Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to achieving efficient SpMM for graph data on GPUs. Recently, the introduction of new efficient computing cores within GPUs offers new opportunities for acceleration. In this paper, we present HC-SpMM, a pioneering algorithm that leverages Hybrid GPU Cores (Tensor cores and CUDA cores) to accelerate SpMM for graphs. To adapt to the computing characteristics ofdifferent GPU cores, we investigate the impact of sparse graph features on the performance of different cores, develop a data partitioning technique for the graph adjacency matrix, and devise a novel strategy for intelligently selecting the most efficient cores for processing each submatrix. Additionally, we optimize it by considering memory access and thread utilization. To support complex graph computing workloads, we integrate HC-SpMM into the GNN training pipeline. Furthermore, we propose a kernel fusion strategy to enhance data reuse, as well as a cost-effective graph layout reorganization method to mitigate the irregularity of real-world graphs, better fitting the computational models of hybrid GPU cores. Extensive experiments on 14 real-world datasets demonstrate that HC-SpMM achieves an average speedup of 1.33× and 1.23× over state-of-the-art SpMM kernels and GNN frameworks.

Table union search (TUS) represents a fundamental operation in data lakes to find tables unionable to the given one. Recent approaches to TUS mainly learn column representations for searching by introducing Pre-trained Language Models (PLMs), especially on columns with linguistic data. However, a significant amount of non-linguistic data, notably represented by domain-specific strings and numerical data in the data lake, are still under-explored in the existing methods. To address this issue, we propose LIFTus, an adaptive multi-aspect column representation for table unionable search, where aspect refers to a concept more flexible than data types, so that a single column can exhibit multiple aspects simultaneously. LIFTus aims at combining different aspects of a column (including both linguistic and non-linguistic aspects) to promote the effectiveness and generalization of TUS in a self-supervised manner. Specifically, besides employing PLMs to extract the linguistic aspects from an individual column, LIFTus trains a pattern encoder to learn possible character-level sequential patterns for the column, and builds a number encoder to capture numerical aspects of the column, including the distribution and magnitude features. LIFTus further utilizes a hierarchical cross-attention aided by aspect-relevant statistics to combine these aspects adaptively in producing the final column representations, which are indexed by vector retrieval techniques to achieve efficient search. Extensive experimental results demonstrate that LIFTus has outperformed the current state-of-the-art methods in terms of effectiveness, and achieved much better generalization capability to support unseen data.

The non-volatile memory (NVM) with DRAM-like performance and disk-like persistency has attracted considerable attention in a variety of index structures, including hash table, B-Tree and R-Tree. However, existing NVM-optimized consistent R-Tree is still suboptimal because its single level system neglects the potential boost that DRAM can bring. In this paper, we first propose a hybrid DRAM-NVM consistent R-Tree (HR-Tree), which separately stores internal nodes in DRAM and leaf nodes in NVM. To avoid inconsistency, HR-Tree uses several auxiliary flag bits and pointers to record the process of writes to NVM and employs persistence operations to strictly control the order of writes to NVM. To reduce DRAM consumption, which mainly depends on the metadata size of a leaf node, we present a shared byte strategy to abolish restrictions on metadata size while still keeping HR-Tree consistency. Next, for further shortening search time, we propose an alternative Hilbert-curve-based hybrid R-Tree (HHR-Tree). It has better search efficiency yet leads to insertion performance degradation. Contrary to in-place update in HR-Tree, HHR-Tree applies out-of-place mechanism to enforce data consistency. We conduct comprehensive evaluations on Intel Optane DC Persistent Memory. The proposed HR-Tree outperforms FBR-Tree in terms of insertion, deletion and search throughput while HHR-Tree exhibits a significant improvement for search performance by sacrificing insertion efficiency.

