With the continuous emergence of Internet of Vehicles (IoV) applications, the demand for computational resources of many resource-intensive applications in IoV has shown an explosive growth trend, which poses a serious challenge to the limited computational resources of the vehicles themselves. This paper designs a federated learning structure with a two-layer game for vehicular networks, using intelligent roadside terminals for federated optimization. Meanwhile, this paper proposes a Federated Learning and Cloud-Edge Gaming with Incentive-Driven (FL-CEGID) algorithm for dynamic task offloading in IoV. Our proposed algorithm optimizes vehicle and computing resource allocation as well as cache updates through a hierarchical distributed approach, which has separate vehicle and edge intelligence strategies for offloading decisions and caching strategies. The experimental results show that our proposed FL-CEGID has significant improvements in transmission capacity, transmission delay, and advantages in different key tasks and times in IoV compared to other schemes.

The non-volatile memory (NVM) is becoming the main device of next-generation memory, due to the high density, near-zero standby power, non-volatile and byte-addressable features. The multi-level cell (MLC) technique has been used in non-volatile memory to significantly increase device density and capacity, which however leads to much weaker endurance than the single-level cell (SLC) counterpart. Although wear-leveling techniques can mitigate this weakness in MLC, the improvements upon MLC-based NVM become very limited due to not achieving uniform write distribution before some cells are really worn out. To address this problem, our paper proposes a self-adaptive wear-leveling (SAWL) scheme for MLC-based NVM. The idea behind SAWL is to dynamically tune the wear-leveling granularities and balance the writes across the cells of entire memory, thus achieving suitable tradeoff between the lifetime and cache hit rate. Moreover, to reduce the size of the address-mapping table, SAWL maintains a few recently-accessed mappings in a small on-chip cache. Experimental results demonstrate that SAWL significantly improves the NVM lifetime and the performance, compared with state-of-the-art schemes.

Non-volatile memories (NVMs) are vulnerable to serious threat due to the endurance variation. We identify a new type of malicious attack, called Uniform Address Attack (UAA), which performs uniform and sequential writes to each line of the whole memory, and wears out the weaker lines (lines with lower endurance) early. Experimental results show that the lifetime of NVMs under UAA is reduced to 4.1% of the ideal lifetime. To address such attack, we propose a spare-line replacement scheme called Max-WE (Maximize the Weak lines' Endurance). By employing weak-priority and weak-strong-matching strategies for spare-line allocation, Max-WE is able to maximize the number of writes that the weakest lines can endure. Furthermore, Max-WE reduces the storage overhead of the mapping table by 85% through adopting a hybrid spare-line mapping scheme. Experimental results show that Max-WE can improve the lifetime by 9.5X with the spare-line overhead and mapping overhead as 10% and 0.016% of the total space respectively.

As promising alternatives in building future main memory systems, emerging non-volatile memory (NVM) technologies can increase memory capacity in a cost-effective and power-efficient way. However, NVM is facing security threats due to its limited write endurance: a malicious adversary can wear out the cells and cause the NVM system to fail quickly. To address this issue, several wear-leveling schemes have been proposed to evenly distribute write traffic in a security-aware manner. In this study, we present a new type of timing attack, remapping timing attack (RTA), based on information leakage from the remapping latency difference in NVM. Our analysis and experimental results show that RTA can cause three of the latest wear-leveling schemes (i.e., region-based start-gap, security refresh, and multi-way wear leveling) to lose their effectiveness in several days (even minutes), causing failure of NVM. To defend against such an attack, we further propose a novel wear-leveling scheme called the ‘security region-based start-gap (security RBSG)’, which is a two-stage strategy using a dynamic Feistel network to enhance the simple start-gap wear leveling with level-adjustable security assurance. The theoretical analysis and evaluation results show that the proposed security RBSG not only performs well when facing traditional malicious attacks, but also better defends against RTA.

Counter-mode encryption has been widely used to resist NVMs from malicious attacks, due to its proved security and high performance. However, this scheme suffers from the counter size versus re-encryption problem, where per-line counters must be relatively large to avoid counter overflow, or re-encryption of the entire memory is required to ensure security. In order to address this problem, we propose a novel wear-leveling-aware counter mode for data encryption, called Resetting Counter via Remapping (RCR). The basic idea behind RCR is to leverage wear-leveling remappings to reset the line counter. With carefully designed procedure, RCR avoids counter overflow with much smaller counter size. The salient features of RCR include low storage overhead of counters, high counter cache hit ratio, and no extra re-encryption overhead. Compared with state-of-the-art works, RCR obtains significant performance improvements, e.g., up to a 57% reduction in the IPC degradation, under the evaluation of 8 memory-intensive benchmarks from SPEC 2006.

