Skip to main content
SpringerLink
Log in

A novel water pressure change optimization technique for solving scheduling problem in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Presently, cloud computing has become a very popular platform in the computing world. It provides users with high efficient resources on demand. Nevertheless, due to the strong turnout to use the cloud in everything in the Information Technology (IT) field, cloud platforms have become very crowded with heavy loads. Therefore, providers need to use new techniques to manage resources allocation to users. One of the most important techniques, in managing cloud resources, is scheduling technique. Recently, many heuristic and meta-heuristic techniques are developed to solve the scheduling problem. However, each technique is efficient to solve a part of the problem but unable to solve the overall problem. This paper presents a new technique called Water Pressure Change Optimization (WPCO) to solve the scheduling problem in cloud computing. The new WPCO technique is inspired from the phenomenon of water density changing when increasing the pressure due to the changing in the physical characteristics of the water. The new technique is evaluated and compared with the most recent existing techniques. The results indicate that the WPCO can distribute any number of tasks on the available resources in low time complexity. In addition, it improves schedule length, load balancing, resources utilization, memory usage and throughput of the cloud system.

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
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Manvi, S.S., Shyam, G.K.: Resource management for infrastructure as a service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  3. Bansal, N., Singh, A.K.: Trust for task scheduling in cloud computing unfolds it through fruit congenial. Networking Communication and Data Knowledge Engineering, pp. 41–48. Springer, New York (2018)

    Chapter  Google Scholar 

  4. Pham, V.V.H., Liu, X., Zheng, X., Fu, M., Deshpande, S.V., Xia, W., Zhou, R., and Abdelrazek, M.: PaaS-black or white: an investigation into software development model for building retail industry SaaS. In: Proceedings of the IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), pp. 285–287 (2017)

  5. Tripathy, L., Patra, R.R.: Scheduling in cloud computing. Int. J. Cloud Comput.: Serv. Arch. 4(5), 21–27 (2014)

    Google Scholar 

  6. Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Gener. Comput. Syst. 78, 257–271 (2018)

    Article  Google Scholar 

  7. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inform. J. 16(3), 275–295 (2015)

    Article  Google Scholar 

  8. Feitelson, D.G., Tsafrir, D., Krakov, D.: Experience with using the parallel workloads archive. J. Parallel Distrib. Comput. 74(10), 2967–2982 (2014)

    Article  Google Scholar 

  9. http://www.cs.huji.ac.il/labs/parallel/workload/l_nasa_ipsc/index.html#usage

  10. Shoman, M.A., Attiya, G.M., Morsi, I.Z.: A modified genetic algorithm for load balancing in heterogeneous distributed computing systems. Menoufia J. Electron. Eng. Res. 21(1), 1–18 (2011)

    Google Scholar 

  11. Bellman, R., Esogbue, A.O., Nabeshima, I.: Mathematical aspects of scheduling and applications. In: Modern Applied Mathematics and Computer Science, vol. 4 (2014)

  12. Attiya, G., Hamam, Y.: Task allocation for maximizing reliability of distributed systems: a simulated annealing approach. J. Parallel Distrib. Comput. 66(10), 1259–1266 (2006)

    Article  MATH  Google Scholar 

  13. Lam, A.Y.S., Li, V.O.K.: Chemical reaction optimization: a tutorial. Memet. Comput. 4(1), 3 (2012)

    Article  Google Scholar 

  14. Mahmoodi, F., Dooley, K.: A comparison of exhaustive and non-exhaustive group scheduling heuristics in a manufacturing cell. Int. J. Prod. Res. 29(9), 1923–1939 (1991)

    Article  MATH  Google Scholar 

  15. Toumi, S., Jarboui, B., Eddaly, M., Rebaï, A.: Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion. Int. J. Math. Oper. Res. 10(1), 34–48 (2017)

    Article  MathSciNet  Google Scholar 

  16. Lin, Y.-K., Chong, C.S.: Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J. Intell. Manuf. 28(5), 1189–1201 (2017)

    Article  MathSciNet  Google Scholar 

  17. Saidi-Mehrabad, M., Bairamzadeh, S.: Design of a hybrid genetic algorithm for parallel machines scheduling to minimize job tardiness and machine deteriorating costs with deteriorating jobs in a batched delivery system. J. Optim. Ind. Eng. 11(1), 35–50 (2018)