Abstract Distributed in-memory databases are widely adopted to achieve low latency and high bandwidth for data-intensive applications. They support scale-out by sharding and distributing data across multiple nodes. To efficiently adapt to various workloads, distributed in-memory databases must be capable of migrating shards across nodes. In this paper, we demonstrate that state-of-the-art approaches experience significant performance degradation during migration due to service downtime and redundant data transfer. Furthermore, our findings indicate that the presence of service downtime constrains the scalability of migration strategies, while the transfer of redundant data during the snapshot transfer phase limits their adaptability to dynamic workloads. To this end, this paper proposes Aion, a live migration strategy designed for distributed in-memory databases. Aion eliminates any potential service downtime by immediately switching transaction routing to the destination node. To ensure data consistency between the source and destination nodes, as well as serializable execution during migration, Aion proposes the mutual validation phase. Moreover, Aion introduces an analysis phase before the snapshot transfer phase to identify dynamically changing hotspots in workloads. The analysis phase identifies and transfers tuples and versions accessed less frequently to the destination node, reducing the amount of data transferred. Aion is implemented on a distributed in-memory database and evaluated using various OLTP workloads. The results demonstrate that Aion can fundamentally eliminate service downtime, adapt effectively to various workloads and exhibit robust scalability. Compared to state-of-the-art approaches, Aion achieves up to 2.25x–6.57x higher throughput during migration and shortens the migration duration by 53.7–68.2%.

With the breakthrough of Transformer-based pre-trained models, the demand for fine-tuning (FT) to adapt the base pre-trained models to downstream applications continues to grow, so it is essential for service providers to reduce the cost of processing FT requests. Low-rank adaption (LoRA) is a widely used FT technique that only trains small-scale adapters and keeps the base model unaltered, conveying the possibility of processing multiple FT tasks by jointly training different LoRA adapters with a shared base model. Nevertheless, through in-depth analysis, we reveal the efficiency of joint FT is dampened by two heterogeneity issues in the training data — the sequence length variation and skewness. To tackle these issues, we develop LobRA, a brand new framework that supports processing multiple FT tasks by jointly training LoRA adapters. Two innovative designs are introduced. Firstly, LobRA deploys the FT replicas (i.e., model replicas for FT) with heterogeneous resource usages and parallel configurations, matching the diverse workloads caused by the sequence length variation. Secondly, for each training step, LobRA takes account of the sequence length skewness and dispatches the training data among the heterogeneous FT replicas to achieve workload balance. We conduct experiments to assess the performance of LobRA, validating that it significantly reduces the GPU seconds required for joint FT by 45.03%-60.67%.

How to effectively diagnose and mitigate database performance anomalies remains a significant concern for modern database systems. Manually identifying the root causes of the anomalies is a labor-intensive process and significantly relies on professional experience. Meanwhile, existing work on automatic database diagnosis mainly focuses on detecting anomalous performance metrics or system log. These solutions lack the power to pinpoint detailed issues such as bad queries or problematic operators, which are indispensable for most database troubleshooting processes. In this paper, we propose OpDiag, a diagnosis framework that attributes database performance anomalies to query operators. In this framework, we first construct models offline to represent the relationship between query operators, performance metrics, and anomalies. These models can capture query plan features and support ad-hoc queries and schemas. Then, through feature attribution on these models during online diagnosis, OpDiag can effectively identify critical anomalous metrics and further trace back to suspicious queries and operators. This can provide concrete guidance for subsequent steps in anomaly mitigation. We applied OpDiag to both synthetic benchmark and real industry cases from ZTE Corporation. Empirical studies prove that OpDiag can accurately localize anomalous queries and operators, thus reducing human efforts in diagnosing and mitigating database performance anomalies.

