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Distributed caches are widely deployed to serve social networks and web applications at billion-user scales. This paper presents Cache-on-Track (CoT), a decentralized, elastic, and predictive caching framework for cloud environments. CoT proposes a new cache replacement policy specifically tailored for small front-end caches that serve skewed workloads with small update percentage. Small front-end caches are mainly used to mitigate the load-imbalance across servers in the distributed caching layer. Front-end servers use a heavy hitter tracking algorithm to continuously track the top-k hot keys. CoT dynamically caches the top-C hot keys out of the tracked keys. CoT’s main advantage over other replacement policies is its ability to dynamically adapt its tracker and cache sizes in response to workload distribution changes. Our experiments show that CoT’s replacement policy consistently outperforms the hit-rates of LRU, LFU, and ARC for the same cache size on different skewed workloads. Also, CoT slightly outperforms the hit-rate of LRU-2 when both policies are configured with the same tracking (history) size. CoT achieves server size load-balance with 50% to 93.75% less front-end cache in comparison to other replacement policies. Finally, experiments show that CoT’s resizing algorithm successfully auto-configures the tracker and cache sizes to achieve back-end load-balance in the presence of workload distribution changes.