In this article, we introduce ${sf PROPHET}$ , an innovative approach to predictive process monitoring based on Heterogeneous Graph Neural Networks. ${sf PROPHET}$ is designed to strike a balance between accurate predictions and interpretability, particularly focusing on the next-activity prediction task. For this purpose, we represent the event traces recorded for different business process executions as heterogeneous graphs within a multi-view learning scheme combined with a heterogeneous graph learning approach. Using heterogeneous Graph Attention Networks (GATs), we achieve good accuracy by incorporating different characteristics of several events into graphs with different node types and leveraging different types of graph links to express relationships between event characteristics, as well as relationships between events. In addition, the use of a GAT model enables the integration of a modified version of the GNN Explainer algorithm to add the explainable component to the predictive model. In particular, the GNN Explainer algorithm is modified to disclose explainable information related to characteristics, events and relationships between events that mainly influenced the prediction. Experiments with various benchmark event logs prove the accuracy of ${sf PROPHET}$ compared to several current state-of-the-art methods and draw insights from explanations recovered through the GNN Explainer algorithm.
{"title":"${sf PROPHET}$PROPHET: Explainable Predictive Process Monitoring With Heterogeneous Graph Neural Networks","authors":"Vincenzo Pasquadibisceglie;Raffaele Scaringi;Annalisa Appice;Giovanna Castellano;Donato Malerba","doi":"10.1109/TSC.2024.3463487","DOIUrl":"10.1109/TSC.2024.3463487","url":null,"abstract":"In this article, we introduce \u0000<inline-formula><tex-math>${sf PROPHET}$</tex-math></inline-formula>\u0000, an innovative approach to predictive process monitoring based on Heterogeneous Graph Neural Networks. \u0000<inline-formula><tex-math>${sf PROPHET}$</tex-math></inline-formula>\u0000 is designed to strike a balance between accurate predictions and interpretability, particularly focusing on the next-activity prediction task. For this purpose, we represent the event traces recorded for different business process executions as heterogeneous graphs within a multi-view learning scheme combined with a heterogeneous graph learning approach. Using heterogeneous Graph Attention Networks (GATs), we achieve good accuracy by incorporating different characteristics of several events into graphs with different node types and leveraging different types of graph links to express relationships between event characteristics, as well as relationships between events. In addition, the use of a GAT model enables the integration of a modified version of the GNN Explainer algorithm to add the explainable component to the predictive model. In particular, the GNN Explainer algorithm is modified to disclose explainable information related to characteristics, events and relationships between events that mainly influenced the prediction. Experiments with various benchmark event logs prove the accuracy of \u0000<inline-formula><tex-math>${sf PROPHET}$</tex-math></inline-formula>\u0000 compared to several current state-of-the-art methods and draw insights from explanations recovered through the GNN Explainer algorithm.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4111-4124"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463496
Haoxuan Wang;Kun Xie;Xin Wang;Jigang Wen;Ruotian Xie;Zulong Diao;Wei Liang;Gaogang Xie;Jiannong Cao
Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.
{"title":"GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion","authors":"Haoxuan Wang;Kun Xie;Xin Wang;Jigang Wen;Ruotian Xie;Zulong Diao;Wei Liang;Gaogang Xie;Jiannong Cao","doi":"10.1109/TSC.2024.3463496","DOIUrl":"10.1109/TSC.2024.3463496","url":null,"abstract":"Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3713-3726"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245626","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}
Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners, and thus, must be propagated to partners affected by the change. In this paper, we propose an approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private process. A change impacting other partners is propagated to their processes in a decentralized manner using a SC. The change propagation mechanism ensures the compatibility checks between public processes of the partners and the consistency between the private and public processes of one partner. We demonstrate the approach’s feasibility through an implemented prototype and its effectiveness via a set of evaluation tests.