    Google Scholar 

  18. Wang, T., Liu, Z., Chen, Y., Xu, Y., and Dai, X.: Load balancing task scheduling based on genetic algorithm in cloud computing. In: Proceedings of the IEEE 12th International Conference Dependable, Autonomic and Secure Computing (DASC), pp. 146–152 (2014)

  19. da Silva, A.S., Moshi, E., Ma, H., and Hartmann, S.: A QoS-aware web service composition approach based on genetic programming and graph databases. In: International Conference on Database and Expert Systems Applications, pp. 37–44. Springer, Cham (2017)

  20. Kołodziej, J., Khan, S.U., Wang, L., Zomaya, A.Y.: Energy efficient genetic based schedulers in computational grids. Concurr. Comput.: Pract. Exp. 27(4), 809–829 (2015)

    Article  Google Scholar 

  21. Shivasankaran, N., Kumar, P.S., Raja, K.V.: Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. Int. J. Comput. Intell. Syst. 8(3), 455–466 (2015)

    Article  Google Scholar 

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

  23. Zhang, L., Cai, L., Li, M., Wang, F.: A method for least-cost QoS multicast routing based on genetic simulated annealing algorithm. Comput. Commun. 32(1), 105–110 (2009)

    Article  Google Scholar 

  24. Samora, I., Franca, M.J., Schleiss, A.J., Ramos, H.M.: Simulated annealing in optimization of energy production in a water supply network. Water Resour. Manage 30(4), 1533–1547 (2016)

    Article  Google Scholar 

  25. Achary, R.V., Raj, P., and Nagarajan, S.: Dynamic job scheduling using ant colony optimization for mobile cloud computing. In: Intelligent Distributed Computing, pp. 71–82. Springer, Cham (2015)

  26. Tawfeek, M.A., El-Sisi, A., Keshk, A.E., and Torkey, F.A.: Cloud task scheduling based on ant colony optimization. In: Proceedings of the 8th International Conference in Computer Engineering and Systems (ICCES), pp. 64–69 (2013)

  27. Dam, S., Mandal, G., Dasgupta, K., and Dutta, P.: An ant-colony-based meta-heuristic approach for load balancing in cloud computing. In: Appl. Comput. Intell. Soft Comput. Eng., vol. 204 (2017)

  28. Dai, Y., Lou, Y., Xin, L.: A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. Intell. Hum.-Mach. Syst. and Cybern. 2, 428–431 (2015)

    Google Scholar 

  29. Azad, P., Navimipour, N.J.: An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. Int. J. Cloud Appl. Comput. 7(4), 20–40 (2017)

    Google Scholar 

  30. Xu, Y., Li, K., He, L., Zhang, L., Li, K.: A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 12, 3208–3222 (2015)

    Article  Google Scholar 

  31. Bhattacharjee, K., Bhattacharya, A., nee Dey, S.H.: Real coded chemical reaction based optimization for short-term hydrothermal scheduling. Appl. Soft Comput. 24, 962–976 (2014)

    Article  Google Scholar 

  32. Wu, L., Wang, Y.J., Yan, C.K.: Performance comparison of energy-aware task scheduling with GA and CRO algorithms in cloud environment. Appl. Mech. Mater. Trans. Tech. Publ. 596, 204–208 (2014)

    Article  Google Scholar 

  33. Kumar, A.M.S., Venkatesan, M.: Task scheduling in a cloud computing environment using HGPSO algorithm. Clust. Comput. (2018). https://doi.org/10.1007/s10586-018-2515-2

    Article  Google Scholar 

  34. Zhang, R., Tian, F., Ren, X., Chen, Y., Chao, K., Zhao, R., Dong, B., Wang, W.: Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment. Serv. Oriented Comput. Appl. (2018). https://doi.org/10.1007/s11761-018-0231-72018

    Article  Google Scholar 

  35. Bhushan, S.B., Reddy, P.C.H.: A hybrid meta-heuristic approach for QoS-aware cloud service composition. Int. J. Web Serv. Res. 15(2), 1–20 (2018)

    Article  Google Scholar 

  36. Xiong, A., Ch, Xu: Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Math. Probl. Eng. (2014). https://doi.org/10.1155/2014/816518

    Article  Google Scholar 

  37. El-Attar, N.: Prediction Resources Scheduling in Cloud Computing Systems, p. 136. LAP LAMBERT Academic Publishing, Saarbrücken (2016)

    Google Scholar 

  38. Akbari, M., Rashidi, H., Alizadeh, S.H.: An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems. Eng. Appl. Artif. Intell. 61, 35–46 (2017)

