Skip to main content
SpringerLink
Log in

Towards a systematic approach to the dynamic adaptation of structured parallel computations using model predictive control

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Adaptiveness is an essential feature for distributed parallel applications executed on dynamic environments like Grids and Clouds. Being adaptive means that parallel components can change their configuration at run-time (by modifying their parallelism degree or switching to a different parallel variant) to face irregular workload or to react to uncontrollable changes of the execution platform. A critical problem consists in the definition of adaptation strategies able to select optimal reconfigurations (minimizing operating costs and reconfiguration overhead) and achieve the stability of control decisions (avoiding unnecessary reconfigurations). This paper presents an approach to apply Model Predictive Control (a form of optimal control studied in Control Theory) to adaptive parallel computations expressed according to the Structured Parallel Programming methodology. We show that predictive control is amenable to achieve stability and optimality by relying on the predictability of structured parallelism patterns and the possibility to express analytical cost models of their QoS metrics. The approach has been exemplified on two case-studies, providing a first assessment of its potential and feasibility.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Abdelwahed, S., Kandasamy, N., Neema, S.: Online control for self-management in computing systems. In: Real-Time and Embedded Technology and Applications Symposium, 2004. Proceedings. RTAS 2004. 10th IEEE, pp. 368–375 (2004)

  2. Aldinucci, M., Danelutto, M., Kilpatrick, P.: Towards hierarchical management of autonomic components: a case study. In: 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp. 3–10 (2009). doi:10.1109/PDP.2009.48

  3. Aldinucci, M., Danelutto, M., Teti, P.: An advanced environment supporting structured parallel programming in java. In: Future Generation Computer Systems 19(5), pp. 611–626 (2003). Tools for Program Development and Analysis. Best papers from two Technical Sessions, at ICCS2001, San Francisco, CA, USA, and ICCS2002, Amsterdam, The Netherlands (2003)

  4. Arshad, N., Heimbigner, D., Wolf, A.L.: Deployment and dynamic reconfiguration planning for distributed software systems. Softw. Qual. Control 15(3), 265–281 (2007)

    Article  Google Scholar 

  5. Bacci, B., Danelutto, M., Orlando, S., Pelagatti, S., Vanneschi, M.: P3l: a structured high-level parallel language, and its structured support. Concurrency 7(3), 225–255 (1995)

    Article  Google Scholar 

  6. Bacci, B., Danelutto, M., Pelagatti, S., Vanneschi, M.: A heterogeneous environment for hpc applications. Parallel Comput. 25(13–14), 1827–1852 (1999)

    Article  Google Scholar 

  7. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut. Gener. Comput. Syst. 28(5), 755–768 (2012). Special Section: Energy efficiency in large-scale distributed systems

    Article  Google Scholar 

  8. Bernini, D., Micucci, D., Tisato, F.: Space integration services: a platform for space-aware communication. In: IWCMC ’10: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, pp. 489–493. ACM, New York, NY, USA (2010). http://doi.acm.org/10.1145/1815396.1815510

  9. Bertolli, C., Buono, D., Lametti, S., Mencagli, G., Meneghin, M., Pascucci, A., Vanneschi, M.: A programming model for high-performance adaptive applications on pervasive mobile grids. In: Proceeding of the 21st IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 38–54 (2009)

  10. Bertolli, C., Buono, D., Mencagli, G., Vanneschi, M.: Expressing adaptivity and context-awareness in the assistant programming model. In: Proceedings of the Third International ICST Conference on Autonomic Computing and Communication Systems, pp. 38–54 (2009)

  11. Bertolli, C., Mencagli, G., Vanneschi, M.: Adaptivity in risk and emergency management applications on pervasive grids. In: Proceeding of the 10th International Symposium on Pervasive Systems, Algorithms, and Networks, p. to appear (2009)

