Locality-Aware Replacement Algorithm in Flash Memory to Optimize Cloud Computing for Smart Factory of Industry 4.0

Cloud computing platform is one of the most important parts in the smart factory of industry 4.0. Currently, most cloud computing platforms have adopted flash memory as the mainly storage for more effciency, because the flash memory having high capacity and speed. However, flash memory exhibits certain drawbacks in terms of out-of-place updates and asymmetric I/O latencies for read, write, and erase operations. These disadvantages prevent replacing traditional disks. Fortunately, the flash buffer can be used to address these drawbacks, and its replacement policies provide efficiency methods. Therefore, in this paper, we propose a locality-aware least recently used (LLRU) replacement algorithm, which exploits both access and locality characteristics. LLRU divides the LRU list into four lists: the hot-clean, hot-dirty, cold-clean, and cold-dirty LRU lists. According to reuse probability and eviction cost, the eviction page is selected to ensure effective system performance for cloud computing. The experimental results demonstrate LLRU outperforms other algorithms, including LRU, CF-LRU, LRU-WSR, and AD-LRU, which can optimize cloud computing for smart factory of industry 4.0.