Emerging persistent memory (PM) can provide large persistent capacity with performance comparable to DRAM in modern memory systems. Persistent transactional memory (PTM) needs to ensure data consistency after unexpected power loss or crashes. Therefore, crash consistency strategies, such as persistent logging, are still required. However, the additional overhead introduced by these strategies, such as significant extra writes on PM, can lead to system performance degradation. In this article, we propose CodePM, a fault-tolerant PM transactional library that utilizes parity-based crash consistency to remove logging overhead while guaranteeing the correct state of data. CodePM reuses the decoding capability of parity to detect and recover inconsistent objects. To ensure consistency without logs when updating, CodePM employs fine-grained memory fences to carefully align potential inconsistency with the repairability of parity. To detect inconsistency without logs when recovering, CodePM utilizes optimistic speculative scanning recovery by reusing checksum and parity, which supports instant recovery with transient degraded reliability. Moreover, we study the memory fence blocking effects and further augment CodePM with pipelined encoding and persistent writing to hide update latency. We implemented CodePM on Pangolin, the state-of-the-art parity-based PTM for fault-tolerance. Evaluation results with real-world workloads on Intel Optane DCPMM show that CodePM can achieve up to$3.4\times $higher throughput than Pangolin.

This article investigates optimal consensus problems of switching multiagent systems (MASs). For such kind of MASs, local neighborhood tracking error (LNTE) system is time varying because of the switching communication graph (CG). Existing performance index defined on the LNTE system is thus invalid for the switching MASs. This article addresses this problem by defining a new augmented LNTE system. The augmented LNTE system is constructed using the joint CG and is thus time-invariant. Subsequently, the optimal consensus problems for the MASs are formulated using the augmented LNTE system. Value iteration algorithm that employs an actor-critic network is used to learn the optimal controller. The article provides a theoretical analysis demonstrating the learning stability and control stability of the value iteration method. Furthermore, we also show that the MASs will reach approximate Nash equilibrium. Simulation results proves the effectiveness of the proposed method.

As the storage and I/O demand of contemporary computer systems grow, SSDs with deduplication are widely deployed as shared storage devices to provide high performance in cloud platforms where diverse tenant workloads are collocated. However, existing global and fixed fingerprint management schemes are inefficient in the multi-tenant environment. Contention for in-memory fingerprint cache, embedded in DRAM to speed up fingerprint lookups, results in decreased cache utilization, and abundant fingerprint lookups into the backend flash memory degrade the system performance. Besides, a fixed cache replacement policy for all the tenants fails to capture the diverse characteristics, further increasing the lookup overhead.This paper introduces CostFM, a novel fingerprint management mechanism highlighted by two notable features. First, it provides a benefit-aware cache allocation scheme, which estimates the cache hit gains of each tenant and uses dynamic programming to find the optimal cache allocation scheme, avoiding the cache contention for the fingerprint resource and improving cache utilization. Second, it applies a user-based policy model that captures access characteristics to select the suitable cache management policy for each tenant, boosting overall cache efficiency. Extensive experimental results show that CostFM decreases the average request latency by up to 30%, and it also reduces the fingerprint writes into the flash memory by up to 4.7x.

Data deduplication is widely deployed in solid-state drives (SSDs) to improve the storage space utilization and alleviate the endurance issue. However, deduplication increases the degree of fragmentation and the access contention, significantly hurting the system performance. First, the fragmentation at the storage medium level in SSD is closely related to the internal parallelism, and the degree of fragmentation increases as the degree of the parallelism decreases. Deduplication removes the duplicate parts of the write sequences, decreasing the read parallelism. Thus, the degree of fragmentation is increased, eventually degrading the read performance. Second, the uneven distribution of the highly referenced data increases the access contention. This increased access contention prolongs the queueing time, further degrading the system performance. Motivated by our observations, we propose an elastic data cache (EDC) to improve the I/O performance in the deduplicated SSD. EDC redesigns the built-in DRAM-based data cache, tracks the popular and highly-referenced data, and maintains them in the cache. To fulfill the novel data cache, EDC changes the request process. The read requests that access the fragments in the flash memory are mostly performed in the fast-speed data cache, alleviating the negative impact of fragmentation on the read performance. The reduced read accesses to the flash memory also ease the access contention, which improves the system performance significantly. Extensive experimental results validate the efficiency of EDC, showing that it effectively improves the read performance and the write performance by up to 79% and 85% on average, respectively, in the deduplicated SSD.

This paper investigates the consensus problems of heterogeneous discrete-time (DT) nonlinear multi-agent systems (MASs) with unknown dynamics and switching typology. By adding a virtual model to each agent and using the model reference protocol to the actual model, the consensus problems of unknown nonlinear MASs are transformed into consensus problems of known virtual linear MASs, and a distributed control law is designed for the virtual model to make the virtual linear MASs achieve consensus under the switching typology. Two numerical simulations with nonidentical nonlinear dynamics and switching typology are given to prove the effectiveness of the proposed method.

Emerging Non-Volatile Memory (NVM) techniques suffer from many attacks on endurance and data confidentiality. Oblivious RAM (ORAM) can prevent both types of attacks by address obfuscation and encryption. However, ORAM also introduces a lot of redundant memory operations, worsening the NVM endurance and system performance. Furthermore, the slower NVM increases the response latency of target blocks compared to DRAM. As the read requests are on the critical path of program execution, the increased latency further reduces system performance. To alleviate endurance and performance problems, we propose H2ORAM, a low response latency optimized ORAM scheme for NVM. Hybrid DRAM/NVM system is introduced to eliminate 57% read/write traffic to NVM, which improves the lifetime of NVM devices. To reduce response latency of read requests, we securely manipulate data distributions in hybrid memory to speed up hot data access. The manipulation leverages a frequency-based eviction scheme and a data duplication scheme to keep more hot data accessible from DRAM earlier and faster. Moreover, we utilize the empty slots in DRAM region to further improve performance. The experimental results show that 44% response latency reduction and 18% performance improvement are achieved over the basic Ring ORAM with hybrid memory architecture integrated.