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An Intelligent GbMFPA Model for Sales Optimization in Distributed Grid-Market

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Abstract

Grid market enables a sensible distributed and user-vendor friendly environment for improving the sale, services and incentives. In this paper, a multi-factor evaluation based intelligent GbMFPA model is investigated to optimize the production allocation in bid-based Grid Market. The bid-market model is established with intermediate well-informed setup between grid server and clients for selling rare and antique products. The architectural processing is summarized as Dynamic Price updation and Incentive-and-Profile driven scheduling. The continuous monitoring on customer-interest, bid-count, deadline-criticality and product availability parameters is adopted by the server for cyclic updation on product-price. The framework is also integrated with a multi-aspects based scheduler for effective allocation of available products to the bid-user. The composite evaluation is conducted under product-availability, deadline-criticality, product-popularity and user-trust parameters for incentive-gain based product allocation. This dual-layer processed connected and cyclic framework is designed and implemented to increase user admissibility and to gain maximum profit. The proposed framework is implemented in a real open environment with product characteristic specifications. The price-update and sale-price observations are collected and presented to verify the client side and vendor benefits achieved from this framework. The implementation is performed on seven rare and antique products with relative characterization and environmental configuration. The results identified that a significant gain in the price-hike is achieved from the proposed intelligent GbMFPA system.

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References

  1. Nazir, B., Khan, I. A., Shashirband, S., Chronopoulos, A. T., & Khan, S. (2017). Load balancing in grid computing: taxonomy, trends and opportunities. Journal of Network and Computer Applications, 88, 99–111.

    Article  Google Scholar 

  2. Ramesh, V., & Reka, S. S. (2016). Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming. Perspectives in Science, 8, 169–171.

    Article  Google Scholar 

  3. Singh, A. K., & Garg, R. (2015). Adaptive workflow scheduling in grid computing based on dynamic resource availability. Engineering Science and Technology, an International Journal, 18(2), 256–269.

    Article  Google Scholar 

  4. Jie, W., & Wang, L. (2009). Towards supporting multiple virtual private computing environments on computational Grids. Advances in Engineering Software, 40(4), 239–245.

    Article  Google Scholar 

  5. Keshavarz, H., Ohshima, N., Komaki, S., & Amiri, E. (2014). Resource allocation in grid: a review. Procedia-Social and Behavioral Sciences, 129, 436–440.

    Article  Google Scholar 

  6. Qureshi, K., Khan, F. G., & Nazir, B. (2010). Performance evaluation of fault tolerance tech-niques in grid computing system. Computers and Electrical Engineering, 36(6), 1110–1122.

    Article  Google Scholar 

  7. Schmidt, M., Fallenbeck, N., Dörnemann, T., Schridde, C., Freisleben, B., & Smith, M. (2009). Secure on-demand grid computing. Future Generation Computer Systems, 25(3), 315–325.

    Article  Google Scholar 

  8. Chaudhari, S., & Sreeja, S. R. (2015). Study on grid resource monitoring and prediction. Procedia Computer Science, 45, 815–822.

    Article  Google Scholar 

  9. Lee, S. P., Rezaei, R., Parizi, R. M., & Alkhanak, E. N. (2016). Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. Journal of Systems and Software, 113, 1–26.

    Article  Google Scholar 

  10. Tawfik, A., Marzok, M. A., Khamis, S. M., & Reda, N. M. (2015). Sort-Mid tasks schedul-ing algorithm in grid computing. Journal of Advanced Research, 6(6), 987–993.

    Article  Google Scholar 

  11. Lin, C.-Y., Lin, C.-F., & Chang, R. S. (2012). An Adaptive scoring job scheduling algorithm for grid computing. Information Sciences, 207, 79–89.

    Article  Google Scholar 

  12. Chana, I., Rajni. (2013). Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Generation Computer Systems, 29(3), 751–762.

    Article  Google Scholar 

  13. Caminero, B., Carrión, C., Tomás, L., & Conejero, J. (2014). From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments. Future Generation Computer Systems, 34, 76–93.

    Article  Google Scholar 

  14. Ali, H. A., Haikal, A. Y., & Abdullah, A. M. (2017). Reliable and efficient hierarchical organization model for computational grid. Journal of Parallel and Distributed Computing, 104, 191–205.