Memory disaggregation has emerged as a promising architecture for improving resource efficiency by decoupling the computing and memory resources. But building efficient range indices in such an architecture faces three critical challenges: (1) coarse-grained concurrency control schemes for coordinating concurrent read/write operations with node splitting incur high contention under the skewed and write-intensive workloads; (2) existing data layouts fail to balance consistency verification and hardware acceleration via SIMD (Single Instruction Multiple Data); and (3) naive caching schemes struggle to adapt to rapidly changing access patterns. To address these challenges, we proposeMariana, a memory-disaggregated skiplist index that integrates three key innovations. First, it uses a fine-grained (i.e., entry-level) latch mechanism combined with dynamic node resizing to minimize the contention and splitting frequency. Second, it employs a tailored data layout for leaf node, which separates keys and values to enable SIMD acceleration while maintaining consistency checks with minimal write overhead. Third, it implements an adaptive caching strategy that tracks node popularity in real-time to optimize network bandwidth utilization during the index traversal. Experimental results show thatMarianaachieves$1.7\times$higher throughput under write-intensive workloads and reduces the P90 latency by 23% under the read-intensive workloads, when comparing to the state-of-the-art indices on disaggregated memory.

In recent years, there has been a growing interest in the application of deep reinforcement learning (DRL) techniques on query execution plan generation. Although current DRL-based query optimizers achieve competitive performance against traditional methods on specific query workloads, these methods encounter issues when generalizing to workloads unseen during training. Thus, we propose GLO to address the limitations and step towards generalized learned query optimization. First, rather than using ungeneralizable table-specific one-hot labels in almost all existing work, GLO relies on statistical information of the well-established underlying DBMS along with table patterns extracted via a clustering algorithm, enabling GLO to enhance generalization in different scenarios. Second, GLO improves the information capture of plans by integrating Transformer layers into the DRL value model, empowering the model's capability to handle diverse queries with deeper networks and more parameters in plan generation. In addition, GLO allows the injection of cost estimations from the DBMS as external knowledge for better generalization. Third, GLO recognizes and replaces disastrously poor plans by making comparisons between generated plans and those produced by the DBMS. We establish our experiments on composite workloads that combine various query sets including JOB, Extended JOB, TPC-DS, and Stack. The results demonstrate that GLO outperforms previous state-of-the-art learned optimizers, with a speed 1.4x faster than LOGER and 2.1x faster than Balsa on TPC-DS when TPC-DS queries are completely unknown during training. To the best of our knowledge, GLO is the first learned optimizer that directly generates plans while possessing the preliminary generalization ability across different query workloads.

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, and the like. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention. Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, and so on. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on the fly. SecCT+ is a solution based on trusted execution environment while SecCT is a pure software solution. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT and SecCT+.

In a geo-distributed database, data shards and their respective replicas are deployed in distinct datacenters across multiple regions, enabling regional-level disaster recovery and the ability to serve global users locally. However, transaction processing in geo-distributed databases requires multiple cross-region communications, especially during the commit phase, which can significantly impact system performance. To optimize the performance of geo-distributed transactions, we propose Decentralized Two-phase Commit (D2PC), a new transaction commit protocol aiming to minimize the negative impact of cross-region communication. In D2PC, we employ multiple co-coordinators that perform commit coordination in parallel. Each co-coordinator is responsible for collecting 2PC votes and making a PreCommit decision in its local region. This approach allows for the concurrent invocation of multiple cross-region network round trips, and each region can end its concurrency control locally before replication is complete, thus significantly reducing the chances of blocking and enhancing system concurrency. Moreover, we propose the bypass leader replication reply method, leveraging decentralized co-coordinators to bypass the leader for message transmission, thereby reducing the commit latency. Experimental results have demonstrated that D2PC can reduce commit latency by 43% and improve throughput by up to 2.43 × compared to the geo-distributed transaction processing methods based on 2PC.

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. This paper proposes a unified framework, namely the HyperCalm sketch, to detect batch and periodic batch in data streams. HyperCalm sketch takes two phases to detect periodic batches. In phase 1, we propose a time-aware Bloom filter, called HyperBloomFilter (HyperBF), to detect batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-itk periodic batches. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 98.1× in term of speed. All related codes are open-sourced.

Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models. However, efficiently training these models across multiple GPUs remains a complex challenge due to the abundance of parallelism options. Existing DL systems either require manual efforts to design distributed training plans or limit parallelism combinations to a constrained search space. In this paper, we present Galvatron-BMW, a novel system framework that integrates multiple prevalent parallelism dimensions and automatically identifies the most efficient hybrid parallelism strategy. To effectively navigate this vast search space, we employ a decision tree approach for decomposition and pruning based on intuitive insights. We further utilize a dynamic programming search algorithm to derive the optimal plan. Moreover, to improve resource utilization and enhance system efficiency, we propose a bi-objective optimization workflow that focuses on workload balance. Our evaluations on different Transformer models demonstrate the capabilities of Galvatron-BMW in automating distributed training under varying GPU memory constraints. Across all tested scenarios, Galvatron-BMW consistently achieves superior system throughput, surpassing previous approaches that rely on limited parallelism strategies.

Nowadays, there exists a lot of cross-region data transmission demand on the cloud. It is promising to use serverless computing for data compressing to save the total data size. However, it is challenging to estimate the data transmission time and monetary cost with serverless compression. In addition, minimizing the data transmission cost is non-trivial due to the enormous parameter space. This paper focuses on this problem and makes the following contributions: 1) We propose empirical data transmission time and monetary cost models based on serverless compression. It can also predict compression information, e.g., ratio and speed using chunk sampling and machine learning techniques. 2) For single-task cloud data transmission, we propose two efficient parameter search methods based on Sequential Quadratic Programming (SQP) and Eliminate then Divide and Conquer (EDC) with proven error upper bounds. Besides, we propose a parameter fine-tuning strategy to deal with transmission bandwidth variance. 3) Furthermore, for multi-task scenarios, a parameter search method based on dynamic programming and numerical computation is proposed. We have implemented the system called Fluid-Shuttle, which includes straggler optimization, cache optimization, and the autoscaling decompression mechanism. Finally, we evaluate the performance of Fluid-Shuttle with various workloads and applications on the real-world AWS serverless computing platform. Experimental results show that the proposed approach can improve the parameter search efficiency by over$3\times $compared with the state-of-art methods and achieves better parameter quality. In addition, our approach achieves higher time efficiency and lower monetary cost compared with competing cloud data transmission approaches.

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced.

A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters.

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, etc. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention.Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, etc. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on-the-fly. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT.

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.

In distributed storage systems, replication and erasure code (EC) are common methods for data redundancy. Compared with replication, EC has better storage efficiency, but suffers higher overhead in update. Moreover, consistency and reliability problems caused by concurrent updates bring new challenges to applications of EC. Many works focus on optimizing the EC solution, including algorithm optimization, novel data update method, and so on, but lack the solutions for consistency and reliability problems. In this paper, we introduce a storage system that decouples data updating and EC encoding, namely, decoupled data updating and coding (DDUC), and propose a data placement policy that combines replication and parity blocks. For the ( N, M ) EC system, the data are placed as N groups of M +1 replicas, and redundant data blocks of the same stripe are placed in the parity nodes, so that the parity nodes can autonomously perform local EC encoding. Based on the above policy, a two-phase data update method is implemented in which data are updated in replica mode in phase 1, and the EC encoding is done independently by parity nodes in phase 2. This solves the problem of data reliability degradation caused by concurrent updates while ensuring high concurrency performance. It also uses persistent memory (PMem) hardware features of the byte addressing and eight-byte atomic write to implement a lightweight logging mechanism that improves performance while ensuring data consistency. Experimental results show that the concurrent access performance of the proposed storage system is 1.70–3.73 times that of the state-of-the-art storage system Ceph, and the latency is only 3.4%–5.9% that of Ceph.

