Skip to main content
Log in

Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approach

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In video-on-demand (VoD) services, large volumes of digital data are kept at hubs which are spatially distributed over large geographic areas and users are connected to these hubs based on their demands. In this article, we consider a large database of video files, that are pre-partitioned to multiple segments based on the demand patterns of users. These segments are restricted to be located only in hubs. Here, users are allowed to be allocated to multiple hubs and all hubs are assumed to be connected with each other. We jointly decide the location of hubs, the placement of segments to these hubs and then the assignment of users to these hubs as per their demand patterns and finally, we find the optimal paths to route the demands of users for different segments having the objective of minimizing the total routing cost. In this article, a differential evolution (DE) based method is proposed to solve the problem. The proposed DE-based method utilizes an efficient function to evaluate the objective value of a candidate solution to the proposed problem. It also incorporates two problem-specific solution refinement techniques for faster convergence. Instances of the problem are generated from the real world movie database and the proposed method is applied to these instances and the performance is evaluated against the benchmark results obtained from CPLEX.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  2. Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111– 125

    Article  Google Scholar 

  3. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  4. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  5. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

  6. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

    Google Scholar 

  7. Alumur S, Kara BY (2008) Network hub location problems: The state of the art. Eur J Oper Res 190 (1):1–21

    Article  MathSciNet  MATH  Google Scholar 

  8. Androutsellis-Theotokis S, Spinellis D (2004) A survey of peer-to-peer content distribution technologies. ACM Comput Surv (CSUR) 36(4):335–371

    Article  Google Scholar 

  9. Applegate D, Archer A, Gopalakrishnan V, Lee S, Ramakrishnan K (2016) Optimal content placement for a large-scale vod system. IEEE/ACM Trans Netw 24(4):2114–2127

    Article  Google Scholar 

  10. Atta S, Mahapatra PRS (2013) Genetic algorithm based approach for serving maximum number of customers using limited resources. Procedia Technol 10:492–497

    Article  Google Scholar 

  11. Atta S, Mahapatra PRS (2014) Genetic algorithm based approaches to install different types of facilities. In: ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I. Springer, pp 195–203

  12. Atta S, Mahapatra PRS (2019) Population-based improvement heuristic with local search for single-row facility layout problem. Sādhanā 44(11):222

    Article  MathSciNet  Google Scholar 

  13. Atta S, Mahapatra PRS, Mukhopadhyay A (2018) Solving maximal covering location problem using genetic algorithm with local refinement. Soft Comput 22(12):3891–3906

    Article  Google Scholar 

  14. Atta S, Mahapatra PRS, Mukhopadhyay A (2018) Solving uncapacitated facility location problem using monkey algorithm. In: Intelligent engineering informatics. Springer, pp 71–78

  15. Atta S, Mahapatra PRS, Mukhopadhyay A (2019) Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum ga-based approaches. Soft Comput 23(23):12,347–12,362

    Article  Google Scholar 

  16. Atta S, Mahapatra PRS, Mukhopadhyay A (2019) Solving tool indexing problem using harmony search algorithm with harmony refinement. Soft Comput 23(16):7407–7423

    Article  Google Scholar 

  17. Bhattacharya A, Chattopadhyay PK (2010) Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst 25(4):1955–1964

    Article  Google Scholar 

  18. de Camargo RS, Miranda Jr G, Luna HP (2008) Benders decomposition for the uncapacitated multiple allocation hub location problem. Comput Oper Res 35(4):1047–1064

  19. Chakraborty UK (2008) Advances in differential evolution, vol 143 Springer

  20. Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern-Part A: Syst Hum 38(1):218–237

    Article  Google Scholar 

  21. Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution–an updated survey. Swarm Evol Comput 27:1–30

    Article  Google Scholar 

  22. Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  23. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  24. Drezner Z, Hamacher H (2001) Facility location: applications and theory. Springer Science & Business Media

  25. Ebery J, Krishnamoorthy M, Ernst A, Boland N (2000) The capacitated multiple allocation hub location problem: Formulations and algorithms. Eur J Oper Res 120(3):614–631

    Article  MATH  Google Scholar 

  26. Ernst AT, Hamacher H, Jiang H, Krishnamoorthy M, Woeginger G (2009) Uncapacitated single and multiple allocation p-hub center problems. Comput Oper Res 36(7):2230–2241

    Article  MathSciNet  MATH  Google Scholar 

  27. Ernst AT, Krishnamoorthy M (1996) Efficient algorithms for the uncapacitated single allocation p-hub median problem. Locat Sci 4(3):139–154

    Article  MATH  Google Scholar 

  28. Ernst AT, Krishnamoorthy M (1998) Exact and heuristic algorithms for the uncapacitated multiple allocation p-hub median problem. Eur J Oper Res 104(1):100–112

