Deep neural networks (DNNs) are vulnerable to backdoor attacks, where an attacker manipulates a small portion of the training data to implant hidden backdoors into the model. The compromised model behaves normally on clean samples but misclassifies backdoored samples into the attacker-specified target class, posing a significant threat to real-world DNN applications. Currently, several empirical defense methods have been proposed to mitigate backdoor attacks, but they are often bypassed by more advanced backdoor techniques. In contrast, certified defenses based on randomized smoothing have shown promise by adding random noise to training and testing samples to counteract backdoor attacks. In this paper, we reveal that existing randomized smoothing defenses implicitly assume that all samples are equidistant from the decision boundary. However, it may not hold in practice, leading to suboptimal certification performance. To address this issue, we propose a certified backdoor defense method with sample-specific smoothing noises, termed Cert-SSBD. Cert-SSBD first employs stochastic gradient ascent to optimize the noise magnitude for each sample, ensuring a sample-specific noise level that is then applied to multiple poisoned training sets to retrain several smoothed models. After that, Cert-SSBD aggregates the predictions of multiple smoothed models to generate the final robust prediction. In particular, in this case, existing certification methods become inapplicable since the optimized noise varies across different samples. To conquer this challenge, we introduce a storage-update-based certification method, which dynamically adjusts each sample’s certification region to improve certification performance. We conduct extensive experiments on multiple benchmark datasets, demonstrating the effectiveness of our proposed method. Our code is available at https://github.com/NcepuQiaoTing/Cert-SSBD.

Elastic block storage (EBS) with the storagecompute disaggregated architecture stands as a pivotal piece in today’s cloud. EBS furnishes users with storage capabilities through the elastic solid-state drive (ESSD). Nevertheless, despite the widespread integration into cloud services, the absence of a thorough ESSD performance characterization raises critical doubt: when more and more services are shifted onto the cloud, can ESSD satisfactorily substitute the storage responsibilities of the local SSD and offer comparable performance? In this paper, we for the first time target this question by characterizing two ESSDs from Amazon AWS and Alibaba Cloud. We present an unwritten contract of cloud-based ESSDs, encapsulating four observations and five implications for cloud storage users. Specifically, the observations are counter-intuitive and contrary to the conventional perceptions of what one would expect from the local SSD. The implications we hope could guide users in revisiting the designs of their deployed cloud software, i.e., harnessing the distinct characteristics of ESSDs for better system performance.

Zoned Namespace (ZNS) is an emerging SSD interface with great potential for performance and cost in largescale cloud SSD deployments. Enabling in-storage hardware compression on ZNS SSDs is promising for further enhancing the cost-effectiveness of ZNS-based storage infrastructures. However, based on our investigation, existing solutions based on the host-transparent methodology are sub-optimal on ZNS SSDs due to two intrinsic challenges: (1) locating compressed chunks and (2) harvesting space savings from compression.In this paper, we for the first time revisit the compression storage system architecture on the emerging ZNS SSD and propose to decouple compression execution with indexing. We propose CCZNS (CC: collaborative compression), an advanced ZNS interface that revives in-storage hardware compression on ZNS SSDs through a novel host-SSD collaborative approach. We present how CCZNS can benefit host software by performing a case study on RocksDB and ZenFS. Extensive experiments demonstrate that the CCZNS-based storage system significantly outperforms existing system solutions.

The legacy block interface for I/O benefits from data locality but faces challenges with I/O amplification due to the frequent small read-write operations common in most applications. Dual-Interface SSDs, which integrate block-interface Flash memory with byte-addressable memory, create opportunities for application redesign by reducing unnecessary read-write amplification on storage devices. However, current Dual-Interface SSD hardware remains limited in terms of size and functionality. Existing software designs for Dual-Interface SSDs often use the byte-addressable space as sequential logs with basic batch reclamation. While this space allows random access, and batch reclamation introduces significant tail latency caused by excessive read and write-back operations. To fully exploit the potential of Dual-Interface SSDs, we have developed a prototype on a hardware-software configurable platform. This publicly accessible Dual-Interface SSD offers realistic and optimized performance, overcoming the size and functionality limitations of previous designs. In addition, we demonstrate the ability of Dual-Interface SSDs to reduce write amplification in a traditional Copy-on-Write B-Tree data store. By employing two key techniques–Tree Pointer Relocation, which decouples indirection from the tree structure, and Tree Node Accommodation, which enables small-sized key-value pair updates to be processed directly in the byte-addressable space–we significantly improve the efficiency of storage operations. Our evaluation reveals that these techniques, when applied to Dual-Interface SSDs, achieve performance gains of 30.5% and 52.5% compared with a CoW B-Tree operating on traditional block-interface SSDs.

