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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Mathematical framework for real-time data processing in edge computing : Context-aware priority scheduling analysis

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pp. 721–732Vol. 27Issue 3March 2024DOI: 10.47974/JSMS-1281XML
Published Online:
01 Apr 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1281
Pages:
721–732

Abstract

The increased demand for real-time data processing has brought into the emergence of edge computing as an important paradigm that helps to fulfil all such requirements on time-sensitive applications as they have Latency and efficiency prerequisites. The work offers a new framework designed to conduct real-time processing of data, and it relates to situations when the data is located at the network edge. To achieve the best scheduling and edge processing efficiency, this work proposed a “Context-Aware Priority Scheduling Framework” that utilizes contextual information. Choosing the ideal processing method in this architecture requires that one understands the context with which to undertake data collection. The framework has the ability to automatically prioritize data processing activities based on various variables, which include location residence, network situation, device capabilities and web needs or preferences by data consumers. The overall responsiveness of a system is improved and becomes more effective when the critical tasks are given high priority. When two scheduling approaches context-aware and priority are united, they not only enhance the efficiency of edge computing, but also provide with a systematic basis for intelligent decision-making in the edge environment. The given framework is able to mitigate the latencies of data processing while fully utilizing available resources with a series of experiments and performance evaluations.

Keywords

Real-time data processingEdge computingContext-awarenessPriority schedulingFrameworkLatencyEfficiency and contextual information

Subject Classifications

94A6020C0520C07

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