{"title":"Towards a Trustworthy and Adaptive Execution of Business Process Choreographies","authors":"Amina Brahem;Tiphaine Henry;Sami Bhiri;Thomas Devogele;Nizar Messai;Yacine Sam;Walid Gaaloul","doi":"10.1109/TSC.2024.3463427","DOIUrl":"10.1109/TSC.2024.3463427","url":null,"abstract":"Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners, and thus, must be propagated to partners affected by the change. In this paper, we propose an approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private process. A change impacting other partners is propagated to their processes in a decentralized manner using a SC. The change propagation mechanism ensures the compatibility checks between public processes of the partners and the consistency between the private and public processes of one partner. We demonstrate the approach’s feasibility through an implemented prototype and its effectiveness via a set of evaluation tests.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4383-4396"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245631","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463429
Yaqiong Peng;Weiguo Gao;Haocheng Peng
Deep Neural Networks (DNNs) are commonly deployed as online inference services. To meet interactive latency requirements of requests, DNN services require the use of Graphics Processing Unit (GPU) to improve their responsiveness. The unique characteristics of inference workloads pose new challenges to manage GPU resources. First, the GPU scheduler needs to carefully manage requests to meet their latency targets. Second, a single inference task often underutilizes GPU resources. Third, the fluctuating patterns of inference workloads pose difficulties in determining the resources allocated to each DNN model. Therefore, it is critical for the GPU scheduler to maximize GPU utilization by collocating multiple DNN models without violating the latency Service-Level Objectives (SLOs) of requests. However, we find that existing works are not adequate for achieving this goal among latency-sensitive inference tasks. Hence, we propose FineST, a scheduling framework for serving DNNs with fine-grained spatio-temporal sharing of GPU inference servers. To maximize GPU utilization, FineST allocates intra-GPU computing resources from both spatial and temporal dimensions across DNNs in a cost-effective way, while predicting interference overheads under diverse consolidated executions for controlling SLO violation rates. Compared to a state-of-the-art work, FineST improves the peak throughput of serving heterogeneous DNNsby up to 64.7% under SLO constraints.
{"title":"Serving DNN Inference With Fine-Grained Spatio-Temporal Sharing of GPU Servers","authors":"Yaqiong Peng;Weiguo Gao;Haocheng Peng","doi":"10.1109/TSC.2024.3463429","DOIUrl":"10.1109/TSC.2024.3463429","url":null,"abstract":"<italic>Deep Neural Networks</i>\u0000 (DNNs) are commonly deployed as online inference services. To meet interactive latency requirements of requests, DNN services require the use of \u0000<italic>Graphics Processing Unit</i>\u0000 (GPU) to improve their responsiveness. The unique characteristics of inference workloads pose new challenges to manage GPU resources. First, the GPU scheduler needs to carefully manage requests to meet their latency targets. Second, a single inference task often underutilizes GPU resources. Third, the fluctuating patterns of inference workloads pose difficulties in determining the resources allocated to each DNN model. Therefore, it is critical for the GPU scheduler to maximize GPU utilization by collocating multiple DNN models without violating the latency \u0000<italic>Service-Level Objectives</i>\u0000 (SLOs) of requests. However, we find that existing works are not adequate for achieving this goal among latency-sensitive inference tasks. Hence, we propose FineST, a scheduling framework for serving DNNs with fine-grained spatio-temporal sharing of GPU inference servers. To maximize GPU utilization, FineST allocates intra-GPU computing resources from both spatial and temporal dimensions across DNNs in a cost-effective way, while predicting interference overheads under diverse consolidated executions for controlling SLO violation rates. Compared to a state-of-the-art work, FineST improves the peak throughput of serving heterogeneous DNNsby up to 64.7% under SLO constraints.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4310-4323"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245623","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463394
Huijuan Zhu;Lei Yang;Liangmin Wang;Victor S. Sheng
Smart contracts have gained extensive adoption across diverse industries, including finance, supply chain, and the Internet of Things. Nevertheless, the surge in security incidents of smart contracts over recent years has led to substantial economic losses. Therefore, ensuring the security of smart contracts has become a critical and complex challenge in both academic and industrial domains. Based on 539 real-world security incidents in the Ethereum platform and audit reports from 10 authoritative auditing institutions, we summarize 27 types of exploited security vulnerabilities and draw insights into their principles, typical cases, relevant research and recommended prevention strategies. Besides, we also gather 7 other potentially threatening vulnerability types as supplements. On this basis, we conduct an in-depth analysis of the root causes of vulnerabilities and further formulate eight safety practical rules. Moreover, we perform a comprehensive review of 178 recent papers on smart contract security analysis, classifying detection methods into formal verification, fuzz testing, machine learning, program analysis, and others. For each category, we seize the specific detection tools and analyze them comprehensively. Then, we conduct an extensive analysis and synthesis from various angles, presenting a comprehensive overview of the current research landscape in smart contract security detection. We also discuss current on-chain and off-chain repair methods. Finally, this review outlines major challenges and highlights potential areas for future research in this field.