Chunk-level deduplication, while robust in removing duplicate chunks, introduces chunk fragmentation which decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation and accelerate the restore speed. Delta compression can remove redundant data between non-duplicate but similar chunks which cannot be eliminated by chunk-level deduplication. Some applications use delta compression as a complement for chunk-level deduplication to attain extra space and bandwidth savings. However, we observe that delta compression introduces a new type of chunk fragmentation stemming from delta compressed chunks whose base chunks are fragmented. We refer to such delta compressed chunks as base-fragmented chunks. We found that this new type of chunk fragmentation has a more severely impact on the restore performance than the chunk fragmentation introduced by chunk-level deduplication and cannot be reduced by existing rewriting algorithms. In order to address the problem due to the base-fragmented chunks, we propose SDC, a scheme that selectively performs delta compression after chunk-level deduplication. The main idea behind SDC is to simulate a restore cache to identify the non-base-fragmented chunks and only perform delta compression for these chunks, thus avoiding the new type of chunk fragmentation. Due to the locality among the backup streams, most of the non-base-fragmented chunks can be detected by the simulated restore cache. Experimental results based on real-world datasets show that SDC improves the restore performance of the delta compressed and deduplicated backup system by 1.93X-7.48X, and achieves 95.5%-97.4% of its compression, while imposing negligible impact on the backup throughput.

Chunk-level deduplication plays an important role in backup storage systems. Existing Content-Defined Chunking (CDC) algorithms, while robust in finding suitable chunk boundaries, face the key challenges of (1) low chunking throughput that renders the chunking stage a serious deduplication performance bottleneck, (2) large chunk size variance that decreases deduplication efficiency, and (3) being unable to find proper chunk boundaries in low-entropy strings and thus failing to deduplicate these strings. To address these challenges, this paper proposes a new CDC algorithm called the Asymmetric Extremum (AE) algorithm. The main idea behind AE is based on the observation that the extreme value in an asymmetric local range is not likely to be replaced by a new extreme value in dealing with the boundaries-shifting problem. As a result, AE has higher chunking throughput, smaller chunk size variance than the existing CDC algorithms, and is able to find proper chunk boundaries in low-entropy strings. The experimental results based on realworld datasets show that AE improves the throughput performance of the state-of-the-art CDC algorithms by more than 2.3×, which is fast enough to remove the chunking-throughput performance bottleneck of deduplication, and accelerates the system throughput by more than 50 percent, while achieving comparable deduplication efficiency.

Phase Change Memory (PCM) has emerged as a promising candidate for building the future main memory systems. However, the limited write endurance is one of the major obstacles for PCM to be practically applied. Traditional wear-leveling techniques try to uniformly balance the write traffics under both general applications and malicious attacks to enhance the PCM lifetime. However, these techniques fail to consider the endurance variation in PCM chips, and result in severe lifespan degradation since uniform write distribution leads to the weakest cell to be worn out much earlier. In this paper, we propose a weight-based algebraic wear-leveling (WAWL) scheme to balance wear rates (i.e., write traffics/endurance) in a secure manner according to the endurance distribution. In WAWL, the entire memory space is divided into multiple regions. When the number of the writes to a region reaches a threshold (i.e., swapping interval), the region is swapped with a randomly chosen region. The basic idea behind WAWL is that the swapping interval and the chosen probability of each region are variable and associated with the endurance metric of the region. By deploying suitable swapping interval and chosen probability, WAWL achieves uniform wear-rate distribution across the entire memory in an undetectable way. In addition, to reduce space consumption and alleviate performance degradation during region swapping, we propose a fine-grained swapping scheme which migrates the lines one-by-one between the candidate regions. Experimental evaluation driven by the various attacks demonstrates that WAWL significantly increases the PCM lifespan and improves security with slight performance degradation and affordable hardware overhead.

As an emerging memory technology to build the future main memory systems, Phase Change Memory (PCM) can increase memory capacity in a cost-effective and power-efficient way. However, PCM is facing security threats for its limited write endurance: a malicious adversary could wear out the cells and cause the whole PCM system to fail within a short period of time. To address this issue, several wear-leveling schemes have been proposed to evenly distribute write traffic in a security-aware manner. In this work, we present a new type of timing attacknamed Remapping Timing Attack (RTA), based on the asymmetry in write time of PCM. Our analysis and experimental results show that the new revealed RTA can make two state-of-the-art wear-leveling schemes (Region Based Start-Gap and Security Refresh) lose effectiveness, failing PCM with these two techniques in several days (even minutes). In order to defend such attack, we further propose a novel wear-leveling scheme called Security Region Based Start-Gap (Security RBSG), which employs a two-stage strategy and uses a dynamic Feistel Network to enhance the simple Start-Gap wear leveling with level-adjustable security assurance. The theoretical analysis and evaluation results show that the proposed Security RBSG is the most robust wear-leveling methodology so far, which not only better defends the new RTA, but also performs well on the traditional malicious attacks, i.e., Repeated Address Attack and Birthday Paradox Attack.