    Article  Google Scholar 

  15. Cinque, M., Corradi, A., Foschini, L., Frattini, F., Povedano-Molina, J., & Bellavista, P. (2017). GAMESH: A grid architecture for scalable monitoring and enhanced de-pendable job scheduling. Future Generation Computer Systems, 71, 192–201.

    Article  Google Scholar 

  16. Haron, N., Zakaria, M. N. B., Mahmood, A. K. B., & Shah, S. N. M. (2012). Agent-based robust grid scheduling framework for high performance computing. AASRI Procedia, 1, 554–560.

    Article  Google Scholar 

  17. Vega-Rodríguez, M. A., Prieto-Castrillo, F., & Arsuaga-Ríos, M. (2013). Meta-schedulers for grid computing based on multi-objective swarm algorithms. Applied Soft Computing, 13(4), 1567–1582.

    Article  Google Scholar 

  18. Mahmood, A. K. B., Oxley, A., & Shah, S. N. M. (2011). Dynamic multilevel hybrid scheduling algorithms for grid computing. Procedia Computer Science, 4, 402–411.

    Article  Google Scholar 

  19. Cancela, H., Alba, E., & Nesmachnow, S. (2012). A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling. Applied Soft Computing, 12(2), 626–639.

    Article  Google Scholar 

  20. Manimegalai, D., & Selvi, S. (2015). Multiobjective variable neighborhood search algorithm for schedul-ing independent jobs on computational grid. Egyptian Informatics Journal, 16(2), 199–212.

    Article  Google Scholar 

  21. Tinini, R. I., & Pavani, G. S. (2016). Distributed meta-scheduling in lambda grids by means of ant colony optimization. Future Generation Computer Systems, 63, 15–24.

    Article  Google Scholar 

  22. McGough, A. S., Darlington, J., & Afzal, A. (2008). Capacity planning and scheduling in Grid computing environments. Future Generation Computer Systems, 24(5), 404–414.

    Article  Google Scholar 

  23. Li, L., & Li, C. (2009). Utility-based scheduling for grid computing under constraints of energy budget and deadline. Computer Standards and Interfaces, 31(6), 1131–1142.

    Article  MathSciNet  Google Scholar 

  24. Abawajy, J. H. (2009). Adaptive hierarchical scheduling policy for enterprise grid computing systems. Journal of Network and Computer Applications, 32(3), 770–779.

    Article  Google Scholar 

  25. Jagan, A., Narayana, N. S., & Naik, K. J. (2015). A novel algorithm for fault tolerant job Scheduling and load balancing in grid computing environment. In International conference on green computing and internet of things (ICGCIoT) (pp. 1113–1118) Noida.

  26. Wei, S.-C., & Yeh, W.-C. (2012). Economic-based resource allocation for reliable Grid-computing service based on Grid Bank. Future Generation Computer Systems, 28(7), 989–1002.

    Article  Google Scholar 

  27. Mahan, F., Isazadeh, A., & Khanli, L. M. (2011). Active rule learning using decision tree for resource management in Grid computing. Future Generation Computer Systems, 27(6), 703–710.

    Article  Google Scholar 

  28. Navimipour, N. J., & Souri, A. (2014). Behavioral modeling and formal verification of a re-source discovery approach in Grid computing. Expert Systems with Applications, 41(8), 3831–3849.

    Article  Google Scholar 

  29. Chang, R.-S., & Chung, W.-C. (2009). A new mechanism for resource monitoring in Grid computing. Future Generation Computer Systems, 25(1), 1–7.

    Article  Google Scholar 

  30. Basu, A., Kiyomoto, S., Rahman, M. S., & Bhuiyan, M. A. (2017). Privacy-friendly secure bidding for smart grid demand-response. Information Sciences, 379, 229–240.

    Article  Google Scholar 

  31. Sanjay, M., Adigun, M., & Babafemi, O. (2013). Towards developing grid-based portals for e-Commerce on-Demand services on a utility computing platform. IERI Procedia, 4, 81–87.

    Article  Google Scholar 

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Juneja, K. An Intelligent GbMFPA Model for Sales Optimization in Distributed Grid-Market. Wireless Pers Commun 103, 2403–2421 (2018). https://doi.org/10.1007/s11277-018-5918-8

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