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

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.