IEEE Transactions on Cloud Computing最新文献_第5页

StreamSys: A Lightweight Executable Delivery System for Edge Computing StreamSys：用于边缘计算的轻量级可执行文件交付系统

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-24 DOI: 10.1109/TCC.2024.3521978

Jun Lu;Zhenya Ma;Yinggang Gao;Sheng Yue;Ju Ren;Yaoxue Zhang

{"title":"StreamSys: A Lightweight Executable Delivery System for Edge Computing","authors":"Jun Lu;Zhenya Ma;Yinggang Gao;Sheng Yue;Ju Ren;Yaoxue Zhang","doi":"10.1109/TCC.2024.3521978","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521978","url":null,"abstract":"Edge computing brings several challenges when it comes to data movement. First, moving large data from edge devices to the server is likely to waste bandwidth. Second, complex data patterns (e.g., traffic cameras) on devices require flexible handling. An ideal approach is to move code to data instead. However, since only a small portion of code is required, moving the executable as well as their libraries to the devices can be an overkill. While loading code on demand from remote such as NFS can be a stopgap, but on the other hand leads to low efficiency for irregular access patterns. This article presents <sc>StreamSys, a lightweight executable delivery system that loads code on demand by redirecting the local disk IO to the server through optimized network IO. We employ a Markov-based prefetch mechanism on the server side. It learns the access pattern of code and predicts the block sequence for the client to reduce the network round trip. Meanwhile, server-side <sc>StreamSys asynchronously prereads the block sequence from the disk to conceal disk IO latency beforehand. Evaluation shows that the latency of <sc>StreamSys is up to 71.4% lower than the native Linux file system based on SD card and up to 62% lower than NFS in wired environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"213-226"},"PeriodicalIF":5.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570779","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

AI Applications Resource Allocation in Computing Continuum: A Stackelberg Game Approach 计算连续体中的人工智能应用资源分配：一个Stackelberg博弈方法

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-20 DOI: 10.1109/TCC.2024.3521213

Roberto Sala;Hamta Sedghani;Mauro Passacantando;Giacomo Verticale;Danilo Ardagna

{"title":"AI Applications Resource Allocation in Computing Continuum: A Stackelberg Game Approach","authors":"Roberto Sala;Hamta Sedghani;Mauro Passacantando;Giacomo Verticale;Danilo Ardagna","doi":"10.1109/TCC.2024.3521213","DOIUrl":"https://doi.org/10.1109/TCC.2024.3521213","url":null,"abstract":"The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"166-183"},"PeriodicalIF":5.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570676","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

Two-Stage Learning Approach for Semantic-Aware Task Scheduling in Container-Based Clouds 基于容器云中语义感知任务调度的两阶段学习方法

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-19 DOI: 10.1109/TCC.2024.3520101

Lilu Zhu;Kai Huang;Yanfeng Hu;Yang Wang

{"title":"Two-Stage Learning Approach for Semantic-Aware Task Scheduling in Container-Based Clouds","authors":"Lilu Zhu;Kai Huang;Yanfeng Hu;Yang Wang","doi":"10.1109/TCC.2024.3520101","DOIUrl":"https://doi.org/10.1109/TCC.2024.3520101","url":null,"abstract":"Container-based task scheduling is critical for ensuring a reliable, flexible and cost-effective cloud computing mode. However, in different business cloud systems, state-of-the-art scheduling models are not as effective as those in the simulated world due to the sparsity issues associated with sample sizes and features. Herein, we propose a novel containerized task scheduling framework (SA2CTS) based on reinforcement learning (RL) that incorporates cross-modal contrastive learning (CL) loss. This framework optimizes the scheduler's understanding of the container-based cloud state in RL by adding a pretraining stage, promoting accurate scheduling action inference. Specifically, we design a two-stage learning pipeline. The initial stage involves pretraining the model on a large collection of aligned image-text pairs to extract fine-grained scheduling affinity features, and the high-level semantic representations of scheduling tasks are learned in the multimodal space. In the second stage, we fine-tune the pretrained model with multisource cluster feedback, i.e., build a mapping from state representations to scheduling actions through the RL paradigm, achieving task-oriented and semantic-aware scheduling. The experimental results obtained on three large-scale production cluster datasets substantiate that the proposed SA2CTS method can provide average convergence efficiency and resource utilization improvements of 17.57% and 10.42%, respectively, over the state-of-the-art RL scheduling methods.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"148-165"},"PeriodicalIF":5.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570726","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

