Journal of Grid Computing

, Volume 10, Issue 3, pp 475–499 | Cite as

On the Improvement of Grid Resource Utilization: Preventive and Reactive Rescheduling Approaches

  • Luis Tomás
  • Blanca Caminero
  • Carmen Carrión
  • Agustín C. Caminero


One of the key motivations of computational and data Grids is the ability to make coordinated use of heterogeneous computing resources which are geographically dispersed. However, the provision of Quality of Service (QoS) to Grid users is still a challenge that needs the attention of the research community. Reservation of resources in advance has been proposed as a way of providing QoS guarantees but they may not always be possible. For this reason, this work focuses on meta-scheduling of jobs in advance as a way of enhancing the provision of QoS. Thereby, jobs are scheduled some time before they are actually executed, but no resource is physically reserved. One of the drawbacks of this scenario is that fragmentation may appear in resources (free time slots but not large enough to execute a job) which leads to poor resource utilization. For that reason, two techniques have been developed to tackle poor resource utilization, whose main idea consists of rescheduling already scheduled jobs so that a new incoming job can be allocated. These rescheduling techniques have been implemented within a middleware that supports meta-scheduling in advance, which relies on the GridWay meta-scheduler. Finally, these proposals have been tested using a real testbed involving heterogeneous computing resources distributed across different national organizations, with different experiments showing their efficiency.


