CEGraph: Cache-Efficient Management for Streaming Graph Processing

Efficient processing of streaming graphs is crucial to improve system performance. Due to the highly irregular and frequent access to data in streaming graph processing, existing cache management methods are difficult to accurately predict cache behavior, resulting in serious cache misses. To address the issues, we propose CEGraph, an efficient cache management approach for streaming graph processing. Specifically, for graph data, we propose a cache replacement policy based on vertex importance. This policy accurately evaluates the importance of vertices in the incremental processing of streaming graphs from our proposed three factors: 1) the association degree of affected state of a vertex; 2) the path distance of a vertex; and 3) whether a vertex will be updated. Vertices with high importance are identified and kept in the cache to reduce cache thrashing. Experimental results reveal that compared with LRU, DRRIP and Grasp, CEGraph reduces the last-level cache (LLC) misses by an average of 22.93% (maximum 34.27%), 20.87% and 11.91%, respectively. Compared with the state-of-the-art cache management method P-OPT, CEGraph reduces the LLC misses by 6.46% on average, therefore demonstrating the effectiveness of CEGraph.