IBNR-RD: Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication IBNR-RD：基于块内邻域关系的高性能多节点重复数据删除后相似性检测

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-09 DOI: 10.1109/TCC.2024.3514784

Dewen Zeng;Wenlong Tian;Tingting He;Ruixuan Li;Xuming Ye;Zhiyong Xu

{"title":"IBNR-RD: Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication","authors":"Dewen Zeng;Wenlong Tian;Tingting He;Ruixuan Li;Xuming Ye;Zhiyong Xu","doi":"10.1109/TCC.2024.3514784","DOIUrl":"https://doi.org/10.1109/TCC.2024.3514784","url":null,"abstract":"Post-deduplication in traditional cloud environments primarily focuses on single-node, where delta compression is performed on the same deduplication node located on server side. However, with data explosion, the multi-node post-deduplication, also called global deduplication, has become a hot issue in research communities, which aims to simultaneously execute delta compression on data distributed across all nodes. Simply setting up single-node deduplication systems on multi-node environments would significantly affect storage utilization and incur secondary overhead from file migration. Nevertheless, existing global deduplication solutions suffer from lower data compression ratios and high computational overhead due to their resemblance detection's inherent limitations and overly coarse granularities. Similar blocks typically have high correlations between sub-blocks; inspired by this observation, we propose IBNR (Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication), which introduces a novel resemblance detection based on relationships between sub-blocks and determines the ownership of blocks in entry stage to achieve efficient global deduplication. Furthermore, the by-products of IBNR have shown powerful scalability by replacing internal resemblance detection scheme with existing solutions on practical workloads. Experimental results indicate that IBNR outperforms state-of-the-art solutions, achieving an average 1.99× data reduction ratio and varying degrees of improvement across other key metrics.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"118-129"},"PeriodicalIF":5.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570723","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

SST-LOF: Container Anomaly Detection Method Based on Singular Spectrum Transformation and Local Outlier Factor SST-LOF：基于奇异频谱变换和局部离群因子的集装箱异常检测方法

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-09 DOI: 10.1109/TCC.2024.3514297

Shilei Bu;Minpeng Jin;Jie Wang;Yulai Xie;Liangkang Zhang

{"title":"SST-LOF: Container Anomaly Detection Method Based on Singular Spectrum Transformation and Local Outlier Factor","authors":"Shilei Bu;Minpeng Jin;Jie Wang;Yulai Xie;Liangkang Zhang","doi":"10.1109/TCC.2024.3514297","DOIUrl":"https://doi.org/10.1109/TCC.2024.3514297","url":null,"abstract":"In recent years, the use of container cloud platforms has experienced rapid growth. However, because containers are operating-system-level virtualization, their isolation is far less than that of virtual machines, posing considerable challenges for multi-tenant container cloud platforms. To address the issues associated with current container anomaly detection algorithms, such as the difficulty in mining periodic features and the high rate of false positives due to noisy data, we propose an anomaly detection method named SST-LOF, based on singular spectrum transformation and the local outlier factor. Our method enhances the traditional Singular Spectrum Transformation (SST) algorithm to meet the needs of streaming unsupervised detection. Furthermore, our method improves the calculation mode of the anomaly score of the Local Outlier Factor algorithm (LOF) and reduces false positives of noisy data with dynamic sliding windows. Additionally, we have designed and implemented a container cloud anomaly detection system that can perform real-time, unsupervised, streaming anomaly detection on containers quickly and accurately. The experimental results demonstrate the effectiveness and efficiency of our method in detecting anomalies in containers in both simulated and real cloud environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"130-147"},"PeriodicalIF":5.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570675","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 Dynamic Resource Management for Spatial Multitasking GPUs 空间多任务gpu的高效动态资源管理

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-05 DOI: 10.1109/TCC.2024.3511548

