Pub Date : 2024-07-26DOI: 10.1109/TNSM.2024.3434337
Fabian Ihle;Steffen Lindner;Michael Menth
Time-Sensitive Networking (TSN) extends Ethernet to enable real-time communication. In TSN, bounded latency and zero congestion-based packet loss are achieved through mechanisms such as the Credit-Based Shaper (CBS) for bandwidth shaping and the Time-Aware Shaper (TAS) for traffic scheduling. Generally, TSN requires streams to be explicitly admitted before being transmitted. To ensure that admitted traffic conforms with the traffic descriptors indicated for admission control, Per-Stream Filtering and Policing (PSFP) has been defined. For credit-based metering, well-known token bucket policers are applied. However, time-based metering requires time-dependent switch behavior and time synchronization with sub-microsecond precision. While TSN-capable switches support various TSN traffic shaping mechanisms, a full implementation of PSFP is still not available. To bridge this gap, we present a P4-based implementation of PSFP on a 100 Gb/s per port hardware switch. We explain the most interesting aspects of the PSFP implementation whose code is available on GitHub. We demonstrate credit-based and time-based policing and synchronization capabilities to validate the functionality and effectiveness of P4-PSFP. The implementation scales up to 35840 streams depending on the stream identification method. P4-PSFP can be used in practice as long as appropriate TSN switches lack this function. Moreover, its implementation may be helpful for other P4-based hardware implementations that require time synchronization.
{"title":"P4-PSFP: P4-Based Per-Stream Filtering and Policing for Time-Sensitive Networking","authors":"Fabian Ihle;Steffen Lindner;Michael Menth","doi":"10.1109/TNSM.2024.3434337","DOIUrl":"10.1109/TNSM.2024.3434337","url":null,"abstract":"Time-Sensitive Networking (TSN) extends Ethernet to enable real-time communication. In TSN, bounded latency and zero congestion-based packet loss are achieved through mechanisms such as the Credit-Based Shaper (CBS) for bandwidth shaping and the Time-Aware Shaper (TAS) for traffic scheduling. Generally, TSN requires streams to be explicitly admitted before being transmitted. To ensure that admitted traffic conforms with the traffic descriptors indicated for admission control, Per-Stream Filtering and Policing (PSFP) has been defined. For credit-based metering, well-known token bucket policers are applied. However, time-based metering requires time-dependent switch behavior and time synchronization with sub-microsecond precision. While TSN-capable switches support various TSN traffic shaping mechanisms, a full implementation of PSFP is still not available. To bridge this gap, we present a P4-based implementation of PSFP on a 100 Gb/s per port hardware switch. We explain the most interesting aspects of the PSFP implementation whose code is available on GitHub. We demonstrate credit-based and time-based policing and synchronization capabilities to validate the functionality and effectiveness of P4-PSFP. The implementation scales up to 35840 streams depending on the stream identification method. P4-PSFP can be used in practice as long as appropriate TSN switches lack this function. Moreover, its implementation may be helpful for other P4-based hardware implementations that require time synchronization.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5273-5290"},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779626","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 : 2024-07-25DOI: 10.1109/TNSM.2024.3432148
Huanlin Liu;Yang Hu;Yong Chen;Haonan Chen;Bingchuan Huang;Huiling Zhou;Shiqi Yi
Heterogeneous networks based on multicolor visible light communication (VLC) and wireless fidelity (WiFi) have been considered as a key technology to achieve the capacity target in the future 6G mobile communication. However, the inter-cell interference (ICI) pattern in the multi-cell VLC scenario degrades the performance of the heterogeneous networks. To solve this problem, a multi-cell resource allocation mechanism based on interference control (MCRAMIC) is proposed, which includes a preparatory phase and an execution phase. Firstly, according to the different locations and data rate requirements of varied user equipments (UEs), the VLC access point (AP) selection algorithm based on interference avoidance and the UE priority assessment algorithm based on the requirement of UE are proposed.Then, according to the influence factor and priority factor, the multi-cell resource allocation algorithm based on interference control is proposed. According to the algorithm, the candidate VLC AP sets of the UEs are determined firstly, and the VLC APs and lightwave bands are selected for the UEs in turn. Meanwhile, some UEs are connected to the WiFi AP. Finally, the lightwave bands are further allocated to the UEs. Numeric results show that the proposed MCRAMIC outperforms the centralized resource allocation algorithm based on link conflict graph (LCG) and the resource allocation algorithm based on hypergraph theory in terms of the system throughput, UE satisfaction and service fairness.
