Owing to the increased need for machine learning and artificial intelligence in current cloud computing systems, the amount of data that needs to be processed has exponentially increased. Thus, it is important to optimize memory and storage systems to reduce the energy consumption and execution time of applications. This paper proposes a new Q-learning-based prefetching algorithm for DRAM–NAND flash hybrid main memory architecture. To minimize the computational overheads of learning-based schemes, we have designed two learning policies, namely aggressive learning and lazy learning. The proposed system reduces the energy consumption by about 80% of the memory and storage for Redis, OpenStack Swift which is a cloud computing open source framework and Apache Storm workloads. Further, the overall execution time of workloads in cloud computing applications is reduced by almost half. Using a path generator with a Q-learning-based prefetching algorithm, we realize an increased hit rate of about 21% compared to that with a no-prefetching system, compared to non-prefetching system.
Bibliographical noteFunding Information:
Acknowledgements This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT Future Planning (NRF-2015M3C4A7065522).
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture