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

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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.

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Authors and Affiliations

  1. Indian Institute of Information Technology (IIIT) Vadodara, Gandhinagar Campus, Sector 28, Gandhinagar, Gujarat, 382028, India

    Soumen Atta

  2. Faculty of Informatics, Masaryk University, Botanická 68a, Brno, Czech Republic

    Soumen Atta

  3. Industrial and Systems Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, 721302, India

    Goutam Sen

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  1. Soumen Atta
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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

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