Minimum-cost data allocation with guaranteed probability on multiple types of memory

As the advance of memory technologies, multiple types of memory such as different kinds of non-volatile memory (NVM), SRAM, DRAM, etc. provide a flexible configuration considering performance, energy and cost. For improving the performance of systems with multiple types of memory, data allocation is one of the most important tasks. The previous studies on data allocation problem assume the worst (fixed) case of data-access frequencies. However, the data allocation produced by employing worst case usually leads to an inferior performance for most of time. In this paper, we model this problem by probabilities and design efficient algorithms that can give optimal-cost data allocation with a guaranteed probability. The proposed DAGP algorithm produces a set of feasible data allocation solutions which generates the minimum access time or cost guaranteed by a given probability. The experiments show that our technique can significantly reduce the access time or cost compared with the technique considering worst case scenario. For example, comparing with the optimal result generated by employing the worst cases, our technique can reduce memory access time by 10.35% on average when guaranteed probability is set to be 0.8. Moreover, for 80 percents of cases, memory access time is reduced by 23.98% on average.