A Novel Memory Concurrent Editing Model for Large-Scale Video Streams in Edge Computing

Haitao Liu, Qingkui Chen () and Puchen Liu
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Haitao Liu: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Qingkui Chen: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Puchen Liu: Department of Statistics, Shanghai Polytechnic University, Shanghai 201209, China

Mathematics, 2023, vol. 11, issue 14, 1-20

Abstract: Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficiency of concurrent editing and the satisfaction of service users under the constraint of limited memory buffer resources, the allocation of memory buffers of concurrent editing servers is transformed into the bin-packing problem, which is solved using an ant colony algorithm to achieve the least loaded utilization batch. Meanwhile, a new distributed online concurrent editing algorithm for video streams is designed for the conflict problem of large-scale video editing in an edge computing environment. It incorporates dual-buffer read-and-write technology to solve the difficult problem of concurrent inefficiency of editing and writing disks. The experimental results of the simulation show that the scheme not only achieves a good performance in the scheduling of concurrent editing but also implements the editing resource allocation function in an efficient and reasonable way. Compared with the benchmark traditional single-exclusive editing scheme, the proposed optimized scheme can simultaneously enhance editing efficiency and user satisfaction under the restriction of providing the same memory buffer computing resources. The proposed model has a wide application to video real-time processing application scenarios in edge computing.

Keywords: concurrent editing; memory models; edge computing; large-scale video streams (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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