Anomaly diagnosis is vital to the performance of online transaction processing (OLTP) systems. In the meanwhile, machine learning techniques can reason complex relationships beyond human abilities and perform well on such problems. However, they rely on a large number of training samples for anomalies, which are in serious shortage in both industry and academia due to the difficulty of collection. The problem raises the demand of a benchmark for anomaly reproduction and data collection. In this paper, we propose DBPA, a benchmark for transactional database performance anomalies. Specifically, we identify nine common anomalies rooted in the diverse influence factors. For each anomaly, we carefully design a reproduction procedure, which consists with its root cause in real-world databases. With the reproduction procedures, users can easily generate a dataset in a new environment and extend new anomaly types. For compound anomalies, we provide a generation algorithm that allows users to generate compound anomalies data of any possible combinations with existing collected data. We also provide a large dataset of both normal and anomalous monitoring data collected from various environments, facilitating the training of machine learning models and the evaluation of new algorithms for anomaly diagnosis.

Inner-product estimation is the base of many important tasks in a variety of big data scenarios, including measuring similarity of streams in data stream processing, estimating join size in database, and analyzing cosine similarity in various applications. Sketch, as a class of probability algorithms, is promising in inner-product estimation. However, existing sketch solutions suffer from low accuracy due to their neglect of the high skewness of real data. In this paper, we design a new sketch algorithm for accurate and unbiased inner-product estimation, namely JoinSketch. To improve accuracy, JoinSketch consists of multiple components, and records items with different frequency in different components. We theoretically prove that JoinSketch is unbiased, and has lower variance compared with the well-known AGMS and Fast-AGMS sketch. The experimental results show that JoinSketch improves the accuracy by 10 times in average while maintaining a comparable speed. All code is open-sourced at Github.

Query optimization based on deep reinforcement learning (DRL) has become a hot research topic recently. Despite the achieved promising progress, DRL optimizers still face great challenges of robustly producing efficient plans, due to the vast search space for both join order and operator selection and the highly varying execution latency taken as the feedback signal. In this paper, we propose LOGER, a learned optimizer towards generating efficient and robust plans, aiming at producing both efficient join orders and operators. LOGER first utilizes Graph Transformer to capture relationships between tables and predicates. Then, the search space is reorganized, in which LOGER learns to restrict specific operators instead of directly selecting one for each join, while utilizing DBMS built-in optimizer to select physical operators under the restrictions. Such a strategy exploits expert knowledge to improve the robustness of plan generation while offering sufficient plan search flexibility. Furthermore, LOGER introduces ε -beam search, which keeps multiple search paths that preserve promising plans while performing guided exploration. Finally, LOGER introduces a loss function with reward weighting to further enhance performance robustness by reducing the fluctuation caused by poor operators, and log transformation to compress the range of rewards. We conduct experiments on Join Order Benchmark (JOB), TPC-DS and Stack Overflow, and demonstrate that LOGER can achieve a performance better than existing learned query optimizers, with a 2.07x speedup on JOB compared with PostgreSQL.

Finding persistent and low-active activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of PISketch is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4, FPGA, and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of PISketch is around 22.1% and 57.6% higher than two strawman solutions, respectively; 2) The Average Relative Error (ARE) of PISketch is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively; 3) The insertion throughput of PISketch is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub.

1Data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learner. However, while most CASH methods focus on searching for a single learner with the best performance, they neglect the diversity among base learners (i.e., they may suggest similar configurations to previously evaluated ones), which is also a crucial consideration when building an ensemble. To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems. In the framework, we propose to use a diversity surrogate to predict the pair-wise diversity of two unseen configurations. Furthermore, we introduce a temporary pool and a weighted acquisition function to guide the search of both performance and diversity based on Bayesian optimization. Empirical results on 15 public datasets show that DivBO achieves the best average ranks (1.82 and 1.73) on both validation and test errors among 10 compared methods, including post-hoc designs in recent AutoML systems and state-of-the-art baselines for ensemble learning on CASH problems.

