An efficient cache management algorithm for streaming workloads
International Journal of Networking and Virtual Organisations, 2020•inderscienceonline.com
In this paper, we present a novel cache management algorithm for real-world streaming
workloads. Streaming workloads are believed to exhibit very large and sequential access
patterns, which has been the main consideration in designing media caching algorithms.
However, legacy caching algorithms do not fully utilise fine-grained access patterns of
streaming workloads and also tend to ignore human interactivity. In this paper, we present
the least expectation first (LEF) algorithm, which manages a large number of block caches …
workloads. Streaming workloads are believed to exhibit very large and sequential access
patterns, which has been the main consideration in designing media caching algorithms.
However, legacy caching algorithms do not fully utilise fine-grained access patterns of
streaming workloads and also tend to ignore human interactivity. In this paper, we present
the least expectation first (LEF) algorithm, which manages a large number of block caches …
In this paper, we present a novel cache management algorithm for real-world streaming workloads. Streaming workloads are believed to exhibit very large and sequential access patterns, which has been the main consideration in designing media caching algorithms. However, legacy caching algorithms do not fully utilise fine-grained access patterns of streaming workloads and also tend to ignore human interactivity. In this paper, we present the least expectation first (LEF) algorithm, which manages a large number of block caches as two-level grouping. Specifically, we select caching and eviction targets based on the expected gain of the cached data blocks, thereby improving the cache hit ratio significantly. Experimental results show that the proposed algorithm performs better than well-known interval caching and LRU algorithms with respect to the hit ratio and the I/O bandwidth.
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