{"title":"A Survey on Security Analysis Methods of Smart Contracts","authors":"Huijuan Zhu;Lei Yang;Liangmin Wang;Victor S. Sheng","doi":"10.1109/TSC.2024.3463394","DOIUrl":"10.1109/TSC.2024.3463394","url":null,"abstract":"Smart contracts have gained extensive adoption across diverse industries, including finance, supply chain, and the Internet of Things. Nevertheless, the surge in security incidents of smart contracts over recent years has led to substantial economic losses. Therefore, ensuring the security of smart contracts has become a critical and complex challenge in both academic and industrial domains. Based on 539 real-world security incidents in the Ethereum platform and audit reports from 10 authoritative auditing institutions, we summarize 27 types of exploited security vulnerabilities and draw insights into their principles, typical cases, relevant research and recommended prevention strategies. Besides, we also gather 7 other potentially threatening vulnerability types as supplements. On this basis, we conduct an in-depth analysis of the root causes of vulnerabilities and further formulate eight safety practical rules. Moreover, we perform a comprehensive review of 178 recent papers on smart contract security analysis, classifying detection methods into formal verification, fuzz testing, machine learning, program analysis, and others. For each category, we seize the specific detection tools and analyze them comprehensively. Then, we conduct an extensive analysis and synthesis from various angles, presenting a comprehensive overview of the current research landscape in smart contract security detection. We also discuss current on-chain and off-chain repair methods. Finally, this review outlines major challenges and highlights potential areas for future research in this field.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4522-4539"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245388","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463431
Xinzhe Zhang;Lei Wu;Zhien Liu;Hao Wang;Lijuan Xu;Songnian Zhang;Rongxing Lu
While online medical diagnosis provides significant convenience to users, it also incurs the risk of privacy breaches, which inspired the emergence of various privacy-preserving online medical schemes. Nonetheless, existing schemes either compromise partial privacy to third parties or rely on cryptographic methods with high computational complexity. In particular, they do not anticipate user’s disputes to the extent that there is no audit process to guarantee the correctness of the diagnosis results and the fairness of the schemes. Consequently, we propose an efficient and privacy-preserving online medical diagnosis scheme based on additive secret sharing (ASS). First, the anonymity of the user is provided in the medical diagnosis process, which ensures that the cloud cannot link the diagnosis results to the user. Then, we devise a minimum value protocol and a range comparison protocol to enhance the security of the online diagnosis. In addition, considering user’s disputes that arise in realistic scenarios (e.g., malicious users may cheat the diagnosis system for personal benefits), we construct a blockchain-based audit process to detect user’s behaviors and settle controversies. Finally, we demonstrate the security and efficiency of the proposed scheme with theoretical analysis and experimental evaluation.
