Read disturb is a circuit-level noise in high-density solid-state drives (SSDs), which may corrupt existing data in SSD blocks and then cause high read error rates. The approach of read reclaim (RR) is commonly used to avoid read disturb errors by migrating the valid data pages to other free blocks for resetting the negative effects of read disturbs, but it affects both I/O responsiveness and SSD lifetime. This article proposes SWEEP to minimize the number of RR operations. Specifically, it gathers hot read and hot write data pages together, and saves them in a portion of designated blocks. After that, the hot write data pages will be invalidated and the hot read data pages in such blocks are likely to be migrated to other blocks through garbage collection (GC). Consequently, the read disturbs on these hot read data pages will be reset, without additional RR operations. Trace-driven simulation experiments show that our proposal can significantly reduce the number of RR operations by between 1.6 % and 74.4 %, which contributes to a maximum reduction of 29.7 % on total erase operations, indicating a better lifetime of SSD devices. In addition, it can reduce the overall I/O response time by up to 52.9 %, compared to existing optimization schemes for SSDs.

Modern solid state drives (SSDs) are composed of a four-level parallel structure, including channels, chips, dies, and planes, to enhance SSD performance with maximum access parallelism. Because the planes within the same die share the same set of control units and peripheral circuits, it generally has to open multiple aligned blocks to enable access parallelism through multi-plane (MP) operations. Such a passive method, however, cannot effectively exploit plane level parallelism, since MP operations can only be triggered when the accessed data pages have the same offset address across the planes. In addition, it will worsen the block open time issue, as multiple aligned blocks are opened to enable MP operations for simultaneous data writing. This, in turn, increases the error rate when reading data from blocks with long open time. This paper introduces SetMP, a novel approach that proactively aggregates requests to exploit plane level parallelism through set associative management. By increasing the frequency of MP operations, SetMP enhances I/O responsiveness while reducing the open time of block associated with maintaining multiple open blocks for MP operations. Evaluation results demonstrate that SetMP achieves an average reduction in I/O latency of 16.9%, without significantly increasing the open time of block, outperforming existing optimization schemes.

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose ajointcomputation offloading,caching,communication resource allocation,computation resource allocation, and UAV trajectorycontrolapproach (J$\rm{C}^{5}$A). Specifically, J$\rm{C}^{5}$A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of J$\rm{C}^{5}$A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms.

Mobile robot navigation in a human-populated environment has been of great interest to the research community in recent years, referred to as crowd navigation. Currently, offline reinforcement learning (RL)-based method has been introduced to this domain, for its ability to alleviate the sim2real gap brought by online RL which relies on simulators to execute training, and its scalability to use the same dataset to train for differently customized rewards. However, the performance of the navigation policy suffered from the distributional shift between the training data and the input during deployment, since when it gets an input out of the training data distribution, the learned policy has the risk of choosing an erroneous action that leads to catastrophic failure such as colliding with a human. To realize risk sensitivity and improve the safety of the offline RL agent during deployment, this work proposes a multipolicy control framework that combines offline RL navigation policy with a risk detector and a force-based risk-avoiding policy. In particular, a Lyapunov density model is learned using the latent feature of the offline RL policy and works as a risk detector to switch the control to the risk-avoiding policy when the robot has a tendency to go out of the area supported by the training data. Experimental results showed that the proposed method was able to learn navigation in a crowded scene from the offline trajectory dataset and the risk detector substantially reduces the collision rate of the vanilla offline RL agent while maintaining the navigation efficiency outperforming the state-of-the-art methods.