Understanding and Optimizing Flash-based Key-value Systems in Data Centers

Flash-based key-value systems are widely deployed in today’s data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations. In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs

CBA: Improving Online Continual Learning via Continual Bias Adaptor

Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams. Due to the time-varying training setting, the model learned from a changing distribution easily forgets the previously learned knowledge and biases toward the newly received task. To address this problem, we propose a Continual Bias Adaptor (CBA) module to augment the classifier network to adapt to catastrophic distribution change during training, such that the classifier network is able to learn a stable consolidation of previously learned tasks. In the testing stage, CBA can be removed which introduces no additional computation cost and memory overhead. We theoretically reveal the reason why the proposed method can effectively alleviate catastrophic distribution shifts, and empirically demonstrate its effectiveness through extensive experiments based on four rehearsal-based baselines and three public continual learning benchmarks.Comment: Accepted by ICCV 202

Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose

Domain gap between synthetic and real data in visual regression (e.g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning. Specifically, our method incorporates an explicit self-supervised manifold regularization, revealing consistent cumulative target dependency across domains, to a self-training scheme (e.g. the popular Self-Paced Self-Training) to encourage more discriminative transferable representations of regression tasks. Moreover, learning unified implicit neural functions to estimate relative direction and distance of targets to their nearest class bins aims to refine target classification predictions, which can gain robust performance against inconsistent feature scaling sensitive to UDA regressors. Experiment results on three public benchmarks of the challenging 6D pose estimation task can verify the effectiveness of our method, consistently achieving superior performance to the state-of-the-art for UDA on 6D pose estimation.Comment: Accepted by IJCAI 202

Vizard: A Metadata-hiding Data Analytic System with End-to-End Policy Controls

Owner-centric control is a widely adopted method for easing owners\u27 concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners\u27 data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce $\textsf{Vizard}$, a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. $\textsf{Vizard}$ is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of $\textsf{Vizard}$ that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of $\textsf{Vizard}$ for large-scale and metadata-hiding analytics over data streams

Twin-field quantum key distribution without phase locking

Twin-field quantum key distribution (TF-QKD) has emerged as a promising solution for practical quantum communication over long-haul fiber. However, previous demonstrations on TF-QKD require the phase locking technique to coherently control the twin light fields, inevitably complicating the system with extra fiber channels and peripheral hardware. Here we propose and demonstrate an approach to recover the single-photon interference pattern and realize TF-QKD \emph{without} phase locking. Our approach separates the communication time into reference frames and quantum frames, where the reference frames serve as a flexible scheme for establishing the global phase reference. To do so, we develop a tailored algorithm based on fast Fourier transform to efficiently reconcile the phase reference via data post-processing. We demonstrate no-phase-locking TF-QKD from short to long distances over standard optical fibers. At 50-km standard fiber, we produce a high secret key rate (SKR) of 1.27 Mbit/s, while at 504-km standard fiber, we obtain the repeater-like key rate scaling with a SKR of 34 times higher than the repeaterless secret key capacity. Our work provides a scalable and practical solution to TF-QKD, thus representing an important step towards its wide applications.Comment: Published versio

Characteristics of enzymolysis of silkworm pupa protein after tri-frequency ultrasonic pretreatment: kinetics, thermodynamics, structure and antioxidant changes

As a by-product of the sericulture industry, the utilization rate of silkworm pupa resources is currently not high. Proteins are converted into bioactive peptides through enzymatic hydrolysis. Not only can it solve the utilization problem, but it also creates more valuable nutritional additives. Silkworm pupa protein (SPP) was pretreated with tri-frequency ultrasonic (22/28/40 kHz). Effects of ultrasonic pretreatment on enzymolysis kinetics, enzymolysis thermodynamics, hydrolysate structure as well as hydrolysate antioxidant of SPP were investigated. Ultrasonic pretreatment significantly increased the hydrolysis efficiency, showing a 6.369% decrease in km and a 16.746% increase in kA after ultrasonic action (p < 0.05). The SPP enzymolysis reaction followed a second-order rate kinetics model. Evaluation of enzymolysis thermodynamics revealed that Ultrasonic pretreatment markedly enhanced the SPP enzymolysis, leading to a 21.943% decrease in Ea. Besides, Ultrasonic pretreatment significantly increased SPP hydrolysate’s surface hydrophobicity, thermal stability, crystallinity, and antioxidant activities (DPPH radical scavenging activity, Fe2+ chelation ability, and reducing power). This study indicated that tri-frequency ultrasonic pretreatment could be an efficient approach to enhancing the enzymolysis and improving the functional properties of SPP. Therefore, tri-frequency ultrasound technology can be applied industrially to enhance enzyme reaction process

The effect of thickness and elastic modulus of the anterior talofibular ligament on anterior ankle joint stiffness: A subject-specific finite element study

Ankle sprain is a frequent type of sports injury leading to lateral ligament injury. The anterior talofibular ligament (ATFL) is a primary ligamentous stabilizer of the ankle joint and typically the most vulnerable ligament injured in a lateral ankle sprain (LAS). This study aimed to quantitively investigate the effect of the thickness and elastic modulus of ATFL on anterior ankle joint stiffness (AAJS) by developing nine subject-specific finite element (FE) models under acute injury, chronic injury, and control conditions of ATFL. A 120 N forward force was applied at the posterior calcaneus leading to an anterior translation of the calcaneus and talus to simulate the anterior drawer test (ADT). In the results, the ratio of the forward force to the talar displacement was used to assess the AAJS, which increased by 5.85% in the acute group and decreased by 19.78% in the chronic group, compared to those of the control group. An empirical equation described the relationship between AAJS, thickness, and elastic modulus (R-square 0.98). The equation proposed in this study provided an approach to quantify AAJS and revealed the effect of the thickness and the elastic modulus of ATFL on ankle stability, which may shed light on the potential diagnosis of lateral ligament injury