{"title":"Multi-Cell Resource Allocation Mechanism Based on Interference Control in Indoor Multicolor VLC-WiFi Heterogeneous Networks","authors":"Huanlin Liu;Yang Hu;Yong Chen;Haonan Chen;Bingchuan Huang;Huiling Zhou;Shiqi Yi","doi":"10.1109/TNSM.2024.3432148","DOIUrl":"10.1109/TNSM.2024.3432148","url":null,"abstract":"Heterogeneous networks based on multicolor visible light communication (VLC) and wireless fidelity (WiFi) have been considered as a key technology to achieve the capacity target in the future 6G mobile communication. However, the inter-cell interference (ICI) pattern in the multi-cell VLC scenario degrades the performance of the heterogeneous networks. To solve this problem, a multi-cell resource allocation mechanism based on interference control (MCRAMIC) is proposed, which includes a preparatory phase and an execution phase. Firstly, according to the different locations and data rate requirements of varied user equipments (UEs), the VLC access point (AP) selection algorithm based on interference avoidance and the UE priority assessment algorithm based on the requirement of UE are proposed.Then, according to the influence factor and priority factor, the multi-cell resource allocation algorithm based on interference control is proposed. According to the algorithm, the candidate VLC AP sets of the UEs are determined firstly, and the VLC APs and lightwave bands are selected for the UEs in turn. Meanwhile, some UEs are connected to the WiFi AP. Finally, the lightwave bands are further allocated to the UEs. Numeric results show that the proposed MCRAMIC outperforms the centralized resource allocation algorithm based on link conflict graph (LCG) and the resource allocation algorithm based on hypergraph theory in terms of the system throughput, UE satisfaction and service fairness.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5707-5717"},"PeriodicalIF":4.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779627","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}
With the vigorous development of the blockchain industry, cross-chain transactions can effectively solve the problem of “islands of value” caused by the inability to interact between different chains. However, security risks in reputation management caused by cross-chain transactions implemented through notary solutions have always existed. Consequently, this paper proposes a blockchain cross-chain transaction method based on decentralized dynamic reputation value assessment. The notary election phase addresses the issue of the continually changing behavior of notaries in actual transactions by designing a dynamic evaluation window mechanism based on an RNN. Moreover, a reputation-rating decay mechanism is introduced to avoid the problem of reputation value recovery caused by malicious notaries being inactive for a long time. Relative to alternative reputation assessment models, the proposed method offers a thorough evaluation of user behavior and effectively identifies malicious activities in real-time. Finally, the method was tested by deploying it on the Ethereum blockchain. Our approach offers more dynamic settings for window parameters, adapting to changes in notary behavior and reducing the number of detections within the same timeframe by approximately 59.14%. The weight factor settings are also optimized, allowing for adjustments based on specific situations to achieve accurate reputation values. Overall, this method not only enhances the security of cross-chain transactions but also reduces operational costs by 53.3% compared to traditional technologies.
{"title":"A Blockchain Cross-Chain Transaction Method Based on Decentralized Dynamic Reputation Value Assessment","authors":"Xiaoxuan Hu;Yaochen Ling;Jialin Hua;Zhenjiang Dong;Yanfei Sun;Jin Qi","doi":"10.1109/TNSM.2024.3433414","DOIUrl":"10.1109/TNSM.2024.3433414","url":null,"abstract":"With the vigorous development of the blockchain industry, cross-chain transactions can effectively solve the problem of “islands of value” caused by the inability to interact between different chains. However, security risks in reputation management caused by cross-chain transactions implemented through notary solutions have always existed. Consequently, this paper proposes a blockchain cross-chain transaction method based on decentralized dynamic reputation value assessment. The notary election phase addresses the issue of the continually changing behavior of notaries in actual transactions by designing a dynamic evaluation window mechanism based on an RNN. Moreover, a reputation-rating decay mechanism is introduced to avoid the problem of reputation value recovery caused by malicious notaries being inactive for a long time. Relative to alternative reputation assessment models, the proposed method offers a thorough evaluation of user behavior and effectively identifies malicious activities in real-time. Finally, the method was tested by deploying it on the Ethereum blockchain. Our approach offers more dynamic settings for window parameters, adapting to changes in notary behavior and reducing the number of detections within the same timeframe by approximately 59.14%. The weight factor settings are also optimized, allowing for adjustments based on specific situations to achieve accurate reputation values. Overall, this method not only enhances the security of cross-chain transactions but also reduces operational costs by 53.3% compared to traditional technologies.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5597-5612"},"PeriodicalIF":4.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779628","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 : 2024-07-22DOI: 10.1109/TNSM.2024.3432334
Yixin Li;Liang Liang;Yunjian Jia;Wanli Wen
Block propagation is a critical step in the consensus process, which determines the fork rate and transaction throughput of public blockchain systems. To accelerate block propagation, existing block relay protocols reduce the block size using transaction hashes, which requires the receiver to reconstruct the block based on the transactions in its mempool. Hence, their performance is highly affected by the number of transactions missed by mempools, especially in the P2P network with frequent arrival and departure of nodes. In this paper, we introduce Presync, a transaction synchronization protocol that can reduce the difference of transactions between the block and the mempool with controllable bandwidth overhead. It allows mining pool servers to synchronize the transactions in candidate blocks before the propagation of a valid block. Low-bandwidth mode provides a lightweight synchronization by identifying the unsynchronized transactions, so that the missing transactions can be detected with a low redundancy. High-bandwidth mode conducts a full synchronization of the candidate block using short hashes, and the Merkle root is utilized to match the valid block. We study the performance of Presync through stochastic modeling and experimental evaluations. The results illustrate that low and high-bandwidth modes can respectively reduce the end-to-end delay of compact block by 60% and 78% with bandwidth usages 25KB and 63KB, in a network with 5 active pool servers and 2/3 online probability of full nodes.
