Lyapunov optimization

Business

Computer network

Lyapunov redesign

Latency (audio)

Server

Operating system

Algorithm

Distributed computing

Enhanced Data Rates for GSM Evolution

Marketing

Optimization problem

Edge device

Artificial intelligence

Lyapunov exponent

Online algorithm

Chaotic

Vendor

Edge computing

Computer science

Quality of service

Cloud computing

Telecommunications

IEEE Transactions on Parallel and Distributed Systems

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users' retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is NP-complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

Online Collaborative Data Caching in Edge Computing