High-density NAND flash has revolutionized the storage ecosystem because of its rapidly decreasing per-bit costs and unprecedented capacities. However, the inherent large block size of modern high-density NAND flash inevitably aggravates the reclamation latency (i.e., the time required to reclaim the storage space occupied by the obsolete data), which subsequently prolongs the tail latency of flash-based storage devices. Inspired by the “erase duality” from the emerging bit-alterable NAND flash, this article proposes a reclamation latency suppressed (RLS) space management design to synergize the strengths of both block-level erase and page-level erase. Taking into account the data update frequency during runtime, RLS enables proactive adjustment of the dual-granularity erase. Moreover, RLS tightly couples the data cluster allocation strategy with a novel dual-granularity space reclamation design, thereby alleviating the reclamation latency. We extensively examine the benefits of RLS with real-world workloads. Our evaluation results reveal that, with the suppressed space reclamation latency, RLS achieves up to 37.51% improvement for both write and read tail latency (latency at the 99.9th percentile) compared with the state-of-the-art approaches.

The zoned storage has revolutionized the decades-old block storage in lowering the cost-per-gigabyte while enabling the host system to achieve better performance. With such benefit of cost and performance, we still require careful consideration on the endurance when deploying the applications on the zoned storage, since the modern storage tends to trade its endurance for larger capacity at lower cost. In this regard, although previous studies have deployed the log-structure merge (LSM-tree)-based key-value (KV) store on the zoned storage, the LSM-tree-based KV store can be suboptimal choice to build a cost-effective KV store on zoned storage, since LSM-tree has a well-known problem of write amplification (WA). Therefore, based on the key insight that the Fragmented Log-Structured Merge tree (FLSM-tree) substantially alleviates the notorious write amplification problem of the classical LSM-tree and inherently complies with the sequential write constraint of zoned storage, FLSM-tree would be a promising design choice to build a cost-effective KV store on zoned storage. However, based on our investigation, deploying an FLSM-tree-based KV store on zoned storage faces two challenges: The write amplification of the host-initiated garbage collection (GC) cancels out the low WA merit of FLSM-tree, and FLSM-tree results in high space amplification to increase the cost. In this regard, this article presents ZonesDB , a novel FLSM-tree-based KV store that comes with a series of innovative “zone-aware” techniques for pursuing write optimality and space adaptability. Our evaluations, based on two types of production-grade zoned storage (i.e., ZNS SSD and HM-SMR HDD), reveal that ZonesDB can bring into play the low WA merit of FLSM-tree, deliver outstanding write performance, and mitigate the space amplification problem of FLSM-tree on zoned storage.

Zoned Namespace (ZNS) is an emerging interface that shows great promise for high-density and low-cost cloud environment deployments. Modern high-density SSDs, on the other hand, typically use a hybrid SLC/QLC architecture to mitigate the deficiencies of QLC. Unfortunately, simply integrating ZNS with the mainstream host-transparent hybrid architecture would lead to substantial performance and endurance overhead, defeating the purpose of this architecture in the first place.

Combined with the characteristics of vehicle operation, a torque distribution control strategy based on logic threshold method for a series-parallel hybrid electric bus is proposed to improve the vehicle's fuel economy. And the simulink model of this strategy is built. Then the simulation is completed under the selected cycle condition. Compared with the real vehicle test data, the control strategy can reduce the fuel consumption by 6.2% on the basis of guaranteeing the vehicle's dynamic performance, which achieves the design goals. And the charge balance of the super-capacitor is also well maintained. The validity and feasibility of the torque distribution control strategy has been verified.