Predicting cache needs and cache sensitivity for applications in cloud computing on CMP servers with configurable caches

QoS criteria in cloud computing require guarantees about application runtimes, even if CMP servers are shared among multiple parallel or serial applications. Performance of computation-intensive application depends significantly on memory performance and especially cache performance. Recent trends are toward configurable caches that can dynamically partition the cache among cores. Then, proper cache partitioning should consider the applications' different cache needs and their sensitivity towards insufficient cache space. We present a simple, yet effective and therefore practically feasible black-box model that describes application performance in dependence on allocated cache size and only needs three descriptive parameters. Learning these parameters can therefore be done with very few sample points. We demonstrate with the SPEC benchmarks that the model adequately describes application behavior and that curve fitting can accomplish very high accuracy, with mean relative error of 2.8% and maximum relative error of 17%.