A new memory monitoring scheme for memory-aware scheduling and partitioning

We propose a low overhead, online memory monitoring scheme utilizing a set of novel hardware counters. The counters indicate the marginal gain in cache hits as the size of the cache is increased, which gives the cache miss-rate as a function of cache size. Using the counters, we describe a scheme that enables an accurate estimate of the isolated miss-rates of each process as a function of cache size under the standard LRU replacement policy. This information can be used to schedule jobs or to partition the cache to minimize the overall miss-rate. The data collected by the monitors can also be used by an analytical model of cache and memory behavior to produce a more accurate overall miss-rate for the collection of processes sharing a cache in both time and space. This overall miss-rate can be used to improve scheduling and partitioning schemes.