{"title":"Towards Auditable and Privacy-Preserving Online Medical Diagnosis Service Over Cloud","authors":"Xinzhe Zhang;Lei Wu;Zhien Liu;Hao Wang;Lijuan Xu;Songnian Zhang;Rongxing Lu","doi":"10.1109/TSC.2024.3463431","DOIUrl":"10.1109/TSC.2024.3463431","url":null,"abstract":"While online medical diagnosis provides significant convenience to users, it also incurs the risk of privacy breaches, which inspired the emergence of various privacy-preserving online medical schemes. Nonetheless, existing schemes either compromise partial privacy to third parties or rely on cryptographic methods with high computational complexity. In particular, they do not anticipate user’s disputes to the extent that there is no audit process to guarantee the correctness of the diagnosis results and the fairness of the schemes. Consequently, we propose an efficient and privacy-preserving online medical diagnosis scheme based on additive secret sharing (ASS). First, the anonymity of the user is provided in the medical diagnosis process, which ensures that the cloud cannot link the diagnosis results to the user. Then, we devise a minimum value protocol and a range comparison protocol to enhance the security of the online diagnosis. In addition, considering user’s disputes that arise in realistic scenarios (e.g., malicious users may cheat the diagnosis system for personal benefits), we construct a blockchain-based audit process to detect user’s behaviors and settle controversies. Finally, we demonstrate the security and efficiency of the proposed scheme with theoretical analysis and experimental evaluation.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4397-4410"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245622","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}
Opportunistic network-based mobile crowdsourcing (MCS) outsources location-based human tasks to a crowd of workers, where workers with mobile devices opportunistically have contact with the server. While a number of task assignment algorithms have been proposed for different objectives, real-timeness is not considered. In this article, we are interested in real-time MCS (RT-MCS), in which tasks can be generated at any time step, and task assignment is performed in real-time. We first model an abstract RT-MCS and then instantiate the real-time task assignment problem for opportunistic network-based RT-MCS. A generic real-time task assignment (RTA) algorithm is designed based on the principle of the greedy approach, where each task is assigned to the best worker with the highest expected completion probability. To understand the fundamental performance issues, we formulate closed-form solutions for task completion probability as well as delay. In addition, we identify the critical condition that illuminates the busy state and the not-busy state of an RT-MCS. Furthermore, the analytical and simulation results demonstrate that our analysis yields close approximation of simulation results.
{"title":"Modeling Real-Time Task Assignment for Mobile Crowdsourcing in Opportunistic Networks","authors":"Haruumi Imamura;Kazuya Sakai;Min-Te Sun;Wei-Shinn Ku;Jie Wu","doi":"10.1109/TSC.2024.3463419","DOIUrl":"10.1109/TSC.2024.3463419","url":null,"abstract":"Opportunistic network-based mobile crowdsourcing (MCS) outsources location-based human tasks to a crowd of workers, where workers with mobile devices opportunistically have contact with the server. While a number of task assignment algorithms have been proposed for different objectives, real-timeness is not considered. In this article, we are interested in real-time MCS (RT-MCS), in which tasks can be generated at any time step, and task assignment is performed in real-time. We first model an abstract RT-MCS and then instantiate the real-time task assignment problem for opportunistic network-based RT-MCS. A generic real-time task assignment (RTA) algorithm is designed based on the principle of the greedy approach, where each task is assigned to the best worker with the highest expected completion probability. To understand the fundamental performance issues, we formulate closed-form solutions for task completion probability as well as delay. In addition, we identify the critical condition that illuminates the busy state and the not-busy state of an RT-MCS. Furthermore, the analytical and simulation results demonstrate that our analysis yields close approximation of simulation results.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3942-3955"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245386","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463198
Xinchang Zhang;Maoli Wang;Yuanjie Zheng;Dongjie Liu
Delays are crucial factors in the service management of networks, especially software-defined networks. Unfortunately, it is very difficult to accurately model a traffic-delay mapping without any assumptions on an uncertain network. In this article, we present a machine learning-based solution to generate a mapping between link traffic and link delay in software-defined networks. The proposed solution only requires a small number of link delay samples from the production network. The small number of link delay samples is not sufficient for learning link traffic-delay mapping. To solve the above problem, we extend the link delay-related data via a sample transfer method and a distributed path delay data collection method without the assistance of the controller. We design a link traffic-delay mapping learning solution using the above three classes of data. This solution uses a traffic segment-based statistical mechanism to deduce the mean link delay effectively from the collected path delay information and implements effective sample transfer via a distance-based approximation. On the basis of specially designed deep learning structures and training procedures, the proposed learning solution effectively builds traffic-delay mapping models using the samples transferred from an experimental network and the samples of the production network.