  12. Bertolli, C., Mencagli, G., Vanneschi, M.: Analyzing memory requirements for pervasive grid applications. In: Parallel, Distributed, and Network-Based Processing, Euromicro Conference on pp. 297–301 (2010)

  13. Bhavanishankar, R., Subramaniam, C., Kumar, M., Dugar, D.: A context aware approach to emergency management systems. In: IWCMC ’09: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing, pp. 1350–1354. ACM, New York, NY, USA (2009). http://doi.acm.org/10.1145/1582379.1582675

  14. Botorog, G.H., Kuchen, H.: Efficient high-level parallel programming. Theor. Comput. Sci. 196(1G2), 71–107 (1998)

    Article  MATH  Google Scholar 

  15. Charteris, A., Syme, W., Walden, W.: Urban flood modelling and mapping 2d or not 2d. In: Proceedings of the 6th Conference on Hydraulics in Civil Engineering: The State of Hydraulics, pp. 355–363. Barton A.C.T: Institution of Engineers, Barton, Australia (2001)

  16. Chatfield, C., Yar, M.: Holt-Winters forecasting: some practical issues. J. R. Stat. Soc. 37(2), 129–140 (1988)

    Google Scholar 

  17. Cole, M.: Bringing skeletons out of the closet: a pragmatic manifesto for skeletal parallel programming. Parallel Comput. 30(3), 389–406 (2004)

    Article  Google Scholar 

  18. D-ITG: Distributed internet traffic generator. http://www.grid.unina.it/software/ITG/papers.php. Accessed 2 March 2013

  19. Fantacci, R., Vanneschi, M., Bertolli, C., Mencagli, G., Tarchi, D.: Next generation grids and wireless communication networks: towards a novel integrated approach. Wirel. Commun. Mob. Comput. 9(4), 445–467 (2009)

    Article  Google Scholar 

  20. Garcia, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice a survey. Automatica 25, 335–348 (1989)

    Article  MATH  Google Scholar 

  21. Gholami, M., Salahshoor, K., Tabatabaei-pour, M., Shaker, H., Alizadeh, T.: Improved model predictive control of discrete-time hybrid systems with mixed inputs. In: 33rd Annual Conference of the IEEE Industrial Electronics Society, 2007 (IECON 2007), pp. 744–749 (2007)

  22. Gomes, A.T.A., Batista, T.V., Joolia, A., Coulson, G.: Architecting dynamic reconfiguration in dependable systems. Lect. Notes Comput. Sci. 4615, 237–261 (2007)

    Article  Google Scholar 

  23. González-Vélez, H., Leyton, M.: A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers. Softw. Prac. Exp. 40(12), 1135–1160 (2010). doi:10.1002/spe.1026

    Article  Google Scholar 

  24. He, Q., Dovrolis, C., Ammar, M.: On the predictability of large transfer TCP throughput. Comput. Netw. 51, 3959–3977 (2007)

    Article  MATH  Google Scholar 

  25. Heemels, W.P.M.H., Sandee, J.H.: Analysis of event-driven controllers for linear systems. Int. J. Control 81(4), 571–590 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  26. Hellerstein, J.L., Diao, Y., Parekh, S., Tilbury, D.M.: Feedback Control of Computing Systems. Wiley, Hoboken (2004)

    Book  Google Scholar 

  27. Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans. Comput. 56(4), 444–458 (2007). doi:10.1109/TC.2007.1003

    Article  MathSciNet  Google Scholar 

  28. Huebscher, M.C., McCann, J.A.: A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv. 40(3), 1–28 (2008)

    Article  Google Scholar 

  29. Kephart, J., Walsh, W.: An artificial intelligence perspective on autonomic computing policies. In: Policies for Distributed Systems and Networks, 2004. POLICY 2004. Proceedings. Fifth IEEE International Workshop on, pp. 3–12 (2004)

  30. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)

    Article  MathSciNet  Google Scholar 

  31. Kieran, D., Yan, W.: A framework for an event driven video surveillance system. In: Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, pp. 97–102 (2010). doi:10.1109/AVSS.2010.57