    Article  Google Scholar 

  39. Henderson, D., Jacobson, S.H., Johnson, A.W.: The theory and practice of simulated annealing. Handbook of Metaheuristics, pp. 287–319. Springer, New York (2006)

    Google Scholar 

  40. Selvi, V., Umarani, R.: Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl. 5, 1–6 (2010)

    Google Scholar 

  41. Xu, Y., Li, K., He, L., Truong, T.K.: A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization. J. Parallel Distrib. Comput. 73, 1306 (2013)

    Article  Google Scholar 

  42. Guo, T., Hu, J., Mao, S., Zhang, Z.: Evaluation of the pressure-volume-temperature (PVT) data of water from experiments and molecular simulations since 1990. Phys. Earth Planet. Inter. 245, 88–102 (2015)

    Article  Google Scholar 

  43. Serway, R.A., Vuille, C.: Essentials of college physics. Cengage Learning, Boston (2007)

    Google Scholar 

  44. Arfken, G.: International Edition University Physics. Elsevier, Amsterdam (2012)

    Google Scholar 

  45. Mishima, O.: Volume of supercooled water under pressure and the liquid-liquid critical point. J. Chem. Phys. 133, 144503 (2010)

    Article  Google Scholar 

  46. Liu, P., Wu, J., Wang, Y.: Hybrid algorithms for hardware/software partitioning and schedulingon reconfigurable devices. Math. Comput. Model. 58, 409–420 (2013)

    Article  MATH  Google Scholar 

  47. He, W., Sun, D.: Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies. Int. J. Adv. Manuf. Technol. 66(1–4), 501–514 (2012)

    Google Scholar 

  48. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., 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)

    Google Scholar 

  49. https://aws.amazon.com/ec2/pricing/on-demand/

  50. Gómez-Martín C., Vega-Rodrígez, M.A., González-Sánchez, J.-L., Corral-García, J., and Cortés-Polo, F.: Performance and energy aware scheduling simulator for high-performance computing. In: Proceedings of the 7th Iberian Grid Infrastructure Conference, pp. 17–29 (2013)

  51. Hao, Y., Liu, G., Hou, R., Zhu, Y., Lu, J.: Performance analysis of gang scheduling in a grid. J. Netw. Syst. Mgmt. 23, 650 (2014)

    Article  Google Scholar 

  52. Jansen, K., Klein, K.-M., and Verschae, J.: Closing the gap for makespan scheduling via sparsification techniques. arXiv preprint arXiv:1604.07153 (2016)

  53. Tyagi, R., Gupta, S.K.: A survey on scheduling algorithms for parallel and distributed systems. Silicon Photonics and High Performance Computing, pp. 51–64. Springer, Singapore (2018)

    Chapter  Google Scholar 

  54. Wu, Z., Xing, S., Cai, S., Xiao, Z., Ming, Z.: A genetic-ant-colony hybrid algorithm for task scheduling in cloud system. International Conference on Smart Computing and Communication, pp. 183–193. Springer, Cham (2016)

    Google Scholar 

  55. Moses, J., Iyer, R., Illikkal, R., Srinivasan, S., Aisopos, K.: Shared resource monitoring and throughput optimization in cloud-computing datacenters. In: Parallel and Distributed Processing Symposium (IPDPS), 2011 IEEE International, pp. 1024–1033 (2011)

  56. Mehdi, N.A., Mamat, A., Amer, A., and Abdul-Mehdi, Z.T.: Minimum completion time for power-aware scheduling in cloud computing. In: Developments in E-systems Engineering (DeSE), pp. 484–489 (2011)

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt

    Aida A. Nasr, Nirmeen A. El-Bahnasawy, Gamal Attiya & Ayman El-Sayed

  2. Department of Computer Science, University of Texas, San Antonio, TX, 78249, USA

    Anthony T. Chronopoulos

  3. Department of Computer Engineering and Informatics, University of Patras, 26500, Rio, Greece

    Anthony T. Chronopoulos

Authors
  1. Aida A. Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony T. Chronopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nirmeen A. El-Bahnasawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gamal Attiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ayman El-Sayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aida A. Nasr.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nasr, A.A., Chronopoulos, A.T., El-Bahnasawy, N.A. et al. A novel water pressure change optimization technique for solving scheduling problem in cloud computing. Cluster Comput 22, 601–617 (2019). https://doi.org/10.1007/s10586-018-2867-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2867-7

Keywords

Search

Navigation