MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction

In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies. We formulate the page request prediction problem as a categorical time series forecasting task. Then, our method queries the learned page request forecaster to obtain the next $k$ predicted page memory references to better approximate the optimal B\'el\'ady's replacement algorithm. We implement several forecasting techniques using advanced deep learning architectures and integrate the best-performing one into an existing open-source cache simulator. Experiments run on benchmark datasets show that MUSTACHE outperforms the best page replacement heuristic (i.e., exact LRU), improving the cache hit ratio by 1.9% and reducing the number of reads/writes required to handle cache misses by 18.4% and 10.3%

Memory Page Stability and its Application to Memory Deduplication

In virtualized environments, typically cloud computing environments, multiple virtual machines run on the same physical host. These virtual machines usually run the same operating systems and applications. This results in a lot of duplicate data blocks in memory. Memory deduplication is a memory optimization technique that attempts to remove this redundancy by storing one copy of these duplicate blocks in the machine memory which in turn results in a better utilization of the available memory capacity.In this dissertation, we characterize the nature of memory pages that contribute to memory deduplication techniques. We show how such characterization can give useful insights towards better design and implementation of software and hardware-assisted memory deduplication systems. In addition, we also quantify the performance impact of different memory deduplication techniques and show that even though memory deduplication allows for a better cache hierarchy performance, there is a performance overhead associated with copy-on-write exceptions that is associated with diverging pages.We propose a generic prediction framework that is capable of predicting the stability of memory pages based on the page flags available through the Linux kernel. We evaluate the proposed prediction framework and then discuss various applications that can benefit from it, specifically memory deduplication and live migration

On the classification and evaluation of prefetching schemes

Abstract available: p. [2