Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

Summary Edge computing is a new computing paradigm that performs data processing at the edge of the network (ie, edge servers) to lower data processing latency. Prior research significantly focused on offloading tasks from terminals to edge servers, yet most ignored how to store task's necessary data (such as databases and pretrained machine‐learning models) on edge servers. Today, as data‐intensive tasks such as deep learning and augmented reality become common, large data storage and powerful computation resources are needed. This is a cumbersome challenge, because many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, then it needs to offload the task to cloud datacenters or download the necessary data from the cloud. Either case could increase data processing latency. To address this problem, this paper proposes an edge‐side collaborative storage framework called Edge‐side Cooperative Storage (ECS). In ECS, edge servers collaboratively store and process data‐intensive tasks's necessary data. Here, we particularly focus on how to effectively place data on ECS, using an approach that differs from existing works (that model data placement problems as linear/integer programming problems). Our work models cooperative storage as a graph and solves the data placement problem by using a graph‐based iterative algorithm. This algorithm easily extends to a distributed version, so distributed ECSworks efficiently without a centralized scheduler. We also evaluate ECS's effectiveness and convergence through simulations. Simulation results show that ECSis 2× better than a traditional nonshared storage framework in terms of the cache hit rate.