Applying Software-based Memory Error Correction for In-Memory Key-Value Store: Case Studies on Memcached and RAMCloud

Abstract

With the nature of being memory hungry, in-memory key-value store is fundamentally subject to very high memory cost and energy consumption. Intuitively, the availability of a strong memory error correction at sufficiently small redundancy overhead could be leveraged to reduce memory cost and/or energy consumption. Nevertheless, current computing systems handle memory error correction solely in the hardware stack with very weak error correction strength. This paper for the first time studies the practical feasibility of implementing strong memory error correction code (ECC) in the software stack for in-memory key-value store without incurring significant speed performance penalty. This is fundamentally enabled by the low memory bandwidth utilization and relatively simple data structure of in-memory key-value store, which are actually shared with many other datacenter applications (e.g., Web search). This paper presents several design techniques to optimize software-based ECC implementation for in-memory key-value store, and elaborates on several important design issues. Using Memcached and RAMCloud as test vehicles, this work shows that the proposed design solution can improve the memory error correction strength by several orders of magnitude at similar (and even less) coding redundancy compared with current hardware-based design practice, and meanwhile incur less than 6% degradation of in-memory key-value store operational throughput.