Toward high-performance key-value stores through GPU encoding and locality-aware encoding.

Although distributed key-value store is becoming increasingly popular in compensating the conventional distributed file systems, it is often criticized due to its costly full-size replication for high availability that causes high I/O overhead. This paper presents two techniques to mitigate such I/O overhead and improve key-value store performance: GPU encoding and locality-aware encoding. Instead of migrating full-size replicas over the network, we split the original file into smaller chunks and encode them with a few additional parity codes using GPUs before dispersing them onto remote nodes. The parity code is usually much smaller than the original file, which saves the extra space required for high availability and reduces the I/O overhead. Meanwhile, the compute-intensive encoding process is largely accelerated by the massive number of GPU cores. Yet, splitting the original file into smaller chunks stored on multiple nodes breaks data locality from application's perspective. To this end, we present a locality-aware encoding mechanism that allows a job to be dispatched as finer-grained tasks right on the node where the required chunk resides. Therefore, the data locality is preserved at the finer granularity of sub-job (i.e., task) level. We conduct an in-depth analysis of the proposed approach and implement a system prototype named Gest. Gest has been deployed and evaluated on a variety of testbeds demonstrating that high data availability, high space efficiency, and high I/O performance could be collectively achieved at the same time.