The reuse distance (least recently used (LRU) stack distance) is an essential metric for performance prediction and optimization of storage cache. Over the past four decades, there have been steady improvements in the algorithmic efficiency of reuse distance measurement. This progress is accelerating in recent years, both in theory and practical implementation. In this article, we present a kinetic model of LRU cache memory, based on the average eviction time (AET) of the cached data. The AET model enables fast measurement and use of low-cost sampling. It can produce the miss ratio curve in linear time with extremely low space costs. On storage trace benchmarks, AET reduces the time and space costs compared to former techniques. Furthermore, AET is a composable model that can characterize shared cache behavior through sampling and modeling individual programs or traces.

The in-memory cache system is a performance-critical layer in today's web server architectures. Memcached is one of the most effective, representative, and prevalent among such systems. An important problem is on its memory allocation. The default design does not make the best use of the memory. It is unable to adapt when the demand changes, a problem known as slab calcification. This paper introduces locality-aware memory allocation (LAMA), which addresses the problem by first analyzing the locality of Memcached's requests and then reassigning slabs to minimize the miss ratio or the average response time. By evaluating LAMA using various industry and academic workloads, the paper shows that LAMA outperforms existing techniques in the steady-state performance, the speed of convergence, and the ability to adapt to request pattern changes, and overcome slab calcification. The new solution is close to optimal, achieving over 98 percent of the theoretical potential. Furthermore, LAMA can also be adopted in resource partitioning to guarantee quality-of-service (QoS).

On multi-core processors, applications are run sharing the cache. This paper presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called footprint symbiosis to obtain the best shared cache performance underfair CPU allocation as well as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slower than the best co-run solutions found with exhaustive testing.

On multicore processors, applications are run sharing the cache. This paper presents online optimization to collocate applications to minimize cache interference to maximize performance. The paper formulates the optimization problem and solution, presents a new sampling technique for locality analysis and evaluates it in an exhaustive test of 12,870 cases. For locality analysis, previous sampling was two orders of magnitude faster than full-trace analysis. The new sampling reduces the cost by another two orders of magnitude. The best prior work improves co-run performance by 56% on average. The new optimization improves it by another 29%. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5% of the best possible performance.