{"title":"Link Traffic-Delay Mapping Model Learning Based on Multi-Class Samples in Software-Defined Networks","authors":"Xinchang Zhang;Maoli Wang;Yuanjie Zheng;Dongjie Liu","doi":"10.1109/TSC.2024.3463198","DOIUrl":"10.1109/TSC.2024.3463198","url":null,"abstract":"Delays are crucial factors in the service management of networks, especially software-defined networks. Unfortunately, it is very difficult to accurately model a traffic-delay mapping without any assumptions on an uncertain network. In this article, we present a machine learning-based solution to generate a mapping between link traffic and link delay in software-defined networks. The proposed solution only requires a small number of link delay samples from the production network. The small number of link delay samples is not sufficient for learning link traffic-delay mapping. To solve the above problem, we extend the link delay-related data via a sample transfer method and a distributed path delay data collection method without the assistance of the controller. We design a link traffic-delay mapping learning solution using the above three classes of data. This solution uses a traffic segment-based statistical mechanism to deduce the mean link delay effectively from the collected path delay information and implements effective sample transfer via a distance-based approximation. On the basis of specially designed deep learning structures and training procedures, the proposed learning solution effectively builds traffic-delay mapping models using the samples transferred from an experimental network and the samples of the production network.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3869-3886"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245629","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463475
Di Wu;Zunliang Wang;Huijiang Pan;Haipeng Yao;Tianle Mai;Song Guo
In recent years, the rapid growth of Internet of Things (IoT) devices and applications has posed significant challenges for existing Mobile Edge Computing (MEC) architectures. The inherent latency uncertainties in MEC architectures make it difficult to support latency-sensitive applications such as autonomous vehicles. Additionally, the increasing number of connected devices has led to substantial challenges in terms of limited throughput for MEC servers. With the recent advancements in programmable network hardware, such as SmartNICs and programmable switches, the Network-based Computing (NBC) paradigm has gained widespread attention. Leveraging line-rate processing capabilities, NBC offers a promising solution for high throughput and low latency processing. This paper aims to explore the potential benefits and challenges of incorporating NBC into existing MEC architectures. The feasibility of our proposed architecture is evaluated using two use cases, Linear Quadratic Regulator (LQR) control and Complex Event Processing (CEP), demonstrating significant improvements in latency performance.
{"title":"In-Network Computing Empowered Mobile Edge Offloading Architecture for Internet of Things","authors":"Di Wu;Zunliang Wang;Huijiang Pan;Haipeng Yao;Tianle Mai;Song Guo","doi":"10.1109/TSC.2024.3463475","DOIUrl":"10.1109/TSC.2024.3463475","url":null,"abstract":"In recent years, the rapid growth of Internet of Things (IoT) devices and applications has posed significant challenges for existing Mobile Edge Computing (MEC) architectures. The inherent latency uncertainties in MEC architectures make it difficult to support latency-sensitive applications such as autonomous vehicles. Additionally, the increasing number of connected devices has led to substantial challenges in terms of limited throughput for MEC servers. With the recent advancements in programmable network hardware, such as SmartNICs and programmable switches, the Network-based Computing (NBC) paradigm has gained widespread attention. Leveraging line-rate processing capabilities, NBC offers a promising solution for high throughput and low latency processing. This paper aims to explore the potential benefits and challenges of incorporating NBC into existing MEC architectures. The feasibility of our proposed architecture is evaluated using two use cases, Linear Quadratic Regulator (LQR) control and Complex Event Processing (CEP), demonstrating significant improvements in latency performance.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3817-3829"},"PeriodicalIF":5.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245627","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}
Pub Date : 2024-09-18DOI: 10.1109/TSC.2024.3463425
Yuanyuan Zhou;Zhijun Ding;Changjun Jiang
Each activity in the service workflow interacts with services as required to meet complex business needs and quickly adapt to market changes. The design of each activity's input/output interface parameters influences whether it can successfully map to appropriate and interactive services. In practice, suitable activity interface parameters should possess 3-features: $realism$ , $relevance$ , and $compatibility$ , as popular parameters originating from the real world and closely related to activity semantics are apt to match user-expected services. However, existing research requires expert specification or ontology-based inference, resulting in outdated, inconsistent parameters that lack necessary elements, making it challenging to match expected services. Therefore, we propose an automated method combining Transformer and weighted HITS to recommend interface parameters with 3-features on activity function requirement. It filters similar Endpoints (EPs) based on the activity's semantics by supervised Transformer-based learning of multi-domain APIs and unsupervised EPs matching. Next, a node-weighted heterogeneous graph is built based on similar EPs and their interface parameter relationships. We then apply a node-weighted HITS to explore mutual gain relationships within the graph and calculate parameter compatibilities. Finally, a top- $k$ non-redundant compatible parameter list and corresponding different formats are recommended for the activity. The method's effectiveness and efficiency are verified using a real API service dataset from RapidAPI.
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