Improving Memory Access Performance of In-Memory Key-Value Store Using Data Prefetching Techniques

Abstract

In-memory Key-Value stores (IMKVs) provide significantly higher performance than traditional disk-based counterparts. As memory technologies advance, IMKVs become practical for modern Big Data processing, which include financial services, e-commerce, telecommunication network, etc. Recently, various IMKVs have been proposed from both academia and industrial. In order to leverage high performance random access capability of main memory, most IMKVs employ hashing based index structures to retrieve data according to keys. Consequently, a regular memory access pattern can be observed in data retrieval from those IMKVs. Normally speaking, one access to index (hash table), which is also located in main memory, is followed by another memory access to value data. Such a regular access pattern provides a potential opportunity that data prefetching techniques can be employed to improve memory access efficiency for data retrieval in these IMKVs. Based on this observation, we explore various data prefetching techniques with proper architecture level modifications on memory controller considering trade-off between design overhead and performance. Specifically, we focus on two key design issues of prefetching techniques: (1) where to fetch data (i.e. data address)? and (2) how many data to fetch (i.e. data size)? Experimental results demonstrate that memory access performance can be substantially improved up to 35.4 %. In addition, we also demonstrate the overhead of prefetching on power consumption.