    Article  MATH  Google Scholar 

  29. Farahani RZ, Hekmatfar M, Arabani AB, Nikbakhsh E (2013) Hub location problems: a review of models, classification, solution techniques, and applications. Comput Indust Eng 64(4):1096–1109

    Article  Google Scholar 

  30. Feoktistov V (2006) Differential evolution. Springer, Berlin

  31. Fiore M (2011) Content replication and placement in mobile networks. Citeseer, Princeton

  32. Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference. In: International encyclopedia of statistical science. Springer, pp 977–979

  33. Harper FM, Konstan JA (2016) The movielens datasets: History and context. ACM Trans Interact Intell Syst (tiis) 5(4):19

    Google Scholar 

  34. Hollander M, Wolfe D (1999) Nonparametric statistical methods. Wiley-Interscience. New York

  35. Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods, vol 751. Wiley

  36. Ilonen J, Kamarainen JK, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93–105

    Article  Google Scholar 

  37. Jaillet P, Song G, Yu G (1996) Airline network design and hub location problems. Locat Sci 4(3):195–212

    Article  MATH  Google Scholar 

  38. Klincewicz JG (1998) Hub location in backbone/tributary network design: a review. Locat Sci 6(1-4):307–335

    Article  Google Scholar 

  39. Liu B, Wang L, Jin YH (2007) An effective pso-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern Part B (Cybern) 37(1):18–27

    Article  Google Scholar 

  40. Loiola EM, de Abreu NMM, Boaventura-Netto PO, Hahn P, Querido T (2007) A survey for the quadratic assignment problem. Eur J Oper Res 176(2):657–690

    Article  MathSciNet  MATH  Google Scholar 

  41. Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1-2):61–106

    Article  Google Scholar 

  42. Noman N, Iba H (2008) Differential evolution for economic load dispatch problems. Electr Power Syst Res 78(8):1322–1331

    Article  Google Scholar 

  43. Onwubolu G, Davendra D (2006) Scheduling flow shops using differential evolution algorithm. Eur J Oper Res 171(2):674– 692

    Article  MATH  Google Scholar 

  44. Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm Evol Comput 44:546–558

    Article  Google Scholar 

  45. Paterlini S, Krink T (2006) Differential evolution and particle swarm optimisation in partitional clustering. Comput Stat Data Anal 50(5):1220–1247

    Article  MathSciNet  MATH  Google Scholar 

  46. Price K, Storn R, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer Science & Business Media

  47. Price K (2013) Differential evolution. In: Handbook of optimization. Springer, pp 187–214

  48. Rocca P, Oliveri G, Massa A (2011) Differential evolution as applied to electromagnetics. IEEE Antenn Propag Mag 53(1):38–49

    Article  Google Scholar 

  49. Sen G, Krishnamoorthy M (2018) Discrete particle swarm optimization algorithms for two variants of the static data segment location problem. Appl Intell 48(3):771–790

    Article  Google Scholar 

  50. Sen G, Krishnamoorthy M, Rangaraj N, Narayanan V (2015) Exact approaches for static data segment allocation problem in an information network. Comput Oper Res 62:282–295

    Article  MathSciNet  MATH  Google Scholar 

  51. Sen G, Krishnamoorthy M, Rangaraj N, Narayanan V (2016) Mathematical models and empirical analysis of a simulated annealing approach for two variants of the static data segment allocation problem. Networks 68(1):4–22

    Article  MathSciNet  Google Scholar 

  52. Sopan A, Teo CL (2009) Analysis of movielens rating network using a novel bipartite graph layout, webpage. https://wiki.cs.umd.edu/cmsc734_09/index.php?title=Analysis_of_MovieLens_rating_network_using_a_novel_Bipartite_Graph_Layout

  53. Storn R (1996) Differential evolution design of an iir-filter. In: Evolutionary Computation, 1996., Proceedings of IEEE International Conference on, pp. 268–273. IEEE

  54. Storn R (1996) On the usage of differential evolution for function optimization. In: Biennial conference of the north american fuzzy information processing society (NAFIPS), vol 519. IEEE, Berkeley

  55. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  56. Thomsen R (2004) Multimodal optimization using crowding-based differential evolution. In: 2004. CEC2004. Congress on Evolutionary computation. IEEE, vol 2, pp 1382–1389

  57. Thouin F, Coates M (2007) Video-on-demand networks: design approaches and future challenges. IEEE Network 21(2):42–48

    Article  Google Scholar 

  58. Thouin F, Coates M, Goodwill D (2006) Video-on-demand equipment allocation IEEE

  59. Verma DC (2002) Content distribution networks. A Wiley-Interscience Publication

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soumen Atta.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Atta, S., Sen, G. Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approach. Appl Intell 50, 1573–1589 (2020). https://doi.org/10.1007/s10489-019-01609-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01609-y

Keywords

Navigation