Phase Change Memory (PCM) is an emerging non-volatile memory with the salient features of large-scale, high-speed, low-power and radiation resistance. It hence becomes an ideal candidate for the next-generation storage media of main memory. However, PCM suffers from inefficient I/O performance due to long write latency. Recent studies propose a multi-partition (or multi-subarray) architecture within each bank to enhance internal parallelism. However, conventional scheduling schemes fail to exploit the advantage of multiple partitions and incur inefficient bank utilization. In this paper, we propose a Write Priority overlap Read (WPoR) scheduling scheme which preferentially serves for a write request in one partition and allows other partitions to perform as many read requests as possible within this partition's program duration. Experimental results demonstrate that WPoR reduces the write latency by 24.7% (on average) compared with state-of-the-art scheduling algorithms. Meanwhile, the IPC indicator of WPoR scheduling increases respectively 6%, 7% and 26% (on average) compared with Read Priority, Write Pausing and Write Cancellation schemes.

In backup systems, the chunks of each backup are physically scattered after deduplication, which causes a challenging fragmentation problem. We observe that the fragmentation comes into sparse and out-of-order containers. The sparse container decreases restore performance and garbage collection efficiency, while the out-of-order container decreases restore performance if the restore cache is small. In order to reduce the fragmentation, we propose History-Aware Rewriting algorithm (HAR) and Cache-Aware Filter (CAF). HAR exploits historical information in backup systems to accurately identify and reduce sparse containers, and CAF exploits restore cache knowledge to identify the out-of-order containers that hurt restore performance. CAF efficiently complements HAR in datasets where out-of-order containers are dominant. To reduce the metadata overhead of the garbage collection, we further propose a Container-Marker Algorithm (CMA) to identify valid containers instead of valid chunks. Our extensive experimental results from real-world datasets show HAR significantly improves the restore performance by 2.84-175.36 × at a cost of only rewriting 0.5-2.03 percent data.

Data deduplication, a space-efficient and bandwidth-saving technology, plays an important role in bandwidth-efficient data transmission in various data-intensive network and cloud applications. Rabin-based and MAXP-based Content-Defined Chunking (CDC) algorithms, while robust in finding suitable cut-points for chunk-level redundancy elimination, face the key challenges of (1) low chunking throughput that renders the chunking stage the deduplication performance bottleneck and (2) large chunk-size variance that decreases deduplication efficiency. To address these challenges, this paper proposes a new CDC algorithm called the Asymmetric Extremum (AE) algorithm. The main idea behind AE is based on the observation that the extreme value in an asymmetric local range is not likely to be replaced by a new extreme value in dealing with the boundaries-shift problem, which motivates AE's use of asymmetric (rather than symmetric as in MAXP) local range to identify cut-points and simultaneously achieve high chunking throughput and low chunk-size variance. As a result, AE simultaneously addresses the problems of low chunking throughput in MAXP and Rabin and high chunk-size variance in Rabin. The experimental results based on four real-world datasets show that AE improves the throughput performance of the state-of-the-art CDC algorithms by 3x while attaining comparable or higher deduplication efficiency.

Nowadays, many customers and enterprises backup their data to cloud storage that performs deduplication to save storage space and network bandwidth. Hence, how to perform secure deduplication becomes a critical challenge for cloud storage. According to our analysis, the state-of-the-art secure deduplication methods are not suitable for cross-user finegrained data deduplication. They either suffer brute-force attacks that can recover files falling into a known set, or incur large computation (time) overheads. Moreover, existing approaches of convergent key management incur large space overheads because of the huge number of chunks shared among users. Our observation that cross-user redundant data are mainly from the duplicate files, motivates us to propose an efficient secure deduplication scheme SecDep. SecDep employs User-Aware Convergent Encryption (UACE) and Multi-Level Key management (MLK) approaches. (1) UACE combines cross-user file-level and inside-user chunk-level deduplication, and exploits different secure policies among and inside users to minimize the computation overheads. Specifically, both of file-level and chunk-level deduplication use variants of Convergent Encryption (CE) to resist brute-force attacks. The major difference is that the file-level CE keys are generated by using a server-aided method to ensure security of cross-user deduplication, while the chunk-level keys are generated by using a user-aided method with lower computation overheads. (2) To reduce key space overheads, MLK uses file-level key to encrypt chunk-level keys so that the key space will not increase with the number of sharing users. Furthermore, MLK splits the file-level keys into share-level keys and distributes them to multiple key servers to ensure security and reliability of file-level keys. Our security analysis demonstrates that SecDep ensures data confidentiality and key security. Our experiment results based on several large real-world datasets show that SecDep is more time-efficient and key-space-efficient than the state-of-the-art secure deduplication approaches.