  32. Kuchen, H., Ernsting, S.: Data parallel skeletons in java. In: Procedia Computer Science, Proceedings of the International Conference on Computational Science, ICCS 2012 9(0), pp. 1817–1826 (2012)

  33. Kusic, D., Kandasamy, N.: Risk-aware limited lookahead control for dynamic resource provisioning in enterprise computing systems. Clust. Comput. 10(4), 395–408 (2007). doi:10.1007/s10586-007-0022-y

    Article  Google Scholar 

  34. Kusic, D., Kandasamy, N., Jiang, G.: Combined power and performance management of virtualized computing environments serving session-based workloads. Netw. Serv. Manag. IEEE Trans. 8(3), 245–258 (2011). doi:10.1109/TNSM.2011.0726.100045

    Article  Google Scholar 

  35. Legaux, J., Loulergue, F., Jubertie, S.: Osl: An algorithmic skeleton library with exceptions. In: 2013 International Conference on Computational Science, Procedia Computer Science 18(0), 260–269 (2013)

  36. Liu, H., Parashar, M.: Accord: a programming framework for autonomic applications. Syst. Man Cybernet. C 36(3), 341–352 (2006). doi:10.1109/TSMCC.2006.871577

    Article  Google Scholar 

  37. Santambrogio, M.D., Agarwal, A., Leva, A.: Comparison of decision-making strategies for self-optimization in autonomic computing systems. ACM Trans. Autonom. Adapt. Syst. 7(4), 36 (2012). doi:10.1145/2382570.2382572

    Google Scholar 

  38. Maheshwari, N., Nanduri, R., Varma, V.: Dynamic energy efficient data placement and cluster reconfiguration algorithm for mapreduce framework. Fut. Gener. Comput. Syst. 28(1), 119–127 (2012)

    Article  Google Scholar 

  39. Mencagli, G., Vanneschi, M.: QoS-control of structured parallel computations: a predictive control approach. In: Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on, pp. 296–303 (2011). doi:10.1109/CloudCom.47

  40. NCTUNS: Network simulator and emulator. http://nsl.csie.nctu.edu.tw/nctuns.html/. Accessed 8 April 2013

  41. Parashar, M., Liu, H., Li, Z., Matossian, V., Schmidt, C., Zhang, G., Hariri, S.: Automate: enabling autonomic applications on the grid. Clust. Comput. 9, 161–174 (2006). doi:10.1007/s10586-006-7561-5. http://dl.acm.org/citation.cfm?id=1127683.1127688

  42. Park, S.M., Humphrey, M.: Predictable high-performance computing using feedback control and admission control. Parallel Distrib. Syst. IEEE Trans. 22(3), 396–411 (2011). doi:10.1109/TPDS.2010.100

    Article  Google Scholar 

  43. Patikirikorala, T., Colman, A., Han, J., Wang, L.: An evaluation of multi-model self-managing control schemes for adaptive performance management of software systems. J. Syst. Softw. 85(12), 2678–2696 (2012). Self-adaptive systems

    Article  Google Scholar 

  44. Pellegrini, M.C., Riveill, M.: Component management in a dynamic architecture. J. Supercomput. 24(2), 151–159 (2003). doi:10.1023/A:1021798709301

    Article  MATH  Google Scholar 

  45. Potocnik, B., Music, G., Zupancic, B.: Model predictive control systems with discrete inputs. In: Electrotechnical Conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean, 1, pp. 383–386 (2004)

  46. Reiff-Marganiec, S., Turner, K.J.: Feature interaction in policies. Comput. Netw. 45, 569–584 (2004). doi:10.1016/j.comnet.2004.03.004. http://portal.acm.org/citation.cfm?id=1031816.1031818

