Advertisement

Cluster Computing

, Volume 21, Issue 1, pp 163–176 | Cite as

ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) approach in cloud computing

  • Dinesh KomarasamyEmail author
  • Vijayalakshmi Muthuswamy
Article

Abstract

Affordability of appropriate computing resources for satisfying prerequisites of Service Level Agreement (SLA) of clients and optimal utilization of cloud service providers are limited in the present scenario of cloud computing. To overcome these limitations, researchers have exploited various scheduling algorithms to process the deadline based autonomous jobs. The scheduling algorithms however do not support multiprocessor demand and adaptive resource provisioning. This inference triggers to propose a new approach called ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) in cloud computing to handle independent jobs that processes the jobs in a multilevel manner. The SHARP approach embeds multiple criteria decision analysis to preprocess the jobs, multiple attribute job scheduling to prioritize the jobs and adaptive resource provisioning to provide resources dynamically. These contributions alleviate SLA violations in terms of deadline, upgrade client satisfaction and enhance resource utilization. The empirical studies verify the proposed approach in a cloud environment and show the necessity of the proposed approach to support elastic resource provisioning and meet SLA requirements.

Keywords

Cloud computing Job scheduling Backfilling algorithm Elastic resource provisioning Resource utilization 

References

  1. 1.
    Jain, R., Paul, S.: Network virtualization and software defined networking for cloud computing: a survey. IEEE Commun. Mag. 51(11), 24–31 (2013)CrossRefGoogle Scholar
  2. 2.
    Gong, C., Liu, J., Zhang, Q., Chen, H., Gong, Z.: The characteristics of cloud computing. In: 2010 39th International Conference on Parallel Processing Workshops, pp 275–279 (2010)Google Scholar
  3. 3.
    Payberah, A.H., Kavalionak, H., Kumaresan, V., Montresor, A., Haridi, S.: Clive: cloud-assisted p2p live streaming. In: 2012 IEEE 12th International Conference on Peer-to-Peer Computing (P2P), IEEE, pp 79–90 (2012)Google Scholar
  4. 4.
    Li, C., Raghunathan, A., Jha, N.K.: A trusted virtual machine in an untrusted management environment. IEEE Trans. Serv. Comput. 5(4), 472–483 (2012)CrossRefGoogle Scholar
  5. 5.
    Garg, S.K., Yeo, C.S., Anandasivam, A., Buyya, R.: Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. J. Parallel Distrib. Comput. 71(6), 732–749 (2011)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zhou, A., Sun, Q., Sun, L., Li, J., Yang, F.: Maximizing the profits of cloud service providers via dynamic virtual resource renting approach. EURASIP J. Wirel. Commun. Netw. 2015(1), 1–12 (2015)Google Scholar
  7. 7.
    Maguluri, S.T., Srikant, R., Ying, L.: Stochastic models of load balancing and scheduling in cloud computing clusters. In: INFOCOM, 2012 Proceedings IEEE, IEEE, pp 702–710 (2012)Google Scholar
  8. 8.
    Zhao, C., Zhang, S., Liu, Q., Xie, J., Hu, J.: Independent tasks scheduling based on genetic algorithm in cloud computing. In: 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, pp 1–4 (2009)Google Scholar
  9. 9.
    Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)CrossRefGoogle Scholar
  10. 10.
    Rajavel, R., Thangarathanam, M.: Adaptive probabilistic behavioural learning system for the effective behavioural decision in cloud trading negotiation market. Future Gener. Comput. Syst. 58, 29–41 (2016)CrossRefGoogle Scholar
  11. 11.
    Yao, G., Ding, Y., Jin, Y., Hao, K.: Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft Comput., 1–14 (2016). doi: 10.1007/s00500-016-2063-8
  12. 12.
    Komarasamy, D., Muthuswamy, V.: A novel approach for dynamic load balancing with effective bin packing and vm reconfiguration in cloud. Indian J. Sci. Technol. 9(11), 1–6 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhu, J., Li, X.: Scheduling for multi-stage applications with scalable virtual resources in cloud computing. Int. J. Mach. Learn. Cybern., 1–9 (2016). doi: 10.1007/s13042-016-0533-z
  14. 14.
    Wu, F., Wu, Q., Tan, Y., Wang, W.: Unified multi-constraint and multi-objective workflow scheduling for cloud system. In: Algorithms and Architectures for Parallel Processing, pp 635–650. Springer, Berlin (2015)Google Scholar
  15. 15.
    Ye, H.: Research on emergency resource scheduling in smart city based on HPSO algorithm. Int. J. Smart Home 5, 6 (2015)Google Scholar
  16. 16.
    Sheikhalishahi, M., Wallace, R.M., Grandinetti, L., Vazquez-Poletti, J.L., Guerriero, F.: A multi-dimensional job scheduling. Future Gener. Comput. Syst. 54, 123–131 (2016)CrossRefGoogle Scholar
  17. 17.
    Nathani, A., Chaudhary, S., Somani, G.: Policy based resource allocation in IaaS cloud. Future Gener. Comput. Syst. 28(1), 94–103 (2012)CrossRefGoogle Scholar
  18. 18.
