Edge computing, as a new computing paradigm, brings cloud computing's computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constitute edge storage systems (ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this article, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel Erasure Coding based Edge Data Placement (EC-EDP) problem as an integer linear programming problem and prove its $\mathcal {NP}$NP-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

Mobile edge computing (MEC) extends cloud computing by deploying edge servers with computing and storage resources at base stations within users’ geographic proximity. The networked edge servers in an area constitute an edge storage system (ESS), where edge servers cooperate to provide services for the users in the area. However, the potential of ESSs is challenged by edge servers’ constrained storage resources due to their limited physical sizes. A straightforward method to tackle this challenge is to reduce data redundancy in the ESS. The unique characteristics and constraints in the MEC environment, e.g., edge servers’ geographic coverage and distribution, render conventional data deduplication techniques designed for cloud storage systems obsolete. In this paper, we make the first attempt to study this novel Edge Data Deduplication (EDDE) problem. First, we model it as a constrained optimization problem with the aim to maximize data deduplication ratio under latency constraint by taking advantage of the collaboration between edge servers. Then, we prove that the EDDE problem is \(\mathcal {NP}\)-hard and propose an approach named EDDE-O for solving the EDDE problem optimally based on integer programming. To accommodate large-scale EDDE scenarios, we propose a \(ln\alpha +1\)-approximation algorithm, namely EDDE-A, to find sub-optimal EDDE solutions efficiently. The results of extensive experiments conducted on a widely-used dataset demonstrate that EDDE-O and EDDE-A can solve the EDDE problem effectively and efficiently, outperforming four representative approaches significantly.