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Parallel Late Acceptance Hill-Climbing Algorithm for the Google Machine Reassignment Problem

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9992)


Google Machine Reassignment Problem (GMRP) is an optimisation problem proposed at ROADEF/EURO challenge 2012. The task of GMRP is to allocate cloud computing resources by reassigning a set of services to a set of machines while not violating any constraints. We propose an evolutionary parallel late acceptance hill-climbing algorithm (P-LAHC) for GMRP in this study. The aim is to improve the efficiency of search by escaping local optima. Our P-LAHC method involves multiple search processes. It utilises a population of solutions instead of a single solution. Each solution corresponds to one LAHC process. These processes work in parallel to improve the overall search outcome. These LAHC processes start with different initial individuals and follow distinct search paths. That reduces the chance of falling into a same local optima. In addition, mutation operators will apply when the search becomes stagnated for a certain period of time. This further reduces the chance of being trapped by a local optima. Our results on GMRP instances show that the proposed P-LAHC performed better than single threaded LAHC. Furthermore P-LAHC can outperform or at least be comparable to the state-of-the-art methods from the literature, indicating that P-LAHC is an effective search algorithm.


  • Google Machine Reassignment Problem
  • Optimisation
  • Resource allocation
  • Late Acceptance Hill-Climbing
  • Parallel search
  • Evolutionary algorithms

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Fig. 1.


  1. ROADEF/EURO challenge: Machine reassignment (2011).

  2. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    CrossRef  Google Scholar 

  3. Brandt, F., Speck, J., Völker, M.: Constraint-based large neighborhood search for machine reassignment. Ann. Oper. Res. 242, 1–29 (2012)

    Google Scholar 

  4. Burke, E.K., Bykov, Y.: A late acceptance strategy in hill-climbing for exam timetabling problems. In: PATAT 2008 Conference, Montreal, Canada (2008)

    Google Scholar 

  5. Burke, E.K., Bykov, Y.: The late acceptance hill-climbing heuristic. University of Stirling. Technical report (2012)

    Google Scholar 

  6. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    CrossRef  Google Scholar 

  7. Crainic, T.G., Toulouse, M.: Parallel meta-heuristics. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of metaheuristics, vol. 146, pp. 497–541. Springer, US (2010). doi:10.1007/978-1-4419-1665-5_17

    CrossRef  Google Scholar 

  8. Domínguez, J., Alba, E.: Dealing with hardware heterogeneity: a new parallel search model. Natural Comput. 12(2), 179–193 (2013)

    MathSciNet  CrossRef  Google Scholar 

  9. Fonseca, G.H.G., Santos, H.G., Carrano, E.G.: Late acceptance hill-climbing for high school timetabling. J. Sched. 19, 1–13 (2015)

    MathSciNet  MATH  Google Scholar 

  10. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)

    CrossRef  Google Scholar 

  11. Gavranović, H., Buljubašić, M., Demirović, E.: Variable neighborhood search for Google Machine Reassignment Problem. Electron. Notes Discrete Math. 39, 209–216 (2012)

    CrossRef  Google Scholar 

  12. Goerler, A., Schulte, F., Voß, S.: An application of late acceptance hill-climbing to the traveling purchaser problem. In: Pacino, D., Voß, S., Jensen, R.M. (eds.) ICCL 2013. LNCS, vol. 8197, pp. 173–183. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41019-2_13

    CrossRef  Google Scholar 

  13. Lopes, R., Morais, V.W.C., Noronha, T.F., Souza, V.A.A.: Heuristics and matheuristics for a real-life machine reassignment problem. Int. Trans. Oper. Res. 22(1), 77–95 (2015)

    MathSciNet  CrossRef  Google Scholar 

  14. Manfrin, M., Birattari, M., Stützle, T., Dorigo, M.: Parallel ant colony optimization for the traveling salesman problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 224–234. Springer, Heidelberg (2006). doi:10.1007/11839088_20

    CrossRef  Google Scholar 

  15. Masson, R., Vidal, T., Michallet, J., Penna, P.H.V., Petrucci, V., Subramanian, A., Dubedout, H.: An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems. Expert Syst. Appl. 40(13), 5266–5275 (2013)

    CrossRef  Google Scholar 

  16. Mehta, D., O’Sullivan, B., Simonis, H.: Comparing solution methods for the machine reassignment problem. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 782–797. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33558-7_56

    CrossRef  Google Scholar 

  17. Özcan, E., Bykov, Y., Birben, M., Burke, E.K.: Examination timetabling using late acceptance hyper-heuristics. In: IEEE Congress on Evolutionary Computation, 2009. CEC 2009, pp. 997–1004. IEEE (2009)

    Google Scholar 

  18. Ritt, M.R.P.: An algorithmic study of the machine reassignment problem. PhD thesis, Universidade Federal do Rio Grande do Sul (2012)

    Google Scholar 

  19. Sabar, N.R., Song, A.: Grammatical evolution enhancing simulated annealing for the load balancing problem in cloud computing. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 997–1003. ACM (2016)

    Google Scholar 

  20. Sabar, N.R., Song, A., Zhang, M.: A variable local search based memetic algorithm for the load balancing problem in cloud computing. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016, Part I. LNCS, vol. 9597, pp. 267–282. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31204-0_18

    CrossRef  Google Scholar 

  21. Turky, A., Abdullah, S., McCollum, B., Sabar, N.R: An evolutionary hill climbing algorithm for dynamic optimization problems. In: The 6th Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2013), 27–30 August 2013

    Google Scholar 

  22. Turky, A., Sabar, N.R., Song, A.: An evolutionary simulating annealing algorithm for Google Machine Reassignment Problem. In: The 20th Asia-Pacific Symposium on Intelligent, Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization. Springer Book Series (2016)

    Google Scholar 

  23. Verstichel, J., Berghe, G.V.: A late acceptance algorithm for the lock scheduling problem. In: Voß, S., Pahl, J., Schwarze, S. (eds.) Logistik Management, pp. 457–478. Physica, Heidelberg (2009). doi:10.1007/978-3-7908-2362-2_23

    CrossRef  Google Scholar 

  24. Wang, Z., Lü, Z., Ye, T.: Multi-neighborhood local search optimization for machine reassignment problem. Comput. Oper. Res. 68, 16–29 (2016)

    MathSciNet  CrossRef  Google Scholar 

  25. Yuan, B., Zhang, C., Shao, X.: A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints. J. Intell. Manuf. 26(1), 159–168 (2015)

    CrossRef  Google Scholar 

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Turky, A., Sabar, N.R., Sattar, A., Song, A. (2016). Parallel Late Acceptance Hill-Climbing Algorithm for the Google Machine Reassignment Problem. In: Kang, B., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham.

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