{"title":"Presync: An Efficient Transaction Synchronization Protocol to Accelerate Block Propagation","authors":"Yixin Li;Liang Liang;Yunjian Jia;Wanli Wen","doi":"10.1109/TNSM.2024.3432334","DOIUrl":"10.1109/TNSM.2024.3432334","url":null,"abstract":"Block propagation is a critical step in the consensus process, which determines the fork rate and transaction throughput of public blockchain systems. To accelerate block propagation, existing block relay protocols reduce the block size using transaction hashes, which requires the receiver to reconstruct the block based on the transactions in its mempool. Hence, their performance is highly affected by the number of transactions missed by mempools, especially in the P2P network with frequent arrival and departure of nodes. In this paper, we introduce Presync, a transaction synchronization protocol that can reduce the difference of transactions between the block and the mempool with controllable bandwidth overhead. It allows mining pool servers to synchronize the transactions in candidate blocks before the propagation of a valid block. Low-bandwidth mode provides a lightweight synchronization by identifying the unsynchronized transactions, so that the missing transactions can be detected with a low redundancy. High-bandwidth mode conducts a full synchronization of the candidate block using short hashes, and the Merkle root is utilized to match the valid block. We study the performance of Presync through stochastic modeling and experimental evaluations. The results illustrate that low and high-bandwidth modes can respectively reduce the end-to-end delay of compact block by 60% and 78% with bandwidth usages 25KB and 63KB, in a network with 5 active pool servers and 2/3 online probability of full nodes.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5582-5596"},"PeriodicalIF":4.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779630","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}
The rapid adoption of IPv6 has increased network access scale while also escalating the threat of Distributed Denial of Service (DDoS) attacks. By the time a DDoS attack is recognized, the overwhelming volume of attack traffic has already made mitigation extremely difficult. Therefore, continuous network monitoring is essential for early warning and defense preparation against DDoS attacks, requiring both sensitive perception of network changes when DDoS occurs and reducing monitoring overhead to adapt to network resource constraints. In this paper, we propose a novel DDoS incident monitoring mechanism that uses macro-level network traffic behavior as a monitoring anchor to detect subtle malicious behavior indicative of the existence of DDoS traffic in the network. This behavior feature can be abstracted from our designed traffic matrix sample by aggregating continuous IPv6 traffic. Compared to IPv4, the fixed-length header of IPv6 allows more efficient packet parsing in preprocessing. As the decision core of monitoring, we construct a lightweight Binary Convolution DDoS Monitoring (BCDM) model, compressed by binarized convolutional filters and hierarchical pooling strategies, which can detect the malicious behavior abstracted from input traffic matrix if DDoS traffic is involved, thereby signaling an ongoing DDoS attack. Experiment on IPv6 replayed CIC-DDoS2019 shows that BCDM, being lightweight in terms of parameter quantity and computational complexity, achieves monitoring accuracies of 90.9%, 96.4%, and 100% when DDoS incident intensities are as low as 6%, 10%, and 15%, respectively, significantly outperforming comparison methods.
{"title":"BCDM: An Early-Stage DDoS Incident Monitoring Mechanism Based on Binary-CNN in IPv6 Network","authors":"Yufu Wang;Xingwei Wang;Qiang Ni;Wenjuan Yu;Min Huang","doi":"10.1109/TNSM.2024.3431701","DOIUrl":"10.1109/TNSM.2024.3431701","url":null,"abstract":"The rapid adoption of IPv6 has increased network access scale while also escalating the threat of Distributed Denial of Service (DDoS) attacks. By the time a DDoS attack is recognized, the overwhelming volume of attack traffic has already made mitigation extremely difficult. Therefore, continuous network monitoring is essential for early warning and defense preparation against DDoS attacks, requiring both sensitive perception of network changes when DDoS occurs and reducing monitoring overhead to adapt to network resource constraints. In this paper, we propose a novel DDoS incident monitoring mechanism that uses macro-level network traffic behavior as a monitoring anchor to detect subtle malicious behavior indicative of the existence of DDoS traffic in the network. This behavior feature can be abstracted from our designed traffic matrix sample by aggregating continuous IPv6 traffic. Compared to IPv4, the fixed-length header of IPv6 allows more efficient packet parsing in preprocessing. As the decision core of monitoring, we construct a lightweight Binary Convolution DDoS Monitoring (BCDM) model, compressed by binarized convolutional filters and hierarchical pooling strategies, which can detect the malicious behavior abstracted from input traffic matrix if DDoS traffic is involved, thereby signaling an ongoing DDoS attack. Experiment on IPv6 replayed CIC-DDoS2019 shows that BCDM, being lightweight in terms of parameter quantity and computational complexity, achieves monitoring accuracies of 90.9%, 96.4%, and 100% when DDoS incident intensities are as low as 6%, 10%, and 15%, respectively, significantly outperforming comparison methods.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5873-5887"},"PeriodicalIF":4.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779629","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}
In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can flexibly customize the telemetry system to accommodate diverse requirements and network environments. In latency-aware networks, AdapINT effectively reduces telemetry latency, while in overhead-aware networks, it significantly lowers the control overheads.
{"title":"AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning","authors":"Penghui Zhang;Hua Zhang;Yibo Pi;Zijian Cao;Jingyu Wang;Jianxin Liao","doi":"10.1109/TNSM.2024.3427403","DOIUrl":"10.1109/TNSM.2024.3427403","url":null,"abstract":"In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can flexibly customize the telemetry system to accommodate diverse requirements and network environments. In latency-aware networks, AdapINT effectively reduces telemetry latency, while in overhead-aware networks, it significantly lowers the control overheads.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5505-5520"},"PeriodicalIF":4.7,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737318","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 : 2024-07-17DOI: 10.1109/TNSM.2024.3430052
Nicola Di Cicco;Memedhe Ibrahimi;Omran Ayoub;Federica Bruschetta;Michele Milano;Claudio Passera;Francesco Musumeci
We investigate classifying hardware failures in microwave networks via Machine Learning (ML). Although ML-based approaches excel in this task, they usually provide only hard failure predictions without guarantees on their reliability, i.e., on the probability of correct classification. Generally, accumulating data for longer time horizons increases the model’s predictive accuracy. Therefore, in real-world applications, a trade-off arises between two contrasting objectives: i) ensuring high reliability for each classified observation, and ii) collecting the minimal amount of data to provide a reliable prediction. To address this problem, we formulate hardware failure-cause identification as an As-Soon-As-Possible (ASAP) selective classification problem where data streams are sequentially provided to an ML classifier, which outputs a prediction as soon as the probability of correct classification exceeds a user-specified threshold. To this end, we leverage Inductive and Cross Venn-Abers Predictors to transform heuristic probability estimates from any ML model into rigorous predictive probabilities. Numerical results on a real-world dataset show that our ASAP framework reduces the time-to-predict by ~8x compared to the state-of-the-art, while ensuring a selective classification accuracy greater than 95%. The dataset utilized in this study is publicly available, aiming to facilitate future investigations in failure management for microwave networks.
