Fogcached: A DRAM/NVMM hybrid KVS server for edge computing

Kouki Ozawa, Takahiro Hirofuchi, Ryousei Takano, Midori Sugaya

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


With the development of IoT devices and sensors, edge computing is leading towards new services like autonomous cars and smart cities. Low-latency data access is an essential requirement for such services, and a large-capacity cache server is needed on the edge side. However, it is not realistic to build a large capacity cache server using only DRAM because DRAM is expensive and consumes substantially large power. A hybrid main memory system is promising to address this issue, in which main memory consists of DRAM and non-volatile memory. It achieves a large capacity of main memory within the power supply capabilities of current servers. In this paper, we propose Fogcached, that is, the extension of a widely-used KVS (Key-Value Store) server program (i.e., Memcached) to exploit both DRAM and non-volatile main memory (NVMM). We used Intel Optane DCPM as NVMM for its prototype. Fogcached implements a Dual-LRU (Least Recently Used) mechanism that seamlessly extends the memory management of Memcached to hybrid main memory. Fogcached reuses the segmented LRU of Memcached to manage cached objects in DRAM, adds another segmented LRU for those in DCPM and bridges the LRUs by a mechanism to automatically replace cached objects between DRAM and DCPM. Cached objects are autonomously moved between the two memory devices according to their access frequencies. Through experiments, we confirmed that Fogcached improved the peak value of a latency distribution by about 40% compared to Memcached.

Original languageEnglish
Pages (from-to)2089-2096
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2021


  • DCPM
  • Dual-LRU
  • Edge
  • Edge computing
  • Fogcached
  • KVS
  • Key-Value-Store
  • Middleware
  • NVM
  • NVMM

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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