Collaborative Edge Data Caching In Online

Abstract

In an edge computing (EC) environment, edge servers located at base points supply nearby app handlers with highly available figuring and storing data. From the app vendor's perspective, reserving data on edge servers can reduce the time it takes for app users to retrieve data. On the other hand, because of its limited scope, an edge server typically has insufficient data. This work seeks to lower system costs associated with information migration, fact caching, and other costs by resolving the collaborative caching conundrum in the EC environment. An NP-complete problem with a limited optimization statement is the collaborative edge data caching problem (CEDC). OCEDC is an operational algorithm for resolving the CEDC issue during all specified window times. OCEDC is an operational algorithm for solving the CEDC problem in all time slots suggested. OCEDC is based on Lyapunov boost and provides verifiable near-optimal results while operating online without the need for future data. OCEDC is tested on a real-world dataset, and the findings show that it outdoes quarter sampling alternatives by a significant margin.