Fragmentation Grid meta-scheduling Laxity QoS Replanning capacity Rescheduling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann (2003)Google Scholar
  2. 2.
    Burchard, L.-O., Hovestadt, M., Kao, O., Keller, A., Linnert, B.: The virtual resource manager: an architecture for SLA-aware resource management. In: Proc. of the IEEE International Symposium on Cluster Computing and the Grid (CCGrid) (2004)Google Scholar
  3. 3.
    Tomás, L., Caminero, A.C., Carrión, C., Caminero, B.: Network-aware meta-scheduling in advance with autonomous self-tuning system. Future Gener. Comput. Syst. 27(5), 486–497 (2011)CrossRefGoogle Scholar
  4. 4.
    Elmroth, E., Tordsson, J.: Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions. Future Gener. Comput. Syst. 24(6), 585–593 (2008)CrossRefGoogle Scholar
  5. 5.
    Castillo, C., Rouskas, G.N., Harfoush, K.: On the design of online scheduling algorithms for advance reservations and QoS in Grids. In: Proc. of the Intl. Parallel and Distributed Processing Symposium (IPDPS). Los Alamitos, USA (2007)Google Scholar
  6. 6.
    Gehr, J., Schneider, J.: Measuring fragmentation of two-dimensional resources applied to advance reservation Grid scheduling. In: Proc. of the 9th IEEE/ACM Intl. Symposium on Cluster Computing and the Grid (CCGRID). Shanghai, China (2009)Google Scholar
  7. 7.
    Tomás, L., Caminero, A., Caminero, B., Carrión, C.: A strategy to improve resource utilization in Grids based on network-aware meta-scheduling in advance. In: Proc. of the 12th IEEE/ACM International Conference on Grid Computing (Grid). Lyon, France (2011)Google Scholar
  8. 8.
    Iosup, A., Epema, D.: Grid computing workloads. Internet Computing, IEEE 15(2), 19–26 (2011)CrossRefGoogle Scholar
  9. 9.
    Merlo, A., Clematis, A., Corana, A., Gianuzzi, V.: Quality of service on Grid: architectural and methodological issues. Concurr. Comput.-Pract. Exp. 23, 745–766 (2011)CrossRefGoogle Scholar
  10. 10.
    Roy, A., Sander, V.: Grid Resource Management. Chapter GARA: A Uniform Quality of Service Architecture, pp. 377–394. Kluwer Academic Publishers (2003)Google Scholar
  11. 11.
    Ali, R.A., Rana, O., von Laszewski, G., Hafid, A., Amin, K., Walker, D.: A model for quality-of-service provision in service oriented architectures. IJGUC (2005)Google Scholar
  12. 12.
    Adami, D., et al.: Design and implementation of a Grid network-aware resource broker. In: Proc. of the Intl. Conference on Parallel and Distributed Computing and Networks. Innsbruck, Austria (2006)Google Scholar
  13. 13.
    Xhafa, F., Abraham, A.: Computational models and heuristic methods for Grid scheduling problems. Future Gener. Comput. Syst. 26(4), 608–621 (2010)CrossRefGoogle Scholar
  14. 14.
    Guan, D., Cai, Z., Kong, Z.: Provision and analysis of QoS for distributed Grid applications. In: Proc. of the 5th Intl. Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 4191–4194 (2009)Google Scholar
  15. 15.
    Chu, H., Nahrstedt, K.: CPU service classes for multimedia applications. In: Proc. of Intl. Conference on Multimedia Computing and Systems (ICMCS). Florence, Italy (1999)Google Scholar
  16. 16.
    Mateescu, G.: Extending the portable batch system with preemptive job scheduling. In: SC2000: High Performance Networking and Computing. Dallas, USA (2000)Google Scholar
  17. 17.
    Cárdenas, C., Gagnaire, M.: Evaluation of flow-aware networking (FAN) architectures under GridFTP traffic. Future Gener. Comput. Syst. 25(8), 895–903 (2009)CrossRefGoogle Scholar
  18. 18.
    Kurowski, K., Ludwiczak, B., Nabezyski, J., Oleksiak, A., Pukacki, J.: Dynamic Grid scheduling with job migration and rescheduling in the GridLab resource management system. Sci. Program. 12(4), 263–273 (2004)Google Scholar
  19. 19.
    Wei, X., Ding, Z., Yuan, S., Hou, C., Li, H.: CSF4: a WSRF compliant meta-scheduler. In: Proc. of the Intl. Conference on Grid Computing & Applications (GCA). Las Vegas, USA (2006)Google Scholar
  20. 20.
    Waldrich, O., Wieder, Ph., Ziegler, W.: A meta-scheduling service for co-allocating arbitrary types of resources. In: Proc. of the 6th Intl. Conference on Parallel Processing and Applied Mathematics (PPAM). Poznan, Poland (2005)Google Scholar
  21. 21.
    Venugopal, S., Buyya, R., Winton, L.J.: A Grid service broker for scheduling e-Science applications on global data Grids. Concurr. Comput.-Pract. Exp. 18(6), 685–699 (2006)CrossRefGoogle Scholar
  22. 22.
    Caminero, A., Rana, O., Caminero, B., Carrión, C.: Performance evaluation of an autonomic network-aware metascheduler for Grids. Concurr. Comput.-Pract. Exp. 21(13), 1692–1708 (2009)CrossRefGoogle Scholar
  23. 23.
    Huedo, E., Montero, R.S., Llorente, I.M.: A modular meta-scheduling architecture for interfacing with pre-WS and WS Grid resource management services. Future Gener. Comput. Syst. 23(2), 252–261 (2007)CrossRefGoogle Scholar
  24. 24.
    Siddiqui, M., Villazón, A., Fahringer, T.: Grid capacity planning with negotiation-based advance reservation for optimized QoS. In: Proc. of the 2006 Conference on Supercomputing (SC ’06). Tampa, USA (2006)Google Scholar
  25. 25.
    Singh, G., Kesselman, C., Deelman, E.: A provisioning model and its comparison with best-effort for performance-cost optimization in Grids. In: Proc. of the 16th Intl. symposium on High Performance Distributed Computing (HPDC). Monterey, USA (2007)Google Scholar
  26. 26.
    Figuerola, S., Ciulli, N., de Leenheer, M., Demchenko, Y., Ziegler, W., Binczewski, A.: Phosphorus: singlestep on-demand services across multi-domain networks for e-science. Proc. SPIE 6784, 67842X (2007) doi: 10.1117/12.746371 CrossRefGoogle Scholar
