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An improved load balanced metaheuristic scheduling in cloud

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Abstract

Cloud computing refers to on-demand delivery of service over internet and has application in various domains like media, research, business, bigdata analysis etc. Task scheduling is one of the prime issues in this type of environment. Various metaheuristic algorithms and hard optimization problems have been proposed for solving cloud task scheduling which is a non-deterministic polynomial or an NP. Adaptation of the scheduling strategy to the changes taking place in the environment has to be done by a good scheduler. A proposal for cloud scheduling by means of a balanced load using both firefly algorithm (FA) and particle swarm optimization (PSO) heuristics has been made. The aim is to balance the load of the entire system while at the same time bring down the makespan of a set of tasks. This new strategy for scheduling has been simulated with CloudSim tool kit package. The results of this experiment proved that the proposed FA performed better than min–min scheduling, PSO, and also the first come first serve methods.

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References

  1. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  Google Scholar 

  2. Sajid, M., Raza, Z.: Cloud computing: issues challenges. In: International Conference on Cloud, Big Data and Trust, vol. 20, no. 13, pp. 13–15 (2013)

  3. Kaur, P., Kaur, P.D.: Efficient and enhanced load balancing algorithms in cloud computing. Int. J. Grid Distrib. Comput. 8(2), 9–14 (2015)

    Article  MathSciNet  Google Scholar 

  4. Haryani, N., Jagli, D.: Dynamic method for load balancing in cloud computing. IOSR J. Comput. Eng. 16(4), 23–28 (2014)

    Article  Google Scholar 

  5. Kashyap, D., Viradiya, J.: A survey of various load balancing algorithms in cloud computing. Int. J. Sci. Technol. Res. 3(11), 115–19 (2014)

    Google Scholar 

  6. Saranya, D., Maheswari, L.S.: Load balancing algorithms in cloud computing: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(7), 1107–1111 (2015)

    Google Scholar 

  7. Pattanaik, P.A., Roy, S., Pattnaik, P.K.: Performance study of some dynamic load balancing algorithms in cloud computing environment. In: IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 619–624 (2015)

  8. Xu, G., Pang, J., Fu, X.: A load balancing model based on cloud partitioning for the public cloud. Tsinghua Sci. Technol. 18(1), 34–39 (2013)

    Article  Google Scholar 

  9. Thakur, V., Kumar, S.: A comparison of select load balancing algorithms in cloud computing. IUP J. Comput. Sci. 9(1), 7 (2015)

    Google Scholar 

  10. Ariharan, V., Manakattu, S.S.: Neighbour aware random sampling (NARS) algorithm for load balancing in cloud computing. In: IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–5 (2015)

  11. Pan, J.S., Wang, H., Zhao, H., Tang, L.: Interaction artificial bee colony based load balance method in cloud computing. In: Genetic and Evolutionary Computing, pp. 49–57. Springer, New York (2015)

  12. Grover, J., Katiyar, S.: Agent based dynamic load balancing in Cloud Computing. In: IEEE International Conference on Human Computer Interactions (ICHCI), pp. 1–6 (2013)

  13. Babu, K.R., Samuel, P.: Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud. In: Innovations in Bio-Inspired Computing and Applications, pp. 67–78. Springer, New York (2016)

  14. Joshi, G., Verma, S.K.: Load balancing approach in cloud computing using improvised genetic algorithm: a soft computing approach. Int. J. Comput. Appl. 122(9) (2015)

  15. Cho, K.M., Tsai, P.W., Tsai, C.W., Yang, C.S.: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)

    Article  Google Scholar 

  16. Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In: IEEE Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–7 (2015)

  17. Priyadarsini, R.J., Arockiam, L.: Performance evaluation of min-min and max-min algorithms for job scheduling in federated cloud. Int. J. Comput. Appl. (0975–8887) 99(18), 47–54 (2014)

    Google Scholar 

  18. Kaur, R., Kinger, S.: Analysis of job scheduling algorithms in cloud computing. Int. J. Comput. Trends Technol. 9(7), 379–386 (2014)

    Article  Google Scholar 

  19. Pacini, E., Mateos, C., García Garino, C.: Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron. J. 17(1), 3–3 (2014)

    Article  Google Scholar 

  20. Azir, D.I.E.: Scheduling jobs on cloud computing using firefly algorithm. Doctoral dissertation, University of Science and Technology (2015)

  21. Selvi, V., Umarani, D.R.: Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl. (0975–8887) 5(4) (2010)

  22. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)

    Article  Google Scholar 

  23. Baskaran, M., Sadagopan, C.: Synchronous firefly algorithm for cluster head selection in WSN. Sci. World J. (2015). doi:10.1155/2015/780879

  24. Florence, A.P., Shanthi, V.: A load balancing model using firefly algorithm in cloud computing. J. Comput. Sci. 10(7), 1156 (2014)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Surya Engineering College, Erode, India

    M. Aruna

  2. Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, India

    D. Bhanu

  3. Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India

    S. Karthik

Authors
  1. M. Aruna
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  2. D. Bhanu
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  3. S. Karthik
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Correspondence to M. Aruna.

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Aruna, M., Bhanu, D. & Karthik, S. An improved load balanced metaheuristic scheduling in cloud. Cluster Comput 22 (Suppl 5), 10873–10881 (2019). https://doi.org/10.1007/s10586-017-1213-9

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  • DOI: https://doi.org/10.1007/s10586-017-1213-9

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