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On scheduling transaction in grid computing using cuckoo search-ant colony optimization considering load

  • Dharmendra Prasad MahatoEmail author
  • Jasminder Kaur Sandhu
  • Nagendra Pratap Singh
  • Vishal Kaushal
Article

Abstract

Scheduling of transactions in the grid computing system is known to be an NP-hard problem. In order to solve this problem, this paper introduces a hybrid approach named cuckoo search-ant colony optimization. The approach is to dynamically generate an optimal schedule by clustering the resources considering their load so as to complete the transactions within their deadlines as well as utilizing the resources in an efficient way. The approach also balances the load of the system before scheduling the transactions. We use cuckoo search method for making clusters of resources based on their load. We use ant colony optimization for selecting the appropriate and optimal resources. We evaluate the performance of the proposed algorithm with six existing algorithms. The results illustrate that an important advantage of the cuckoo search-ant colony optimization algorithm is its speed of clustering and ability to obtain faster and feasible load balanced schedules.

Keywords

Transaction scheduling Grid computing system Ant colony optimization Cuckoo search 

Notes

References

  1. 1.
    Abdullahi, M., Ngadi, M.A., et al.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016)CrossRefGoogle Scholar
  2. 2.
    Abraham, A., Buyya, R., Nath, B.: Nature’s heuristics for scheduling jobs on computational grids. In: Proceedings of the 8th IEEE International Conference on Advanced Computing and Communications. ADCOM 2000, pp. 45–52 (2000)Google Scholar
  3. 3.
    Amiri, E., Mahmoudi, S.: Efficient protocol for data clustering by fuzzy cuckoo optimization algorithm. Appl. Soft Comput. 41, 15–21 (2016)CrossRefGoogle Scholar
  4. 4.
    Anand, L., Ghose, D., Mani, V.: Elisa: an estimated load information scheduling algorithm for distributed computing systems. Comput. Math. Appl. 37(8), 57–85 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Babukartik, R.G., Dhavachelvan, P.: Hybrid algorithm using the advantage of aco and cuckoo search for job scheduling. Int. J. Inf. Technol. Converg. Serv. 2(4), 25 (2012)Google Scholar
  6. 6.
    Bertsekas, D.P., Gallager, R.G., Humblet, P.: Data Networks, vol. 2. Prentice-Hall International, New Jersey (1992)zbMATHGoogle Scholar
  7. 7.
    Casas, I., Taheri, J., Ranjan, R., Wang, L., Zomaya, A.Y.: A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems. Future Gener. Comput. Syst. 74, 168–178 (2016)CrossRefGoogle Scholar
  8. 8.
    Chang, R.-S., Chang, J.-S., Lin, P.-S.: An ant algorithm for balanced job scheduling in grids. Future Gener. Comput. Syst. 25(1), 20–27 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chang, R.-S., Lin, C.-F., Chen, J.-J.: Selecting the most fitting resource for task execution. Future Gener. Comput. Syst. 27(2), 227–231 (2011)CrossRefGoogle Scholar
  10. 10.
    Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Proceedings of the 1st European Conference on Artificial Life, vol. 142, pp. 134–142. Paris, France (1991)Google Scholar
  11. 11.
    Cortés, A., Ripoll, A., Senar, M.A., Luque, E.: Dynamic loadbalancing strategy for scalable parallel systems. In: Joubert, G.R., Trottenberg, E.H., D’Hollander, F.J., Peters, F., Völpel, R. (eds.) Parallel Computing Fundamentals, Applications and NewDirections, volume 12 of Advances in Parallel Computing. Elsevier, North-Holland (1998)Google Scholar
  12. 12.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs, pp. 180–191. Springer, Berlin (2010)Google Scholar
  13. 13.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Extremal optimization applied to load balancing in execution of distributed programs. Appl. Soft Comput. 30, 501–513 (2015)CrossRefGoogle Scholar
  14. 14.
    Ding, Y., Qin, X., Liu, L., Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 50, 62–74 (2015)CrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Birattari, M.: Ant Colony Optimization. In Encyclopedia of Machine Learning, pp. 36–39. Springer, New York (2010)Google Scholar
  16. 16.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2–3), 243–278 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Dorigo, M., Stützle, T.: The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. Handbook of Metaheuristics, pp. 250–285. Springer, New York (2003)zbMATHGoogle Scholar
  18. 18.
    Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. Comput. Intell. Mag. IEEE 1(4), 28–39 (2006)CrossRefGoogle Scholar
  19. 19.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  20. 20.
    Garg, R., Singh, A.K.: Adaptive workflow scheduling in grid computing based on dynamic resource availability. Eng. Sci. Technol. Int. J. 18(2), 256–269 (2015)CrossRefGoogle Scholar
  21. 21.
    Guo, S., Huang, H.-Z., Wang, Z., Xie, M.: Grid service reliability modeling and optimal task scheduling considering fault recovery. Reliab. IEEE Trans. 60(1), 263–274 (2011)CrossRefGoogle Scholar
  22. 22.
    Haque, W., Toms, A., Germuth, A.: Dynamic load balancing in real-time distributed transaction processing. In: Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE), pp. 268–274. IEEE (2013)Google Scholar
  23. 23.
    Hussain, H., Malik, S.U.R., Hameed, A., Khan, S.U., Bickler, G., Min-Allah, N., Qureshi, M.B., Zhang, L., Yongji, W., Ghani, N., Ghani, N., et al.: A survey on resource allocation in high performance distributed computing systems. Parallel Comput. 39(11), 709–736 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of grid resource management systems for distributed computing. Softw. Pract. Exp. 32(2), 135–64 (2002)zbMATHCrossRefGoogle Scholar
  25. 25.
    Laskowski, E., Tudruj, M., De Falco, I., Scafuri, U., Tarantino, E., Olejnik, R.: Extremal Optimization Applied to Task Scheduling of Distributed Java Programs, pp. 61–70. Springer, Berlin (2011)Google Scholar
  26. 26.
    Lee, Y.-H., Leu, S., Chang, R.-S.: Improving job scheduling algorithms in a grid environment. Future Gener. Comput. Syst. 27(8), 991–998 (2011)CrossRefGoogle Scholar
  27. 27.
    Li, K.: Optimal load distribution in nondedicated heterogeneous cluster and grid computing environments. J. Syst. Archit. 54(1–2), 111–123 (2008)CrossRefGoogle Scholar
  28. 28.
    Li, Y., Yang, Y., Ma, M., Zhou, L.: A hybrid load balancing strategy of sequential tasks for grid computing environments. Future Gener. Comput. Syst. 25(8), 819–828 (2009)CrossRefGoogle Scholar
  29. 29.
    Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: Proceedings of the 2011 6th Annual China grid Conference (ChinaGrid), pp. 3–9. IEEE (2011)Google Scholar
  30. 30.
    Liu, H., Abraham, A., Hassanien, A.E.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener. Comput. Syst. 26(8), 1336–1343 (2010)CrossRefGoogle Scholar
  31. 31.
    Lu, K., Subrata, R., Zomaya, A.Y.: On the performance-driven load distribution for heterogeneous computational grids. J. Comput. Syst. Sci. 73(8), 1191–1206 (2007)zbMATHCrossRefGoogle Scholar
  32. 32.
    Ludwig, S.A., Moallem, A.: Swarm intelligence approaches for grid load balancing. J. Grid Comput. 9(3), 279–301 (2011)CrossRefGoogle Scholar
  33. 33.
    Mahato, D.P.: CPNS based reliability modeling for on-demand computing based transaction processing. In: Proceedings of the 47th International Conference on Parallel Processing Companion, pp. 24. ACM (2018)Google Scholar
  34. 34.
    Mahato, D.P.: Cuckoo search-ant colony optimization based scheduling in grid computing. In: Proceedings of the 47th International Conference on Parallel Processing Companion, pp. 39. ACM (2018)Google Scholar
  35. 35.
    Mahato, D.P.: Load balanced transaction scheduling in on-demand computing using cuckoo search-ant colony optimization. In: Proceedings of the 20th International Conference on Distributed Computing and Networking, pp. 439–444. ACM (2019)Google Scholar
  36. 36.
    Mahato, D.P., Sandhu, J.K.: Modeling of load balanced scheduling and reliability evaluation for on-demand computing based transaction processing system. In: 2018 IEEE 14th International Conference on e-Science (e-Science), pp. 390–391. IEEE (2018)Google Scholar
  37. 37.
    Mahato, D.P., Singh, R.S.: Empirical reliability modeling of transaction oriented autonomic grid service. In: Recent Advances in Mathematics, Statistics and Computer Science, pp. 528–537. World Scientific (2016)Google Scholar
  38. 38.
    Mahato, D.P., Singh, R.S.: Balanced task allocation in the on-demand computing-based transaction processing system using social spider optimization. Concurr. Comput. 29(18), e4214 (2017)CrossRefGoogle Scholar
  39. 39.
    Mahato, D.P., Singh, R.S.: Load balanced transaction scheduling using honey bee optimization considering performability in on-demand computing system. Concurr. Comput. 29(21), e4253 (2017)CrossRefGoogle Scholar
  40. 40.
    Mahato, D.P., Singh, R.S.: Maximizing availability for task scheduling in on-demand computing-based transaction processing system using ant colony optimization. Concurr. Comput. 30(11), e4405 (2018)CrossRefGoogle Scholar