  27. 27.
    Dobber, M., van der Mei, R., Koole, G.: A prediction method for job runtimes on shared processors: survey, statistical analysis and new avenues. Perform. Eval. 64(7–8), 755–781 (2007)CrossRefGoogle Scholar
  28. 28.
    Dinda, P.A.: The statistical properties of host load. Sci. Program. 7(3–4), 211–229 (1999)Google Scholar
  29. 29.
    Jin, H., Shi, X., Qiang, W., Zou, D.: An adaptive meta-scheduler for data-intensive applications. IJGUC 1(1), 32–37 (2005)CrossRefGoogle Scholar
  30. 30.
    Tomás, L., Caminero, A., Carrión, C., Caminero, B.: Exponential smoothing for network-aware meta-scheduler in advance in Grids. In: Proc. of the 6th Intl. Workshop on Scheduling and Resource Management on Parallel and Distributed Systems (SRMPDS), Conjunction with the 39th Intl. Conference on Parellel Processing (ICPP). San Diego, USA (2010)Google Scholar
  31. 31.
    Kalekar, P.S.: Time series forecasting using holt-winters exponential smoothing. Technical Report, Kanwal Rekhi School of Information Technology (2004)Google Scholar
  32. 32.
    Tomás, L., Caminero, A., Caminero, B., Carrión, C.: Using network information to perform meta-scheduling in advance in Grids. In: Proc. of the Sixteenth International Conference on Parallel Computing (Euro-Par). Ischia, Italy (2010)Google Scholar
  33. 33.
    Smith, W., Foster, I., Taylor, V.: Scheduling with advanced reservations. In: Proc. of the 14th Intl. Parallel and Distributed Processing Symposium (IPDPS). Washington, USA (2000)Google Scholar
  34. 34.
    Wilson, P.R., Johnstone, M.S., Neely, M., Boles, D.: Dynamic storage allocation: a survey and critical review. In: Proc. of the Intl. Workshop on Memory Managment (IWMM), pp. 1–116. Kinross, UK (1995)Google Scholar
  35. 35.
    De Assunç ao, M.D., Buyya, R.: Performance analysis of multiple site resource provisioning: effects of the precision of availability information. In: Proc. of the 15th Intl. Conference on High Performance Computing (HiPC). Bangalore, India (2008)Google Scholar
  36. 36.
    Elmroth, E., Tordsson, J.: A standards-based Grid resource brokering service supporting advance reservations, coallocation and cross-Grid interoperability. Concurr. Comput.-Pract. Exp. 21(18), 2298–2335 (2009)CrossRefGoogle Scholar
  37. 37.
    Caniou, Y., Charrier, G., Desprez, F.: Analysis of tasks reallocation in a dedicated Grid environment. In: Proc. of the Intl. Conference on Cluster Computing (CLUSTER). Heraklion, Greece (2010)Google Scholar
  38. 38.
    Cooper, K., Dasgupta, A., Kennedy, K., Koelbel, C., Mandal, A., Marin, G., Mazina, M., Mellor-Crummey, J., Berman, F., Casanova, H., Chien, A., Dail, H., Liu, X., Olugbile, A., Sievert, O., Xia, H., Johnsson, L., Liu, B., Patel, M., Reed, D., Deng, W., Mendes, C., Shi, Z., YarKhan, A., Dongarra, J.: New Grid scheduling and rescheduling methods in the grads project. In: Proc. of the 18th Intl. Parallel and Distributed Processing Symposium. Santa Fe, USA (2004)Google Scholar
  39. 39.
    Yu, Z., Shi, W.: An adaptive rescheduling strategy for Grid workflow applications. In: Proc. of the 21th Intl. Parallel and Distributed Processing Symposium. Long Beach, USA (2007)Google Scholar
  40. 40.
    Liu, X., Chen, J., Wu, Z., Ni, Z., Yuan, D., Yang, Y.: Handling recoverable temporal violations in scientific workflow systems: a workflow rescheduling based strategy. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. Melbourne, Australia (2010)Google Scholar
  41. 41.
    Huedo, E., Montero, R.S., Llorente, I.M.: A modular meta-scheduling architecture for interfacing with pre-WS and WS Grid resource management services. Future Gener. Comput. Syst. 23(2), 252–261 (2007)CrossRefGoogle Scholar
  42. 42.
    Caminero, A., Rana, O., Caminero, B., Carrión, C.: Network-aware heuristics for inter-domain meta-scheduling in Grids. J. Comput. Syst. Sci. 77(2), 262–281 (2011)CrossRefGoogle Scholar
  43. 43.
    de Assunç ao, M.D., Buyya, R., Venugopal, S.: InterGrid: a case for internetworking islands of Grids. Concurr. Comput.-Pract. Exp. 20(8), 997–1024 (2008)CrossRefGoogle Scholar
  44. 44.
    Di, A., Stefano, Morana, G., Zito, D.: A P2P strategy for QoS discovery and SLA negotiation in Grid environment. Future Gener. Comput. Syst. 25(8), 862–875 (2009)CrossRefGoogle Scholar
  45. 45.
    Conejero, J., Tomás, L., Carrión, C., Caminero, B.: QoS Provisioning with meta-scheduling in advance within SLA-based Grid environments. Comput. Inform. 31, 73–88 (2012)Google Scholar
  46. 46.
    Bank, J., and Werner, F.: Heuristic algorithms for unrelated parallel machine scheduling with a common due date, release dates, and linear earliness and tardiness penalties. Math. Comput. Model. 33(4–5), 363–383 (2001)MathSciNetMATHCrossRefGoogle Scholar
  47. 47.
    Farooq, U., Majumdar, S., Parsons, E.W.: Efficiently scheduling advance reservations in Grids. Technical Report, Carleton University, Department of Systems and Computer Engineering (2005)Google Scholar
  48. 48.
    Portable Batch System. Web page at Date of last access: 20 Jan 2011
  49. 49.
    Chun, G., Dail, H., Casanova, H., Snavely, A.: Benchmark probes for Grid assessment. In: Proc. of 18th Intl. Parallel and Distributed Processing Symposium (IPDPS). Santa Fe, New Mexico (2004)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Luis Tomás
    • 1
  • Blanca Caminero
    • 1
  • Carmen Carrión
    • 1
  • Agustín C. Caminero
    • 2
  1. 1.Department of Computing SystemsUniversity of Castilla–La ManchaAlbaceteSpain
  2. 2.Department of Communication and Control SystemsNational University of Distance EducationAlbaceteSpain

Personalised recommendations