IEEE Transactions on Knowledge and Data Engineering最新文献_第6页

Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization 混合量化的隐式多行为生成推荐

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-20 DOI: 10.1109/TKDE.2025.3572014

Yuze Tan;Yanjie Gou;Kouying Xue;Shudong Huang;Yi Hu;Ivor W. Tsang;Jiancheng Lv

{"title":"Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization","authors":"Yuze Tan;Yanjie Gou;Kouying Xue;Shudong Huang;Yi Hu;Ivor W. Tsang;Jiancheng Lv","doi":"10.1109/TKDE.2025.3572014","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3572014","url":null,"abstract":"Generative recommendation systems have recently seen a surge in interest, largely due to the promising advancements in generative AI. As a competitive solution for multi-behavior sequence recommendations, much of the recent research has concentrated on predicting the next item a user will likely interact with using a generative approach. However, these methods often 1). assign multiple residual quantization layers to obtain item codes, which leads to extra storage costs of more codebooks. And 2). explicitly utilize behavior sequences leading to longer sequences, potentially increasing the training time as well as inference time compared with original sequences. In response to these challenges, we introduce the <bold>Implicit <bold>Multi-<bold>Behavior <bold>Generative recommendation with a mixture of quantization (IMBGen) approach in this paper. Specifically, we have devised a <bold>Mixture <bold>of <bold>Quantization (MoQ) that combines the merits of both residual and parallel quantization for a more effective tokenization process. Additionally, we propose an Implicit Behavior Modeling (IBM) framework, allowing for more efficient integration of users’ behaviors into the interacted items. Finally, we conducted extensive experiments on two widely used benchmark datasets and further confirmed our findings with an online A/B test. The results consistently demonstrate the advantages of our approach over other baseline methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4704-4715"},"PeriodicalIF":8.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QSTGNN: Quaternion Spatio-Temporal Graph Neural Networks 四元数时空图神经网络

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-20 DOI: 10.1109/TKDE.2025.3571983

Ye Liu;Chaoxiong Lin;Yuchen Mou;Huaiguang Jiang;Hongmin Cai

{"title":"QSTGNN: Quaternion Spatio-Temporal Graph Neural Networks","authors":"Ye Liu;Chaoxiong Lin;Yuchen Mou;Huaiguang Jiang;Hongmin Cai","doi":"10.1109/TKDE.2025.3571983","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3571983","url":null,"abstract":"Spatio-temporal time series forecasting has attracted great attentions in various fields, including climate, power, and traffic forecasting. Recently, Spatio-temporal Graph Neural Networks (STGNNs) have shown promising performances in modeling spatial dependencies based on graph neural networks (GNNs) and temporal dependencies based on temporal learning modules. However, most STGNNs do not effectively integrate explicit and implicit relationships between nodes, nor do they adequately capture long and short-term time dependencies. To address these challenges, this paper presents a Quaternion Spatio-temporal Graph Neural Network (QSTGNN). Specifically, the quaternion spatio-temporal graph is constructed firstly, such that the information of both short and long-term time steps are preserved in quaternion feature tensor, and information of multiple explicit graphs and implicit graph are integrated in quaternion graph adjacency matrix. Then, two modules are designed: a 1D quaternion convolution module and a quaternion graph convolution module. In the 1D quaternion convolution module, complex temporal correlations among short and long-term time steps can be well exploited by 1D quaternion convolution operator based on the quaternion Hamilton product. In the quaternion graph convolution module, quaternion graph convolution is designed to characterize nonlinear dependencies among multiple spatial graphs, including explicit and implicit graphs. Extensive experiments are conducted on six datasets, and the results show that QSTGNN achieves state-of-the-art performances over the existing ten methods. Explainable analysis presents that multiple spatial correlations can accurately illustrate the traffic flow and road functional information in real traffic roads.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4776-4790"},"PeriodicalIF":8.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Based Knowledge Tracing: A Review, a Tool and Empirical Studies 基于深度学习的知识追踪：综述、工具与实证研究

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-19 DOI: 10.1109/TKDE.2025.3552759

Zitao Liu;Teng Guo;Qianru Liang;Mingliang Hou;Bojun Zhan;Jiliang Tang;Weiqi Luo;Jian Weng