Hoda Sedighi;Daniel Gehberger;Amin Ebrahimzadeh;Fetahi Wuhib;Roch H. Glitho

{"title":"Efficient Dynamic Resource Management for Spatial Multitasking GPUs","authors":"Hoda Sedighi;Daniel Gehberger;Amin Ebrahimzadeh;Fetahi Wuhib;Roch H. Glitho","doi":"10.1109/TCC.2024.3511548","DOIUrl":"https://doi.org/10.1109/TCC.2024.3511548","url":null,"abstract":"The advent of microservice architecture enables complex cloud applications to be realized via a set of individually isolated components, increasing their flexibility and performance. As these applications require massive computing resources, graphics processing units (GPUs) are being widely used as high-speed parallel computing devices to meet the stringent demands. Although current GPUs allow application components to be executed concurrently via spatial multitasking, they face several challenges. The first challenge is allocating the computing resources to components dynamically to maximize efficiency. The second challenge is avoiding performance degradation caused by the data transfer overhead between the components. To address these challenges, we propose an efficient GPU resource management technique that dynamically allocates GPU resources to application components. The proposed method allocates resources based on component workloads and uses online performance monitoring to guarantee the application's performance. We also propose a GPU memory manager to reduce the data transfer overhead between components via shared memory. Our evaluation results indicate that the proposed dynamic resource allocation method improves application throughput by up to 134.12% compared to the state-of-the-art spatial multitasking techniques. We also show that using a shared memory results in 6x throughput improvement compared to the baseline User Datagram Protocol (UDP)-based technique.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"99-117"},"PeriodicalIF":5.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570597","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

Optical Self-Adjusting Data Center Networks in the Scalable Matching Model 可扩展匹配模型中的光自调整数据中心网络

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-12-04 DOI: 10.1109/TCC.2024.3510916

Caio Alves Caldeira;Otávio Augusto de Oliveira Souza;Olga Goussevskaia;Stefan Schmid

{"title":"Optical Self-Adjusting Data Center Networks in the Scalable Matching Model","authors":"Caio Alves Caldeira;Otávio Augusto de Oliveira Souza;Olga Goussevskaia;Stefan Schmid","doi":"10.1109/TCC.2024.3510916","DOIUrl":"https://doi.org/10.1109/TCC.2024.3510916","url":null,"abstract":"Self-Adjusting Networks (SAN) optimize their physical topology toward the demand in an online manner. Their application in data center networks is motivated by emerging hardware technologies, such as 3D MEMS Optical Circuit Switches (OCS). The Matching Model (MM) has been introduced to study the hybrid architecture of such networks. It abstracts from the electrical switches and focuses on the added (reconfigurable) optical ones. MM defines any SAN topology as a union of matchings over a set of top-of-rack (ToR) nodes, and assumes that rearranging the edges of a single matching comes at a fixed cost. In this work, we propose and study the Scalable Matching Model (SMM), a generalization of the MM, and present OpticNet, a framework that maps a set of ToRs to a set of OCSs to form a SAN topology. We prove that OpticNet uses the minimum number of switches to realize any bounded-degree topology and allows existing SAN algorithms to run on top of it, while preserving amortized performance guarantees. Our experimental results based on real workloads show that OpticNet is a flexible and efficient framework for the implementation and evaluation of SAN algorithms in reconfigurable data center environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"87-98"},"PeriodicalIF":5.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570781","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

An Efficient Delegatable Order-Revealing Encryption Scheme for Multi-User Range Queries 一种高效的多用户范围查询可委托顺序加密方案

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-27 DOI: 10.1109/TCC.2024.3506614

Jingru Xu;Cong Peng;Rui Li;Jintao Fu;Min Luo

{"title":"An Efficient Delegatable Order-Revealing Encryption Scheme for Multi-User Range Queries","authors":"Jingru Xu;Cong Peng;Rui Li;Jintao Fu;Min Luo","doi":"10.1109/TCC.2024.3506614","DOIUrl":"https://doi.org/10.1109/TCC.2024.3506614","url":null,"abstract":"To balance data confidentiality and availability, order-revealing encryption (ORE) has emerged as a pivotal primitive facilitating range queries on encrypted data. However, challenges arise in diverse user domains where data is encrypted with different keys, giving rise to the development of delegatable order-revealing encryption (DORE) schemes. Regrettably, existing DORE schemes are susceptible to authorization token forgery attacks and rely on computationally intensive bilinear pairings. This work proposes a novel solution to address these challenges. We first introduce a delegatable equality-revealing encryption scheme, enabling the comparison of ciphertexts encrypted by distinct secret keys through authorization tokens. Building upon this, we present a delegatable order-revealing encryption that leverages bitwise encryption. DORE supports efficient multi-user ciphertext comparison while robustly resisting authorization token forgery attacks. Significantly, our approach distinguishes itself by minimizing bilinear pairings. Experimental results highlight the efficacy of DORE, showcasing a notable speedup of <inline-formula><tex-math>$2.8times$</tex-math></inline-formula> in encryption performance and <inline-formula><tex-math>$1.33times$</tex-math></inline-formula> in comparison performance compared to previous DORE schemes, respectively.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"75-86"},"PeriodicalIF":5.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570727","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