Summary With the development of the Internet of Things (IoT) technology, various attacks and threats have emerged. The advanced persistent threat (APT) refers to a class of advanced multiple‐steps attacks among diverse attack activities, which brings severe threats to the IoT systems ascribe to its pertinence, concealment, and permeability. However, the existing technologies and methods fail to timely recognize the APT attack activities (especially the zero‐day exploits) in a comprehensive scope. To address this problem, we propose a novel method of cyber situation perception for IoT systems, which based on zero‐day attack activity recognition within APT (CSPAPTM). Moreover, we also design an edge computing framework for applying CSPAPTM to the typical IoT systems. Specifically, we first provide a cyber situation perception ontology construction module for describing the APT attack activities. Then, a malicious C&C DNS mining method (MCCDRM) is proposed to control the APT malicious activity correlation analysis trigger, which can effectively decrease the computing overhead. Finally, we propose a zero‐day attack activity recognition method within APT (ZDAARA), which acts on system call instances to recognize the malicious activities, which cannot be detected by IDS. A relatively mature access control mechanism PO‐SAAC is also applied to our method. Through the coalescent of these methods, CSPAPTM can accomplish the cyber situation perception effectively by the zero‐day attack activities recognition in the IoT systems. The exhaustive experimental results demonstrate that the two kernel modules, that is, MCCDRM and ZDAARA in our CSPAPTM, can achieve both higher F1 score and acceptable false positive rate.

With the gradual increase of network complexity and network scale in the cloud environment, Root Cause Analysis (RCA) of node failures has become a systematic problem of great research significance. This paper proposes Graph-Attention-Sage (GASage) algorithm, which is a fault RCA algorithm and scheme. The algorithm solves the RCA by incorporating TOPK sampling and Attention-Aggregation with GraphSage algorithm in large-scale and complex microservice network environment. The GASage algorithm is based on graph convolutional neural network and graph attention mechanism, which profoundly combines the characteristics of network fault RCA problems. TOPK sampling mechanism is applied in GASage to select the neighboring nodes with the top [Formula: see text] highest correlation as the objectives to be aggregated. GASage adopts an attention mechanism when aggregated, which aggregates the features of the central node according to the weights of the adjacent nodes and the central node. The weight aggregation method can greatly improve the node representation effect in the RCA scenario. The empirical results of our experiments have demonstrated that the model learned with GASage can outperform other model with the same learning framework and achieves more than 10% improvement in precision.

Multitask optimization aims to solve two or more optimization tasks simultaneously by leveraging intertask knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this article proposes an evolutionary many-task optimization algorithm based on a multisource knowledge transfer mechanism, namely, EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in terms of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.

Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text.To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification.However, currently only a few simple studies have focused on this problem.The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments.In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a review sentence, and jointly predict the sentiment for each of the identified categories.Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments.Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model consistently achieves the best results on four benchmarks.

NVMe (Non-Volatile Memory Express) is a protocol designed specifically for SSD (Solid State Drive), which has significantly improved the performance of SSD storage devices. However, the traditional kernel-space IO path hinders the performance of NVMe SSD devices. In this paper, a user-space raw file system (URFS) based on NVMe SSD is proposed. Through the design of the user-space multi-process shared cache, multiple applications can share access to SSD to reduce the amount of SSD access; NVMe-oriented log-free data layout and Multi-granularity IO queue elastic separation technology are used to improve system performance and throughput. Experiments show that, compared to traditional file systems, URFS performance is improved by more than 23% in CDN (Content Delivery Network) scenarios, and URFS performance is improved more in small file scenarios and read-intensive scenarios.

Flash and RDMA (Remote Direct Memory Access) provide extremely high performance in storage and network hardware. However, a gap between distributed system and new hardware exists. Although RDMA speeds up memory access between two nodes, there are many serious problems to be solved when sending data or command from one flash to another flash. In this paper, we propose a distributed system on flash and RDMA, and implement a User-space Sendfile verb based on it. Experimental results show that the RPC in DSFR outperforms the traditional RPC mechanism by dozens of times, and the uSendfile reduces time overhead significantly.