{"title":"Deep Learning Based Knowledge Tracing: A Review, a Tool and Empirical Studies","authors":"Zitao Liu;Teng Guo;Qianru Liang;Mingliang Hou;Bojun Zhan;Jiliang Tang;Weiqi Luo;Jian Weng","doi":"10.1109/TKDE.2025.3552759","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3552759","url":null,"abstract":"Knowledge tracing (KT) involves utilizing historical data from students’ learning interactions to model their mastery of knowledge over time, with the aim of predicting their future performance in interactions. Recently, significant advancements have been achieved through the application of various deep learning methodologies to address the KT challenge. However, a considerable proportion of deep learning-based knowledge tracing (DLKT) approaches exhibit striking similarities in their methodologies, and model designs, and even the outcomes demonstrate minimal divergence. In addition, the evaluation procedures employed in current DLKT studies are not standardized, resulting in substantial inconsistencies in the reported area under the curve (AUC) outcomes, despite analyzing the same model on identical datasets. To address the two aforementioned problems, this paper proposes a generalized DLKT framework and represents the existing DLKT models with five components, i.e., multimodal data encoder, student knowledge memory, auxiliary knowledge base, learning outcome objective, and computational efficiency and scalability. Furthermore, we develop and open source a standardized DLKT benchmark platform named <sc>pyKT,1 that consists of a standardized set of integrated data preprocessing procedures on 9 popular datasets across different domains, and 21 frequently compared DLKT model implementations. With <sc>pyKT, we conduct empirical and reproducible research to assess the performance of prevalent DLKT algorithms in an unbiased and clear setting over multiple data sources. Finally, we discuss the applications of KT techniques in the educational sector and their future development directions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4512-4536"},"PeriodicalIF":8.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges 通过添加边最小化Kirchhoff指数的有效算法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-18 DOI: 10.1109/TKDE.2025.3552644

Xiaotian Zhou;Ahad N. Zehmakan;Zhongzhi Zhang

{"title":"Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges","authors":"Xiaotian Zhou;Ahad N. Zehmakan;Zhongzhi Zhang","doi":"10.1109/TKDE.2025.3552644","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3552644","url":null,"abstract":"The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3342-3355"},"PeriodicalIF":8.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LOFTune: A Low-Overhead and Flexible Approach for Spark SQL Configuration Tuning LOFTune：一种低开销和灵活的Spark SQL配置调优方法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-18 DOI: 10.1109/TKDE.2025.3549232

Jiahui Li;Junhao Ye;Yuren Mao;Yunjun Gao;Lu Chen

{"title":"LOFTune: A Low-Overhead and Flexible Approach for Spark SQL Configuration Tuning","authors":"Jiahui Li;Junhao Ye;Yuren Mao;Yunjun Gao;Lu Chen","doi":"10.1109/TKDE.2025.3549232","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3549232","url":null,"abstract":"The query efficiency of Spark SQL is significantly impacted by its configurations. Therefore, configuration tuning has drawn great attention, and various automatic configuration tuning methods have been proposed. However, existing methods suffer from two issues: (1) high tuning overhead: they need to repeatedly execute the workloads several times to obtain the training samples, which is time-consuming; and (2) low throughput: they need to occupy resources like CPU cores and memory for a long time, causing other Spark SQL workloads to wait, thereby reducing the overall system throughput. These issues impede the use of automatic configuration tuning methods in practical systems which have limited tuning budget and many concurrent workloads. To address these issues, this paper proposes a <bold>Low-<bold>Overhead and <bold>Flexible approach for Spark SQL configuration <bold>Tuning, dubbed <bold>LOFTune. LOFTune reduces the tuning overhead via a sample-efficient optimization framework, which is proposed based on multi-task SQL representation learning and multi-armed bandit. Furthermore, LOFTune solves the low throughput issue with a recommendation-sampling-decoupled tuning framework. Extensive experiments validate the effectiveness of LOFTune. In the sampling-allowed case, LOFTune can save up to 90% of the workload runs comparing with the state-of-the-art methods. Besides, in the zero-sampling case, LOFTune can reduce up to 41.26% of latency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3528-3542"},"PeriodicalIF":8.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zkfhed: A Verifiable and Scalable Blockchain-Enhanced Federated Learning System Zkfhed：一个可验证和可扩展的区块链增强联邦学习系统

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-17 DOI: 10.1109/TKDE.2025.3550546