    Huang, Y., Bessis, N., Norrington, P., Kuonen, P., Hirsbrunner, B.: Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm. Future Gener. Comput. Syst. 29(1), 402–415 (2013)CrossRefGoogle Scholar
  19. 19.
    Ahmad, A., Arshad, R., Mahmud, S.A., Khan, G.M., Al-Raweshidy, H.S.: Earliest-deadline-based scheduling to reduce urban traffic congestion. IEEE Trans. Intell. Transp. Syst. 15(4), 1510–1526 (2014)CrossRefGoogle Scholar
  20. 20.
    Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Gener. Comput. Syst. 29(4), 973–985 (2013)CrossRefGoogle Scholar
  21. 21.
    Lee, J., Shin, K.G.: Preempt a job or not in EDF scheduling of uniprocessor systems. IEEE Trans. Comput. 63(5), 1197–1206 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Li, J., Luo, Z., Ferry, D., Agrawal, K., Gill, C., Lu, C.: Global edf scheduling for parallel real-time tasks. Real Time Syst. 51(4), 395–439 (2015)CrossRefzbMATHGoogle Scholar
  23. 23.
    Tang, Z., Zhou, J., Li, K., Li, R.: A mapreduce task scheduling algorithm for deadline constraints. Clust. Comput. 16(4), 651–662 (2013)CrossRefGoogle Scholar
  24. 24.
    Abrishami, S., Naghibzadeh, M., Epema, D.: Cost-driven scheduling of grid workflows using partial critical paths. In: 2010 11th IEEE/ACM International Conference on Grid Computing, pp 81–88 (2010)Google Scholar
  25. 25.
    Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener. Comput. Syst. 29(1), 158–169 (2013)CrossRefGoogle Scholar
  26. 26.
    Calheiros, R.N., Buyya, R.: Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans. Parallel Distrib. Syst. 25(7), 1787–1796 (2014)CrossRefGoogle Scholar
  27. 27.
    Komarasamy, D., Muthuswamy, V.: Deadline constrained adaptive multilevel scheduling system in cloud environment. TIIS 9(4), 1302–1320 (2015)Google Scholar
  28. 28.
    Liu, X., Wang, C., Zhou, B.B., Chen, J., Yang, T., Zomaya, A.Y.: Priority-based consolidation of parallel workloads in the cloud. IEEE Trans. Parallel Distrib. Syst. 24(9), 1874–1883 (2013)CrossRefGoogle Scholar
  29. 29.
    Liu, Y., Zhang, C., Li, B., Niu, J.: Dems: a hybrid scheme of task scheduling and load balancing in computing clusters. J. Netw. Comput. Appl. 83, 213–220 (2015)CrossRefGoogle Scholar
  30. 30.
    Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y.: Resource scheduling for infrastructure as a service (iaas) in cloud computing: challenges and opportunities. J. Netw. Comput. Appl. 68, 173–200 (2016)CrossRefGoogle Scholar
  31. 31.
    Arabnejad, H., Barbosa, J.G.: List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25(3), 682–694 (2014)CrossRefGoogle Scholar
  32. 32.
    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
  33. 33.
    Zhang, J., Huang, H., Wang, X.: Resource provision algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 64, 23–42 (2016)CrossRefGoogle Scholar
  34. 34.
    Somasundaram, T.S., Govindarajan, K.: Cloudrb: a framework for scheduling and managing high-performance computing (HPC) applications in science cloud. Future Gener. Comput. Syst. 34, 47–65 (2014)CrossRefGoogle Scholar
  35. 35.
    Krishnamoorthy, N., Asokan, R.: Hybrid adaptive job and resource scoring meta-scheduling system for grid computing. J. Theor. Appl. Inf. Technol. 54(3), 444–452 (2013)Google Scholar
  36. 36.
    Morariu, O., Morariu, C., Borangiu, T.: A genetic algorithm for workload scheduling in cloud based e-learning, In: Proceedings of the 2nd International Workshop on Cloud Computing Platforms, p. 5. ACM, New York (2012)Google Scholar
  37. 37.
    Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., Althebyan, Q.: Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Cluster Comput. 18(2), 919–932 (2015)CrossRefGoogle Scholar
  38. 38.
    Komarasamy, D., Muthuswamy, V.: Associate scheduling of mixed jobs in cloud computing. In: Proceedings of the 3rd International symposium on Big data and Cloud Computing Challenges (ISBCC 16), pp. 133–142. Springer, Cham (2016)Google Scholar
  39. 39.
    Roy, S., Banerjee, S., Chowdhury, K., Biswas, U.: Development and analysis of a three phase cloudlet allocation algorithm. J. King Saud Univ. Comput. Inf. Sci. (2016). doi: 10.1016/j.jksuci.2016.01.003
  40. 40.
    Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: Sla-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45, 108–120 (2014)CrossRefGoogle Scholar
  41. 41.
    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)CrossRefGoogle Scholar
  42. 42.
    Kong, W., Lei, Y., Ma, J.: Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism. Optik Int. J. Light Electron Opt. 127(12), 5099–5104 (2016)CrossRefGoogle Scholar
  43. 43.
    Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C., Wolters, L., Epema, D.H.: The grid workloads archive. Future Gener. Comput. Syst. 24(7), 672–686 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Information Science and Technology, College of EngineeringAnna UniversityChennaiIndia

Personalised recommendations