EDIndex: Enabling Fast Data Queries in Edge Storage Systems

In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.