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Optimal Searching of Prefetched DASH Segments in Fog Nodes: A Multi-Armed Bandit Approach

Published: 22 November 2021 Publication History

Abstract

Data prefetching at fog nodes that significantly reduces latency is critical for video streaming applications. In the framework of dynamic video streaming over fog, some segments (parts of video) can be prefetched by the network provider. Sharing the dynamic information about prefetched node locations to all of the users is not a scalable approach, due to signalling and performance overheads. However, at the time of downloading, the client devices can search for the availability of DASH video segments in the fog nodes present in their vicinity. There is, however, a dilemma of how many fog nodes can be queried, without affecting performance such as continuous playback of the video. \par In this paper, we propose an efficient mechanism for searching video segments over different fog nodes. This search mechanism is formulated as a sequential stochastic modeling framework known as Multi-Armed Bandit~(MAB). While the state space of this model is a countably infinite set, we propose an algorithmic approach to transform to a finite state space model without loss of optimality. With extensive simulation results, the analytical results are validated. We study the different parameters influencing the improved optimal DASH performance, in terms of segment prefetch probability, and number of search attempts made by the client.

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Cited By

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  • (2024)vStream IT: Video Streaming for Resource Constrained IoTs - An Optimal Control Approach2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427461(129-134)Online publication date: 3-Jan-2024
  • (2023)MABSearch: The Bandit Way of Learning the Learning Rate—A Harmony Between Reinforcement Learning and Gradient DescentNational Academy Science Letters10.1007/s40009-023-01292-147:1(29-34)Online publication date: 4-Jun-2023
  • (2023)NPROS: A Not So Pure Random Orthogonal search algorithm—A suite of random optimization algorithms driven by reinforcement learningOptimization Letters10.1007/s11590-023-02038-018:9(2091-2111)Online publication date: 11-Jul-2023

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cover image ACM Conferences
Q2SWinet '21: Proceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks
November 2021
143 pages
ISBN:9781450390804
DOI:10.1145/3479242
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 November 2021

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Author Tags

  1. dash
  2. fog nodes
  3. mab
  4. mdp
  5. performance modeling and evaluation
  6. video streaming

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Overall Acceptance Rate 46 of 131 submissions, 35%

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View all
  • (2024)vStream IT: Video Streaming for Resource Constrained IoTs - An Optimal Control Approach2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427461(129-134)Online publication date: 3-Jan-2024
  • (2023)MABSearch: The Bandit Way of Learning the Learning Rate—A Harmony Between Reinforcement Learning and Gradient DescentNational Academy Science Letters10.1007/s40009-023-01292-147:1(29-34)Online publication date: 4-Jun-2023
  • (2023)NPROS: A Not So Pure Random Orthogonal search algorithm—A suite of random optimization algorithms driven by reinforcement learningOptimization Letters10.1007/s11590-023-02038-018:9(2091-2111)Online publication date: 11-Jul-2023

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