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Edge data distribution as a network Steiner tree estimation in edge computing

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Abstract

Many modern day cloud hosted applications such as virtual reality, real time games require low latency data access and computation to improve response time. So it is essential to bring the computation and data storage edge servers closer to the user’s geographical location to improve response times and save bandwidth. In particulars, in online gaming and on demand video services, the required application data present at cloud servers need to be placed on the edge servers to provide low latency app-functionalities. The transfer of huge amount of data from cloud server to edge server incurs high cost and time penalties. Thus, we need an efficient way to solve edge data distribution (EDD) problem which distribute the application data to the edge servers that minimizes transfer cost. In this work, we provide a refined formulation of an optimal approach to solve the EDD problem using integer linear programming (ILP) technique. Due to the time complexity limitation of the ILP approach, we propose an O(k) approximation algorithm based on network Steiner tree estimation (EDD-NSTE) for estimating solutions to dense large-scale EDD problem. The proposed approach is analyzed to be 11/6 approximation which is better than the state-of-the-art 2 approximation EDD-A approach. The experimental evaluation through simulation using real world EUA data set demonstrate that the EDD-NSTE outperform state-of-the-art approach and other representative approaches.

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Notes

  1. IP solvers are the frameworks or tools used to solve integer programming problems. http://lpsolve.sourceforge.net/5.5/.

  2. https://github.com/swinedge/eua-dataset.

  3. O(k) approximation algorithm means that solution produced by the algorithm is atmost k times the optimal solution.

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Authors and Affiliations

  1. Department of CSE, SRM University, Amaravati, AP, India

    Chinmaya Kumar Swain

  2. Department of CSE, IIT Guwahati, Guwahati, Assam, 781039, India

    Ravi Shankar & Aryabartta Sahu

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  1. Chinmaya Kumar Swain
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  2. Ravi Shankar
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  3. Aryabartta Sahu
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Swain, C.K., Shankar, R. & Sahu, A. Edge data distribution as a network Steiner tree estimation in edge computing. Computing (2024). https://doi.org/10.1007/s00607-024-01259-0

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