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The concept of caching is a fundamental feature in modern computing architectures and has no doubt, found wide applications in diverse areas. Performance evaluation of systems is functionally related to how caching is implemented on a given computing platform, a metric influenced by the cache replacement policy. This paper describes an online learning-induced, self-adjusting cache management strategy with low overhead and scan-resistant characteristics that outperforms the LRU replacement algorithm using adaptation to balance between workload frequency and recency patterns.
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