ACME: Adaptive Caching Using Multiple Experts

Abstract

The gap between CPU speeds and the speed of the technologies provid-ing the data is increasing. As a result, latency and bandwidth to needed data is limited by the performance of the storage devices and the networks that connect them to the CPU. Distributed caching techniques are often used to reduce the penalties associated with such caching; however, such techniques need further development to be truly integrated into the network. This pa-per describes the preliminary design of an adaptive caching scheme using multiple experts, called ACME. ACME is used to manage the replacement policies within distributed caches to further improve the hit rates over static caching techniques. We propose the use of machine learning algorithms to rate and select the current best policies or mixtures of policies via weight updates based on their recent success, allowing each adaptive cache node to tune itself based on the workload it observes. Since no cache databases or synchronization messages are exchanged for adaptivity, the clusters com-posed of these nodes will be scalable and manageable. We show that static techniques are suboptimal when combined in networks of caches, providing potential for adaptivity to improve performance.