Abstract
Due to the complexity of the cloud environment, the optimization of cloud cache has become a research hotspot. Most of the current research focuses on cloud cache replacement and prefetching. However, these methods do not take into account that the size of the cache in cloud scenarios is much smaller than the size of the workload, and an effective cache replacement algorithm does not improve the performance of the cache very well. To address these challenges, we design a cache admission policy for cloud block storage using deep reinforcement learning. By analyzing the request characteristics of the workload, three types of request-related features are constructed, which can accurately mine the current request, request history, and current cache state. Experimental results show that, compared with the industry widely deployed cache algorithm, our method decreases write to cloud cache by 115.12%, while improving the hit rate.
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Long, F. (2022). A Cache Admission Policy for Cloud Block Storage Using Deep Reinforcement Learning. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_46
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DOI: https://doi.org/10.1007/978-981-19-3927-3_46
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