) in grids. Jobs are differently charged depending on the response time demanded by the user and the system must provide completion time guarantees. To tackle these challenges, we propose a new type of utility function for defining QoS in user-centric systems. The proposed utility function is a general form of functions in the literature. This function provides customers and system managers with more options to design SLA contracts. Also, its two due dates can make customers more confident and produce more profit for system providers. This paper develops a novel simulated annealing algorithm combined with geometric sampling (GSSA) for scheduling parallel jobs on a grid system. The proposed algorithm is compared with two other methods from the literature using three metrics of total utility, system utilization and the percentage of accepted jobs. The results show that the proposed GSSA algorithm is able to improve the metrics via better use of resources and also through proper acceptance or rejection decisions made on newly arriving jobs.

Keywords

Grid system Parallel job scheduling Quality of service  Utility function Simulated annealing 

References

  1. 1.
    Feitelson DG, Rudolph L (1995) Parallel job scheduling: issues and approaches. In: Feitelson DG, Rudolph L (eds) Job scheduling strategies for parallel processing. Lecture notes in computer science, vol 949. Springer, Berlin, pp 1–18Google Scholar
  2. 2.
    Chun BN, Culler DE (2002) User-centric performance analysis of market-based cluster batch schedulers. Paper presented at the 2nd IEEE/ACM international symposium on cluster computing and the gridGoogle Scholar
  3. 3.
    Irwin DE, Grit LE, Chase JS (2004) Balancing risk and reward in a market-based task service. Paper presented at the 13th IEEE international symposium on high performance distributed computingGoogle Scholar
  4. 4.
    Islam MK (2008) QoS in parallel job scheduling. The Ohio State University, ColumbusGoogle Scholar
  5. 5.
    Netto MAS, Bubendorfer K, Buyya R (2007) SLA-based advance reservations with flexible and adaptive time QoS parameters. In: Krämer BJ , Lin K-J, Narasimhan P (eds) Service-oriented computing—ICSOC. Lecture notes in computer science, vol 4749. Springer, Berlin, pp 119–131Google Scholar
  6. 6.
    Buyya R, Murshed M, Abramson D, Venugopal S (2005) Scheduling parameter sweep applications on global grids: a deadline and budget constrained cost-time optimization algorithm. Softw Pract Exp 35:491–512CrossRefGoogle Scholar
  7. 7.
    Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14(3–4):217–230Google Scholar
  8. 8.
    Calheiros RN, Buyya R (2013) Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans Parallel Distrib Syst. doi: 10.1109/TPDS.2013.238
  9. 9.
    Mattess M, Calheiros RN, Buyya R (2013) Scaling mapreduce applications across hybrid clouds to meet soft deadlines. In: IEEE 27th international conference on advanced information networking and applications, Barcelona, pp 629–636Google Scholar
  10. 10.
    Calheiros RN, Buyya R (2012) Cost-effective provisioning and scheduling of deadline-constrained applications in hybrid clouds. In: Wang XS, Cruz I, Delis A, Huang G (eds) Web information systems engineering—WISE. Lecture notes in computer science, vol 7651. Springer, Berlin, pp 171–184Google Scholar
  11. 11.
    Ramamritham K, Stankovic JA, Shiah P-F (1990) Efficient scheduling algorithms for real-time multiprocessor systems. IEEE Trans Parallel Distrib Syst 1(2):184–194CrossRefGoogle Scholar
  12. 12.
    Sherwani J, Ali N, Lotia N, Hayat Z, Buyya R (2004) Libra: a computational economy based job scheduling system for clusters. In: Software: practice and experience, vol 34. Wiley, New York, pp 573–590Google Scholar
  13. 13.
    Yeo CS, Buyya R (2007) Pricing for utility-driven resource management and allocation in clusters. Int J High Perform Comput Appl 21(4):405–418CrossRefGoogle Scholar
  14. 14.
    Yeo CS, buyya R (2006) Managing risk of inaccurate runtime estimates for deadline constrained job admission control in clusters. In: International conference on parallel processing. IEEE Explore, Columbus, pp 451–458Google Scholar
  15. 15.
    Arabnia HR (1990) A parallel algorithm for the arbitrary rotation of digitized images using process-and-data-decomposition approach. J Parallel Distrib Comput 10(2):188–193CrossRefGoogle Scholar
  16. 16.
    Chunlin L, Layuan L (2006) QoS based resource scheduling by computational economy in computational grid. Inf Process Lett 98:119–126CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Dogan A, Ozgüner F (2002) Scheduling independent tasks with QoS requirements in grid computing with time-varying resource prices. Lect Notes Comput Sci 2536:58–69CrossRefGoogle Scholar
  18. 18.
    Golconda KS, Ozguner F, Dogan A (2004) A comparison of static QoSbased scheduling heuristics for a meta-task with multiple QoS dimensions in heterogeneous computing. In: Parallel and distributed processing symposium. IEEE XploreGoogle Scholar
  19. 19.
    Ernemann C (2002) Economic scheduling in grid computing. In: 8th international workshop job scheduling strategies for parallel processing. Springer, UK, pp 128–152Google Scholar
  20. 20.
    Foster I, Fidler M, Roy A, Sander V, Winkler L (2004) End-to-end quality of service for high-end applications. Comput Commun 27(14):1375–1388CrossRefGoogle Scholar
  21. 21.
    Sánchez HS, Solís JF (2004) A method to establish the cooling scheme in simulated annealing like algorithms. Lect Notes Comput Sci 3045:755–763CrossRefGoogle Scholar
  22. 22.
    Feitelson DG Parallel Workloads Archive. http://www.cs.huji.ac.il/labs/parallel/workload
  23. 23.
    Yeo CS, Buyya R (2005) Service level agreement based allocation of cluster resources: handling penalty to enhance utility. Paper presented at the IEEE international cluster computing. BurlingtonGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringIsfahan University of TechnologyIsfahanIran
  2. 2.GWDGUniversity of GöttingenGöttingenGermany

Personalised recommendations