Pacaca: Mining Object Correlations and Parallelism for Enhancing User Experience with Cloud Storage

Object-based cloud storage presents an unconventional storage model. Exploiting its unique characteristics, such as the strong semantic correlations among objects and the high I/O parallelism potential, can greatly enhance user experience. Unfortunately, current storage optimization techniques, such as the caching and prefetching schemes, are designed for conventional storage and thus are sub-optimal for cloud storage services. In this paper, we propose a client-side cache management framework, called Pacaca, which integrates object clustering, parallelized prefetching, and cost-aware caching to exploit I/O parallelism and object correlations on cloud storage. We first develop an efficient mining scheme, called Frequent Cluster Mining (FCM), to discover object correlations from the access sequence, and then build a prefetching scheme to fetch the correlated objects in parallel. These two schemes are closely coordinated for achieving high prefetching accuracy, proper control on parallelism degree, and effective mis-prefetching detection and handling. After studying the impact of parallelized prefetching on cache management, we further present a cost-aware caching scheme to differentiate low-cost and high-cost objects for efficient caching by leveraging the awareness of parallelism and object correlations. Our experimental results show that our optimization schemes can effectively reduce the access latency, outperforming traditional schemes by up to 58%.