Bingxue Zhang;Guangguang Lu;Yuncheng Wu;Kunpeng Ren;Feida Zhu

{"title":"Zkfhed: A Verifiable and Scalable Blockchain-Enhanced Federated Learning System","authors":"Bingxue Zhang;Guangguang Lu;Yuncheng Wu;Kunpeng Ren;Feida Zhu","doi":"10.1109/TKDE.2025.3550546","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3550546","url":null,"abstract":"Federated learning (FL) is an emerging paradigm that enables multiple clients to collaboratively train a machine learning (ML) model without the need to exchange their raw data. However, it relies on a centralized authority to coordinate participants’ activities. This not only interrupts the entire training task in case of a single point of failure, but also lacks an effective regulatory mechanism to prevent malicious behavior. Although blockchain, with its decentralized architecture and data immutability, has significantly advanced the development of FL, it still struggles to withstand poisoning attacks and faces limitations in computational scalability. We propose Zkfhed, a verifiable and scalable FL system that overcomes the limitations of blockchain-based FL in poison attacks and computational scalability. First, we propose a two-stage audit scheme based on zero-knowledge proofs (ZKPs), which verifies that the training data are extracted from trusted organizations and that computations on the data exactly follow the specified training protocols. Second, we propose a homomorphic encryption delegation learning (HEDL), based on fully homomorphic encryption (FHE). It is capable of outsourcing complex computing to external computing resources without sacrificing the client's data privacy. Final, extensive experiments on real-world datasets demonstrate that Zkfhed can effectively identify malicious clients and is highly efficient and scalable in terms of online time and communication efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3841-3854"},"PeriodicalIF":8.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiscale Weisfeiler-Leman Directed Graph Neural Networks for Prerequisite-Link Prediction 多尺度Weisfeiler-Leman有向图神经网络用于前提链路预测

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-17 DOI: 10.1109/TKDE.2025.3552045

Yupei Zhang;Xiran Qu;Shuhui Liu;Yan Pang;Xuequn Shang

{"title":"Multiscale Weisfeiler-Leman Directed Graph Neural Networks for Prerequisite-Link Prediction","authors":"Yupei Zhang;Xiran Qu;Shuhui Liu;Yan Pang;Xuequn Shang","doi":"10.1109/TKDE.2025.3552045","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3552045","url":null,"abstract":"Prerequisite-link Prediction (PLP) aims to discover the condition relations of a specific event or a concerned variable, which is a fundamental problem in a large number of fields, such as educational data mining. Current studies on PLP usually developed graph neural networks (GNNs) to learn the representations of pairs of nodes. However, these models fail to distinguish non-isomorphic graphs and integrate multiscale structures, leading to the insufficient expressive capability of GNNs. To this end, we in this paper proposed <italic>k-dimensional Weisferiler-Leman directed GNNs, dubbed <italic>k-WediGNNs, to recognize non-isomorphic graphs via the Weisferiler-Leman algorithm. Furthermore, we integrated the multiscale structures of a directed graph into <italic>k-WediGNNs, dubbed multiscale <italic>k-WediGNNs, from the bidirected views of in-degree and out-degree. With the Siamese network, the proposed models are extended to address the problem of PLP. Besides, the expressive power is then interpreted via theoretical proofs. The experiments were conducted on four publicly available datasets for concept prerequisite relation prediction (CPRP). The results show that the proposed models achieve better performance than the state-of-the-art approaches, where our multiscale <italic>k-WediGNN achieves a new benchmark in the task of CPRP.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3556-3569"},"PeriodicalIF":8.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Searching and Querying Maximum Directed $(k,ell )$(k,ℓ)-Plex 最大有向$(k, well)$(k, r)-Plex的搜索与查询

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-16 DOI: 10.1109/TKDE.2025.3569755

Shuohao Gao;Kaiqiang Yu;Shengxin Liu;Cheng Long;Xun Zhou

{"title":"On Searching and Querying Maximum Directed $(k,ell )$(k,ℓ)-Plex","authors":"Shuohao Gao;Kaiqiang Yu;Shengxin Liu;Cheng Long;Xun Zhou","doi":"10.1109/TKDE.2025.3569755","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3569755","url":null,"abstract":"Finding cohesive subgraphs from a directed graph is a fundamental approach to analyze directed graph data. We consider a new model called directed <inline-formula><tex-math>$(k,ell )$</tex-math></inline-formula>-plex for a cohesive directed subgraph, which is generalized from the concept of <inline-formula><tex-math>$k$</tex-math></inline-formula>-plex that is only applicable to undirected graphs. Directed <inline-formula><tex-math>$(k,ell )$</tex-math></inline-formula>-plex (or DPlex) has the connection requirements on both inbound and outbound directions of each vertex inside, i.e., each vertex disconnects at most <inline-formula><tex-math>$k$</tex-math></inline-formula> vertices and is meanwhile not pointed to by at most <inline-formula><tex-math>$ell$</tex-math></inline-formula> vertices. In this paper, we study the maximum DPlex search problem which finds a DPlex with the most vertices. We formally prove the NP-hardness of the problem. We then design a heuristic algorithm called <monospace>DPHeuris</monospace>, which finds a DPlex with the size close to the maximum one and runs practically fast in polynomial time. Furthermore, we propose a branch-and-bound algorithm called <monospace>DPBB</monospace> to find the exact maximum DPlex and develop effective graph reduction strategies for boosting the empirical performance. We also consider the problem of querying personalized maximum DPlex, and design a new method called <monospace>DPBBQ</monospace> for the problem. Finally, we conduct extensive experiments on real directed graphs. The experimental results show that (1) our heuristic method can quickly find a near-optimal solution and (2) our branch-and-bound method runs up to six orders of magnitude faster than other baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4743-4757"},"PeriodicalIF":8.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-Based Clustering: High-Order Bipartite Graph for Proximity Learning 基于图的聚类：用于接近学习的高阶二部图

