Memory carousel: LLVM-based bitwise wear leveling for nonvolatile main memory

Emerging nonvolatile memory yields, alongside many advantages, technical shortcomings, such as reduced cell lifetime. Although many wear-leveling approaches exist to extend the lifetime of such memories, usually a tradeoff for the granularity of wear leveling has to be made. Due to iterative write schemes (repeatedly sense and write), wear out of memory in certain systems is directly dependent on the written bit value and thus can be highly imbalanced, requiring dedicated bit-wise wear leveling. Such a bit-wise wear leveling so far has only be proposed together with a special hardware support. However, if no dedicated hardware solutions are available, especially for commercial off-the-shelf systems with nonvolatile memories, a software solution can be crucial for the system lifetime. In this work, we propose entirely software-based bit-wise wear leveling, where the position of bits within CPU words in the main memory is rotated on a regular basis. We leverage the LLVM intermediate representation to adjust load and store operations of the application with a custom compiler pass. Experimental evaluation shows that the lifetime by applying local rotation within the CPU word can be extended by a factor of up to 21× . We also show that our method can incorporate with coarser-grained wear leveling, e.g., on block granularity and assist achievement of higher lifetime improvements

Active Data Replica Recovery for Quality-Assurance Big Data Analysis in IC-IoT

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time