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A Cost Efficient Service Broker Policy for Data Center Allocation in IaaS Cloud Model

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Abstract

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

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References

  1. Zhang, Z., Wu, C., & Cheung, D. W. L. (2013). A survey on cloud interoperability: Taxonomies, standards, and practice. SIGMETRICS Performance Evaluation Review, 40(4), 13–22.

    Article  Google Scholar 

  2. Shawish, A., & Salama, M. (2014). Cloud computing: Paradigms and technologies. Inter-cooperative Collective Intelligence: Techniques and Applications, 495, 39–68.

    Google Scholar 

  3. Alizadeh, M., et al. (2014). CONGA : Distributed congestion-aware load balancing for datacenters. In Sigcomm 2014 (pp. 503–514).

  4. Buyya, R., Garg, S. K., & Calheiros, R. N. (2011). SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions. In: International conference on cloud and service computing (pp. 1–10).

  5. Tripathy, C., Parida, S., & Nayak, S. C. (2015). Truthful resource allocation detection mechanism for cloud computing. In Third international symposium on women in computing and informatics (WCI’15), Indu Nair (Ed.) (pp. 487–491). ACM.

  6. Chinnaiah, V., Gudi Pudi, S., Somasundaram, T. S., & Basha, S. S. (2018). A cloud resource allocation strategy based on fitness based live migration and clustering. Wireless Personal Communications, 98(3), 2943–2958.

    Article  Google Scholar 

  7. Menakadevi, T., & Devakirubai, N. (2016). An optimum service broker policy for selecting data center in cloudanalyst. International Research Journal of Engineering and Technology, 5(9), 76–84.

    Article  Google Scholar 

  8. Mustafa, S., Nazir, B., Hayat, A., Khan, R., & Madani, S. A. (2015). Resource management in cloud computing: Taxonomy, prospects, and challenges q. Computers & Electrical Engineering, 47, 186–203.

    Article  Google Scholar 

  9. Nayak, S. C., & Tripathy, C. (2018). Deadline based task scheduling using multi-criteria decision-making in cloud environment. Ain Shams Engineering Journal, 9, 3315–3324.

    Article  Google Scholar 

  10. Vakilinia, S., Ali, M. M., & Qiu, D. (2015). Modeling of the resource allocation in cloud computing centers. Computer Networks, 91, 453–470.

    Article  Google Scholar 

  11. Nayak, S. C., Parida, S., & Tripathy, C. (2018). Modeling of task scheduling algorithm using petri-net in cloud computing. In K. Saeed, N. Chaki, B. Pati, S. Bakshi, & D. Mohapatra (Eds.), Progress in advanced computing and intelligent engineering: Advances in intelligent systems and computing (Vol. 563, pp. 633–643). Singapore: Springer.

    Chapter  Google Scholar 

  12. Nayak, S. C., & Tripathy, C. (2018). Deadline sensitive lease scheduling in cloud computing environment using AHP. Journal of King Saud University: Computer and Information Sciences, 30(2), 152–163.

    Google Scholar 

  13. Chandan, S., Parida, S., Tripathy, C., & Kumar, P. (2018). An enhanced deadline constraint based task scheduling mechanism for cloud environment. Journal of King Saud University: Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.10.009.

    Article  Google Scholar 

  14. Venkata Krishna, J., Apparao Naidu, G., & Upadhayaya, N. (2018). A Lion-Whale optimization-based migration of virtual machines for data centers in cloud computing. International Journal of Communication Systems, 31(8), 1–18.

    Article  Google Scholar 

  15. Asghari, S., & Navimipour, N. J. (2018). Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments. International Journal of Communication Systems, 31, e3708.

    Article  Google Scholar 

  16. Akhter, N., & Othman, M. (2016). Energy aware resource allocation of cloud data center: Review and open issues. Cluster Computing, 19(3), 1163–1182.

    Article  Google Scholar 

  17. Naha, R. K., & Othman, M. (2016). Cost-aware service brokering and performance sentient load balancing algorithms in the cloud. Journal of Network and Computer Applications, 75, 47–57.

    Article  Google Scholar 

  18. Manasrah, A. M., Smadi, T., & ALmomani, A. (2017). A variable service broker routing policy for data center selection in cloud analyst. Journal of King Saud University: Computer and Information Sciences, 29(3), 365–377.

    Google Scholar 

  19. Díaz, J. L., Entrialgo, J., García, M., García, J., & García, D. F. (2017). Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing. Future Generation Computing Systems, 71, 129–144.

    Article  Google Scholar 

  20. Heilig, L., Buyya, R., & Voß, S. (2017). Location-aware brokering for consumers in multi-cloud computing environments. Journal of Network and Computer Applications, 95, 79–93.

    Article  Google Scholar 

  21. Yuan, X., Min, G., Yang, L. T., Ding, Y., & Fang, Q. (2017). A game theory-based dynamic resource allocation strategy in geo-distributed datacenter clouds. Future Generation Computing Systems, 76, 63–72.

    Article  Google Scholar 

  22. Anastasi, G. F., Carlini, E., Coppola, M., & Dazzi, P. (2017). QoS-aware genetic cloud brokering. Future Generation Computing Systems, 75, 1–13.

    Article  Google Scholar 

  23. Michon, É., Gossa, J., Genaud, S., Unbekandt, L., & Kherbache, V. (2017). Schlouder: A broker for IaaS clouds. Future Generation Computing Systems, 69, 11–23.