    Google Scholar 

  47. Rossiter, J.A.: Model-based Predictive Control: A Practical Approach. Control Series. CRC Press, Boca Raton (2003)

    Google Scholar 

  48. Saini, M., Xiangyu, W., Atrey, P., Kankanhalli, M.: Dynamic workload assignment in video surveillance systems. In: Multimedia and Expo (ICME), 2011 IEEE International Conference on, pp. 1–6 (2011). doi:10.1109/ICME.2011.6012076

  49. Sandee, J.H., Heemels, W.P.M.H., Van Den Bosch, P.P.J.: Case studies in event-driven control. In: Proceedings of the 10th International Conference on Hybrid Systems: Computation and Control. HSCC’07, pp. 762–765. Springer-Verlag, Berlin, Heidelberg (2007)

  50. Shi, X., Liu, C., Wu, S., Jin, H., Wu, X., Deng, L.: A cloud service cache system based on memory template of virtual machine. In: Proceedings of the 2011 Sixth Annual ChinaGrid Conference, CHINAGRID ’11, pp. 168–173. IEEE Computer Society, Washington, DC, USA (2011). doi:10.1109/ChinaGrid.2011.20. http://dx.doi.org/10.1109/ChinaGrid.2011.20

  51. Vanneschi, M.: The programming model of assist, an environment for parallel and distributed portable applications. Parallel Comput. 28(12), 1709–1732 (2002)

    Article  MATH  Google Scholar 

  52. Vanneschi, M., Veraldi, L.: Dynamicity in distributed applications: issues, problems and the assist approach. Parallel Comput. 33(12), 822–845 (2007)

    Article  Google Scholar 

  53. Wang, X., Du, Z., Chen, Y., Li, S., Lan, D., Wang, G., Chen, Y.: An autonomic provisioning framework for outsourcing data center based on virtual appliances. Clust. Comput. 11(3), 229–245 (2008). doi:10.1007/s10586-008-0053-z

    Article  Google Scholar 

  54. Warneke, D., Kao, O.: Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. IEEE Trans. Parallel Distrib. Syst. 22(6), 985–997 (2011). doi:10.1109/TPDS.2011.65

    Article  Google Scholar 

  55. Weigold, T., Aldinucci, M., Danelutto, M., Getov, V.: Process-driven biometric identification by means of autonomic grid components. Int. J. Autonom. Adapt. Commun. Syst. 5(3), 274–291 (2012). doi:10.1504/IJAACS.2012.047659

    Google Scholar 

  56. Wernsing, J.R., Stitt, G.: Elastic computing: a framework for transparent, portable, and adaptive multi-core heterogeneous computing. SIGPLAN Not. 45(4), 115–124 (2010)

    Article  Google Scholar 

  57. Wilkinson, B., Allen, M.: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers. Prentice-Hall, Upper Saddle River (1999)

    Google Scholar 

  58. Yaikhom, G., Cole, M., Gilmore, S., Hillston, J.: A structural approach for modelling performance of systems using skeletons. In: Electronic Notes in Theoretical Computer Science 190(3), pp. 167–183. Proceedings of the Fifth Workshop on Quantitative Aspects of Programming Languages (QAPL 2007) (2007)

  59. Yuan, Q., Liu, Z., Peng, J., Wu, X., Li, J., Han, F., Li, Q., Zhang, W., Fan, X., Kong, S.: A leasing instances based billing model for cloud computing. In: Proceedings of the 6th International Conference on Advances in Grid and Pervasive Computing. GPC’11, pp. 33–41. Springer-Verlag, Berlin (2011)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127 , Pisa, Italy

    Gabriele Mencagli & Marco Vanneschi

Authors
  1. Gabriele Mencagli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Vanneschi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriele Mencagli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mencagli, G., Vanneschi, M. Towards a systematic approach to the dynamic adaptation of structured parallel computations using model predictive control. Cluster Comput 17, 1443–1463 (2014). https://doi.org/10.1007/s10586-014-0346-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-014-0346-3

Keywords

Search

Navigation