Write-aware data allocation on heterogeneous memory architecture with minimum cost

More and more Non-Volatile Memories (NVM) have been widely applied to various embedded systems to build the heterogeneous memory architecture. However, the write-endurance of NVM remains a great challenge. Hence, we should take full consideration of the write-endurance of NVM when allocating data on heterogeneous memory architecture. There is an observation that, for most real workloads, about 10% of data account for 90% write operations. This brings us an opportunity to reduce the write wear of NVM through carefully allocating write-intensive data. In this paper, we explore the problem that how to find a balance between the system cost and write-endurance of NVM for data allocation on heterogeneous memory architecture. We propose a write-aware data allocation algorithm, WADA. WADA can not only greatly reduce the write wear of NVM, but also guarantee the near-optimal system cost. We also propose an integer linear programming (ILP) model to generate an optimal data allocation, which can obtain the minimum cost. The result of ILP can be used as a standard to evaluate the efficiency of other algorithms. Experiments show that WADA outperforms all the other algorithms on both system cost and write wear of NVM. Compared to previous algorithms, WADA can reduce up to 47.77% system cost and 60.89% write wear of NVM. Compared to ILP, WADA can achieve the near-optimal system cost within just 2% difference.