    Article  Google Scholar 

  24. Baker, T., Aldawsari, B., Asim, M., Tawfik, H., Maamar, Z., & Buyya, R. (2018). Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications. Sustainable Computing: Informatics and Systems, 19, 242–252.

    Google Scholar 

  25. Halabi, T., & Bellaiche, M. (2018). A broker-based framework for standardization and management of cloud security-SLAs. Computers & Security, 75, 59–71.

    Article  Google Scholar 

  26. Askarizade Haghighi, M., Maeen, M., & Haghparast, M. (2019). An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms: Energy efficient dynamic cloud resource management. Wireless Personal Communications, 104(4), 1367–1391. https://doi.org/10.1007/s11277-018-6089-3.

    Article  Google Scholar 

  27. Kessaci, Y., Melab, N., & Talbi, E. G. (2013). A pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment. In 2013 IEEE congress on evolutionary computation CEC 2013 (pp. 2496–2503).

  28. Quarati, A., Clematis, A., Galizia, A., & D’Agostino, D. (2013). Hybrid clouds brokering: Business opportunities, QoS and energy-saving issues. Simulation Modelling Practice and Theory, 39, 121–134.

    Article  Google Scholar 

  29. Chang, Y. S., Fan, C. T., Sheu, R. K., Jhu, S. R., & Yuan, S. M. (2018). An agent-based workflow scheduling mechanism with deadline constraint on hybrid cloud environment. International Journal of Communication Systems, 31(1), 1–17.

    Article  Google Scholar 

  30. Jeyakrishnan, V., & Sengottuvelan, P. (2017). A hybrid strategy for resource allocation and load balancing in virtualized data centers using BSO algorithms. Wireless Personal Communications, 94(4), 2363–2375.

    Article  Google Scholar 

  31. Grozev, N., & Buyya, R. (2016). Regulations and latency-aware load distribution of web applications in multi-clouds. The Journal of Supercomputing, 72(8), 3261–3280.

    Article  Google Scholar 

  32. Ghobaei-Arani, M., Rahmanian, A. A., Shamsi, M., & Rasouli-Kenari, A. (2018). A learning-based approach for virtual machine placement in cloud data centers. International Journal of Communication Systems, 31(8), 1–18.

    Article  Google Scholar 

  33. Liaqat, M., et al. (2017). Federated cloud resource management: Review and discussion. Journal of Network and Computer Applications, 77, 87–105.

    Article  Google Scholar 

  34. Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016). An overview of virtual machine placement schemes in cloud computing. Journal of Network and Computer Applications, 66, 106–127.

    Article  Google Scholar 

  35. Heilig, L., Lalla-Ruiz, E., & Voß, S. (2016). A cloud brokerage approach for solving the resource management problem in multi-cloud environments. Computer and Industrial Engineering, 95, 16–26.

    Article  Google Scholar 

  36. Tordsson, J., Montero, R. S., Moreno-Vozmediano, R., & Llorente, I. M. (2012). Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computing Systems, 28(2), 358–367.

    Article  Google Scholar 

  37. Do, C. T., Tran, N. H., Huh, E. N., Hong, C. S., Niyato, D., & Han, Z. (2015). Dynamics of service selection and provider pricing game in heterogeneous cloud market. Journal of Network and Computer Applications, 69, 152–165.

    Article  Google Scholar 

  38. Zhang, N., Yang, X., Zhang, M., Sun, Y., & Long, K. (2018). A genetic algorithm-based task scheduling for cloud resource crowd-funding model. International Journal of Communication Systems, 31(1), 1–10.

    Article  Google Scholar 

  39. Ghafouri, R., Movaghar, A., & Mohsenzadeh, M. (2018). Time-cost efficient scheduling algorithms for executing workflow in infrastructure as a service clouds. Wireless Personal Communications,. https://doi.org/10.1007/s11277-018-5895-y.

    Article  Google Scholar 

  40. Rahmanian, A. A., Horri, A., & Dastghaibyfard, G. (2018). Toward a hierarchical and architecture-based virtual machine allocation in cloud data centers. International Journal of Communication Systems, 31(4), 1–28.

    Article  Google Scholar 

  41. Banerjee, S., Mandal, R., & Biswas, U. (2018). An approach towards amelioration of an efficient VM allocation policy in cloud computing domain. Wireless Personal Communications, 98(2), 1799–1820.

    Article  Google Scholar 

  42. Manasrah, A. M., & Gupta, A. B. B. (2019). An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, 22, 1639–1653. https://doi.org/10.1007/s10586-017-1559-z.

    Article  Google Scholar 

  43. Mehta, H. K., Pawar, P., & Kanungo, P. (2016). A two level broker system for infrastructure as a service cloud. Wireless Personal Communications, 90(3), 1135–1147.

    Article  Google Scholar 

  44. Wickremasinghe, B., Calheiros, R. N., & Buyya, R. (2010). CloudAnalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications. In Proceedings: International conference on advanced information networking and application (AINA) (pp. 446–452).

  45. Magalhães, D., Calheiros, R. N., Buyya, R., & Gomes, D. G. (2015). Workload modeling for resource usage analysis and simulation in cloud computing. Computers & Electrical Engineering, 47, 69–81.

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Suvendu Chandan Nayak for his continuous technical support and linguistic assistance during the preparation of this manuscript.

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Correspondence to Sasmita Parida.

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Parida, S., Pati, B. A Cost Efficient Service Broker Policy for Data Center Allocation in IaaS Cloud Model. Wireless Pers Commun 115, 267–289 (2020). https://doi.org/10.1007/s11277-020-07570-1

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