  41. 41.
    Mahato, D.P., Singh, R.S.: Reliability modeling and analysis for deadline-constrained grid service. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 75–81. IEEE (2018)Google Scholar
  42. 42.
    Mahato, D.P., Umrao, L.S., Lokendra, S., Singh, R.S.: Recovery of failures in transaction oriented composite grid service. IJCA Proc. Comput. Commun. Sensor Netw. 2013, 38–42 (2013)Google Scholar
  43. 43.
    Mahato, D.P., Umrao, L.S., Singh, R.S.: Adaptability in transaction oriented grid service. In: 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 239–244. IEEE (2014)Google Scholar
  44. 44.
    Mahato, D.P., Maurya, A.K., Tripathi, A.K., Singh, R.S.: Dynamic and adaptive load balancing in transaction oriented grid service. In: Proceedings of the 2016 2nd International Conference on Green High Performance Computing (ICGHPC), pp. 1–5. IEEE (2016)Google Scholar
  45. 45.
    Mahato, D.P., Singh, R.S., Tripathi, A.K., Maurya, A.K.: On scheduling transactions in a grid processing system considering load through ant colony optimization. Appl. Soft Comput. 61, 875–891 (2017)CrossRefGoogle Scholar
  46. 46.
    Menouer, T., Cerin, C., Saad, W., Shi, X.: A resource allocation framework with qualitative and quantitative sla classes. In: European Conference on Parallel Processing, pp. 69–81. Springer, Cham (2018)Google Scholar
  47. 47.
    Prakash, S., Vidyarthi, D.P.: Maximizing availability for task scheduling in computational grid using genetic algorithm. Concurr. Comput. 27(1), 193–210 (2015)CrossRefGoogle Scholar
  48. 48.
    Reda, N.M., Tawfik, A., Marzok, M.A., Khamis, S.M.: Sort-mid tasks scheduling algorithm in grid computing. J. Adv. Res. 6(6), 987–993 (2015)CrossRefGoogle Scholar
  49. 49.
    Saha, S., Pal, S., Pattnaik, P.K.: A Novel Scheduling Algorithm for Cloud Computing Environment. Computational Intelligence in Data Mining, pp. 387–398. Springer, New Delhi (2016)Google Scholar
  50. 50.
    Silberschatz, A., Galvin, P.B., Gagne, G., Silberschatz, A.: Operating System Concepts, vol. 4. Addison-Wesley, Reading (1998)zbMATHGoogle Scholar
  51. 51.
    Sim, K.M., Sun, W.H.: Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans. Syst. Man Cybern. A 33(5), 560–572 (2003)CrossRefGoogle Scholar
  52. 52.
    Sorenson, M.D., Klitz, K., Payne, R.B., Megahan, J.: The Cuckoos. Oxford University Press, New York (2005)Google Scholar
  53. 53.
    Stützle, T., Dorigo, M.: ACO Algorithms for the Traveling Salesman Problem. Evolutionary Algorithms in Engineering and Computer Science, pp. 163–183. Wiley, Hoboken (1999)zbMATHGoogle Scholar
  54. 54.
    Suresh, S., Huang, H., Kim, H.J.: Hybrid real-coded genetic algorithm for data partitioning in multi-round load distribution and scheduling in heterogeneous systems. Appl. Soft Comput. 24, 500–510 (2014)CrossRefGoogle Scholar
  55. 55.
    Tang, F., Li, M., Huang, J.Z.: Real-time transaction processing for autonomic grid applications. Eng. Appl. Artif. Intell. 17(7), 799–807 (2004)Google Scholar
  56. 56.
    Tang, F.-L., Li, M.-L., Huang, Z.-X., Wang, C.-L.: Transaction service for service grid and its correctness analysis based on petri net. Jisuanji Xuebao/Chin. J. Comput. 28(4), 667–676 (2005)Google Scholar
  57. 57.
    Tang, F., Guo, M., Li, M., Li, L.: Transaction management for reliable grid applications. In: Proceedings of the International Conference on Advanced Information Networking and Applications, 2009, pp. 427–434. AINA ’09 (2009)Google Scholar
  58. 58.
    Türker, C., Haller, K., Schuler, C., Schek, H.: How can we support grid transactions? towards peer-to-peer transaction processing. In: CIDR, pp. 174–185. Citeseer (2005)Google Scholar
  59. 59.
    Wang, T., Vonk, J., Kratz, B., Grefen, P.: A survey on the history of transaction management: from flat to grid transactions. Distrib. Parallel Databases 23(3), 235–270 (2008)CrossRefGoogle Scholar
  60. 60.
    Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Gener. Comput. Syst. 26(4), 608–621 (2010)CrossRefGoogle Scholar
  61. 61.
    Yang, X.-S., Deb, S.: Cuckoo search via lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp. 210–214. IEEE (2009)Google Scholar
  62. 62.
    Yu, B., Yang, Z.Z., Xie, J.X.: A parallel improved ant colony optimization for multi-depot vehicle routing problem. J. Oper. Res. Soc. 62(1), 183–188 (2011)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology HamirpurHamirpurIndia
  2. 2.Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpuraIndia

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