Achieving Load-Balanced, Redundancy-Free Cluster Caching with Selective Partition

Data-intensive clusters increasingly rely on in-memory storages to improve I/O performance. However, the routinely observed file popularity skew and load imbalance create hot spots, which significantly degrade the benefits of in- memory caching. Common approaches to tame load imbalance include copying multiple replicas of hot files and creating parity chunks using storage codes. Yet, these techniques either suffer from high memory overhead due to cache redundancy or incur non-trivial encoding/decoding complexity. In this paper, we propose an effective approach to achieve load balancing without cache redundancy or encoding/decoding overhead. Our solution, termed SP-Cache,
selectively partitions
files based on the loads they contribute and evenly caches those partitions across the cluster. We develop an efficient algorithm to determine the optimal number of partitions for a hot file—too few partitions are incapable of mitigating hot spots, while too many are susceptible to stragglers. We have implemented SP-Cache atop Alluxio, a popular in-memory distributed storage system, and evaluated its performance through EC2 deployment and trace-driven simulations. SP-Cache can quickly react to the changing load by dynamically re-balancing cache servers. Compared to the state-of-the-art solution, SP-Cache reduces the file access latency by up to 40 percent in both the mean and the tail, using 40 percent less memory.