A Run-Time Framework for Ensuring Zero-Trust State of Client’s Machines in Cloud Environment 确保云环境中客户机零信任状态的运行时框架

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-20 DOI: 10.1109/TCC.2024.3503358

Devki Nandan Jha;Graham Lenton;James Asker;David Blundell;Martin Higgins;David C. H. Wallom

{"title":"A Run-Time Framework for Ensuring Zero-Trust State of Client’s Machines in Cloud Environment","authors":"Devki Nandan Jha;Graham Lenton;James Asker;David Blundell;Martin Higgins;David C. H. Wallom","doi":"10.1109/TCC.2024.3503358","DOIUrl":"https://doi.org/10.1109/TCC.2024.3503358","url":null,"abstract":"With the unprecedented demand for cloud computing, ensuring trust in the underlying environment is challenging. Applications executing in the cloud are prone to attacks of different types including malware, network and data manipulation. These attacks may remain undetected for a significant length of time thus causing a lack of trust. Untrusted cloud services can also lead to business losses in many cases and therefore need urgent attention. In this paper, we present <italic>Trusted Public Cloud (<sc>TPC), a generic framework ensuring the <italic>Zero-trust security of client machine. It tracks the system state, alerting the user of unexpected changes in the machine’s state, thus increasing the run-time detection of security vulnerabilities. We validated <sc>TPC on Microsoft Azure with Local, Software Trusted Platform Module (SWTPM) and Software Guard Extension (SGX)-enabled SWTPM security providers. We also evaluated the scalability of <sc>TPC on Amazon Web Services (AWS) with a varying number of client machines executing in a concurrent environment. The execution results show the effectiveness of <sc>TPC as it takes a maximum of 35.6 seconds to recognise the system state when there are 128 client machines attached.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"61-74"},"PeriodicalIF":5.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570760","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

HyperPart: A Hypergraph-Based Abstraction for Deduplicated Storage Systems HyperPart：基于超图的复制存储系统抽象

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-19 DOI: 10.1109/TCC.2024.3502464

Geyao Cheng;Junxu Xia;Lailong Luo;Haibo Mi;Deke Guo;Richard T. B. Ma

{"title":"HyperPart: A Hypergraph-Based Abstraction for Deduplicated Storage Systems","authors":"Geyao Cheng;Junxu Xia;Lailong Luo;Haibo Mi;Deke Guo;Richard T. B. Ma","doi":"10.1109/TCC.2024.3502464","DOIUrl":"https://doi.org/10.1109/TCC.2024.3502464","url":null,"abstract":"Currently, deduplication techniques are utilized to minimize the space overhead by deleting redundant data blocks across large-scale servers in data centers. However, such a process exacerbates the fragmentation of data blocks, causing more cross-server file retrievals with plummeting retrieval throughput. Some attempts prefer better file retrieval performance by confining all blocks of a file to one single server, resulting in non-trivial space consumption for more replicated blocks across servers. An ideal network storage system, in effect, should take both the deduplication and retrieval performance into account by implementing reasonable assignment of the detected unique blocks. Such a fine-grained assignment requires an accurate and comprehensive abstraction of the files, blocks, and the file-block affiliation relationships. To achieve this, we innovatively design the weighted hypergraph to profile the multivariate data correlations. With this delicate abstraction in place, we propose HyperPart, which elegantly transforms this complex block allocation problem into a hypergraph partition problem. For more general scenarios with dynamic file updates, we further propose a two-phase incremental hypergraph repartition scheme, which mitigates the performance degradation with minimal migration volume. We implement a prototype system of HyperPart, and the experiment results validate that it saves around 50% of the storage space and improves the retrieval throughput by approximately 30% of state-of-the-art methods under the balance constraints.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"46-60"},"PeriodicalIF":5.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570780","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