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-16 DOI: 10.1109/TKDE.2025.3569681

Zihua Zhao;Danyang Wu;Rong Wang;Zheng Wang;Feiping Nie;Xuelong Li

{"title":"Graph-Based Clustering: High-Order Bipartite Graph for Proximity Learning","authors":"Zihua Zhao;Danyang Wu;Rong Wang;Zheng Wang;Feiping Nie;Xuelong Li","doi":"10.1109/TKDE.2025.3569681","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3569681","url":null,"abstract":"Structured proximity matrix learning, one of the mainstream directions in clustering research, refers to learning a proximity matrix with an explicit clustering structure from the original first-order proximity matrix. Due to the complexity of the data structure, the original first-order proximity matrix always lacks some must-links compared to the groundtruth proximity matrix. It is worth noting that high-order proximity matrices can provide missed must-link information. However, the computation of high-order proximity matrices and clustering based on them are expensive. To solve the above problem, inspired by the anchor bipartite graph, we present a novel high-order bipartite graph proximity matrix and a fast method to compute it. This proposed high-order bipartite graph proximity matrix contains high-order proximity information and can significantly reduce the computational complexity of the whole clustering process. Furthermore, we introduce an efficient and simple high-order bipartite graph fusion framework that can adaptively assign weights to each order of the high-order bipartite graph matrices. Finally, under the Laplace rank constraint, a consensus structured bipartite graph proximity matrix is obtained. At the same time, an efficient solution algorithm is proposed for this model. The model's efficacy is underscored through rigorous experiments, highlighting its superior clustering performance and time efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4649-4663"},"PeriodicalIF":8.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genie: A Lightweight Serverless Infrastructure for In-Memory Key-Value Caching With Fine-Grained and Prompt Elasticity Genie：用于内存中键值缓存的轻量级无服务器基础设施，具有细粒度和提示弹性

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-15 DOI: 10.1109/TKDE.2025.3556427

Huijuan Xiao;Shixi Yang;Kai Zhang;Yinan Jing;Zhenying He;X. Sean Wang

{"title":"Genie: A Lightweight Serverless Infrastructure for In-Memory Key-Value Caching With Fine-Grained and Prompt Elasticity","authors":"Huijuan Xiao;Shixi Yang;Kai Zhang;Yinan Jing;Zhenying He;X. Sean Wang","doi":"10.1109/TKDE.2025.3556427","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3556427","url":null,"abstract":"An increasing number of web applications require cloud in-memory key-value stores to minimize latency and achieve higher throughput. They generally have diverse characteristics and constantly changing traffic volumes, which require different computational and memory resources. A serverless in-memory key-value store characterized by elastic resource allocation and pay-as-you-go billing could satisfy the requirements of diverse and dynamic workloads. However, we find current serverless IMKVs fail to achieve fine-grained and prompt resource elasticity due to the limitations of their infrastructures. This paper proposes Genie, a lightweight serverless infrastructure for in-memory key-value caching with fine-grained and immediate elasticity. In Genie, a novel approach is adopted to enable dynamic and independent resource allocation to multiple tenants. It processes all arrived requests and estimates the vCPU consumption with a lightweight machine-learning approach for fine-grained billing. Moreover, Genie estimates the working set and dynamically resizes the allocated memory for hit ratio requirements. Evaluation results show that CPU estimation could be achieved every 100 microseconds without impacting system performance, and memory capacity could be adjusted by megabytes within seconds. The holistic design incurs 1% -2% performance degradation compared to our baseline. Moreover, Genie achieves an average of 58.3% CPU and 49.9% memory savings compared to AsparaDB for Memcache.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 7","pages":"4089-4103"},"PeriodicalIF":8.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0