Energy- and Cost-Oriented Optimization of Hybrid Coded Storage in Edge Caching Systems

As the size of data clusters grows, the issues of storage redundancy, data availability, and the cost of edge caching systems become the focus of attention. Although the multi-copy strategy improves data availability, its redundancy cost is high and does not fully consider file data characteristics. Erasure codes techniques have been widely studied as an alternative, but the selection of a suitable coding scheme remains a challenge. To this end, we propose a hybrid coding caching scheme. Firstly, we predict data types using machine learning methods to ensure that the most valuable content is stored. Secondly, for the characteristics of the multi-copy strategy, low-density parity check (LDPC), and Reed-Solomon coding (RS), we propose an adaptive data partitioning strategy (ADPRF) to select appropriate coding schemes for different data. Finally, by combining the ideas of dynamic programming and heuristic algorithms, we propose a redundancy-aware edge collaborative caching algorithm (RPCO) with joint optimization of energy and cost to determine the optimal set of collaborative nodes and the optimal cache locations for files. Compared with the traditional responsive caching scheme, this algorithm can reduce the system cost by 13.8% and access latency by 11.5%.