@inproceedings{9d08ee964ebd4928a4f493daa00b164c,
title = "Work-in-Progress: Maximizing I/O throughput and Minimizing Performance Variation via Reinforcement Learning based I/O Merging for SSDs",
abstract = "Merge technique is widely adopted by I/O schedulers to maximize system I/O throughput. However merging operation could degrade the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged I/O variations of I/O requests. In this case, the requirement of QoS (Quality of Service) performance will be violated. In order to improve QoS performance meanwhile providing high I/O throughput, this paper proposed a Reinforcement Learning based I/O merge approach. Through learning the characteristic of various I/O patterns, the proposed approach make merge decisions adaptive to different I/O workloads.",
keywords = "I/O scheduler, Merge technique, performance variation, Reinforcement Learning, throughput, worst-case latency",
author = "Chao Wu and Cheng Ji and Qiao Li and Chenchen Fu and Xue, {Chun Jason}",
year = "2018",
month = sep,
doi = "10.1109/CASES.2018.8516832",
language = "English",
isbn = "978-1-5386-5564-1",
series = "2018 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2018",
publisher = "IEEE",
booktitle = "2018 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES)",
address = "United States",
note = "2018 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2018 ; Conference date: 30-09-2018 Through 05-10-2018",
}