我们研究通过机器学习（ML）对微波网络中的硬件故障进行分类。虽然基于 ML 的方法在这项任务中表现出色，但它们通常只能提供硬故障预测，而不能保证其可靠性，即正确分类的概率。一般来说，在更长的时间跨度内积累数据可以提高模型的预测准确性。因此，在实际应用中，需要在两个截然不同的目标之间进行权衡：i) 确保每个分类观测结果的高可靠性；ii) 收集最少的数据量以提供可靠的预测。为了解决这个问题，我们将硬件故障原因识别表述为一个 "尽可能快"（ASAP）的选择性分类问题，在这个问题中，数据流被依次提供给一个 ML 分类器，一旦正确分类的概率超过用户指定的阈值，分类器就会输出预测结果。为此，我们利用归纳预测器和交叉文氏预测器将任何 ML 模型的启发式概率估计转化为严格的预测概率。在实际数据集上的数值结果表明，我们的 ASAP 框架与最先进的框架相比，预测时间缩短了约 8 倍，同时确保选择性分类准确率超过 95%。本研究中使用的数据集是公开的，旨在促进未来微波网络故障管理方面的研究。
{"title":"ASAP Hardware Failure-Cause Identification in Microwave Networks Using Venn-Abers Predictors","authors":"Nicola Di Cicco;Memedhe Ibrahimi;Omran Ayoub;Federica Bruschetta;Michele Milano;Claudio Passera;Francesco Musumeci","doi":"10.1109/TNSM.2024.3430052","DOIUrl":"10.1109/TNSM.2024.3430052","url":null,"abstract":"We investigate classifying hardware failures in microwave networks via Machine Learning (ML). Although ML-based approaches excel in this task, they usually provide only hard failure predictions without guarantees on their reliability, i.e., on the probability of correct classification. Generally, accumulating data for longer time horizons increases the model’s predictive accuracy. Therefore, in real-world applications, a trade-off arises between two contrasting objectives: i) ensuring high reliability for each classified observation, and ii) collecting the minimal amount of data to provide a reliable prediction. To address this problem, we formulate hardware failure-cause identification as an As-Soon-As-Possible (ASAP) selective classification problem where data streams are sequentially provided to an ML classifier, which outputs a prediction as soon as the probability of correct classification exceeds a user-specified threshold. To this end, we leverage Inductive and Cross Venn-Abers Predictors to transform heuristic probability estimates from any ML model into rigorous predictive probabilities. Numerical results on a real-world dataset show that our ASAP framework reduces the time-to-predict by ~8x compared to the state-of-the-art, while ensuring a selective classification accuracy greater than 95%. The dataset utilized in this study is publicly available, aiming to facilitate future investigations in failure management for microwave networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5400-5409"},"PeriodicalIF":4.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745487","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 : 2024-07-16DOI: 10.1109/TNSM.2024.3429204
Anna Volkova;Abdorasoul Ghasemi;Hermann de Meer
Restoration of modern interdependent Information and Communication Technology (ICT) and power networks relies on preplanned and reactive strategies to consider simultaneous communication and power system recovery. This paper addresses the problem of finding and energizing a proper communication network connecting the distributed power grid assets in the restoration process, assuming a probability of infeasibility of recovering each communication node. The proper network has the minimum size, meets the communication requirements of power system recovery, and guarantees robustness against ICT nodes not being recoverable during restoration. The problem is formulated as a multi-objective optimization problem and solved using the genetic algorithm to find the optimal subgraph that ensures enough node-disjoint paths between the communicating power grid assets. Simulation results for the restoration strategy of the communication network associated with a power network are provided and discussed. The results show that networks’ ability to mitigate the adverse consequences of node failures can be significantly improved by incorporating just a few additional nodes and links while keeping the ICT network compact and feasible for restoration.
{"title":"Towards Forming Optimal Communication Network for Effective Power System Restoration","authors":"Anna Volkova;Abdorasoul Ghasemi;Hermann de Meer","doi":"10.1109/TNSM.2024.3429204","DOIUrl":"10.1109/TNSM.2024.3429204","url":null,"abstract":"Restoration of modern interdependent Information and Communication Technology (ICT) and power networks relies on preplanned and reactive strategies to consider simultaneous communication and power system recovery. This paper addresses the problem of finding and energizing a proper communication network connecting the distributed power grid assets in the restoration process, assuming a probability of infeasibility of recovering each communication node. The proper network has the minimum size, meets the communication requirements of power system recovery, and guarantees robustness against ICT nodes not being recoverable during restoration. The problem is formulated as a multi-objective optimization problem and solved using the genetic algorithm to find the optimal subgraph that ensures enough node-disjoint paths between the communicating power grid assets. Simulation results for the restoration strategy of the communication network associated with a power network are provided and discussed. The results show that networks’ ability to mitigate the adverse consequences of node failures can be significantly improved by incorporating just a few additional nodes and links while keeping the ICT network compact and feasible for restoration.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5250-5259"},"PeriodicalIF":4.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717383","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 : 2024-07-15DOI: 10.1109/TNSM.2024.3428496
Binbin Huang;Lingbin Wang;Xiao Liu;Zixin Huang;Yuyu Yin;Fujin Zhu;Shangguang Wang;Shuiguang Deng
In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.
{"title":"Reinforcement Learning-Based Online Scheduling of Multiple Workflows in Edge Environment","authors":"Binbin Huang;Lingbin Wang;Xiao Liu;Zixin Huang;Yuyu Yin;Fujin Zhu;Shangguang Wang;Shuiguang Deng","doi":"10.1109/TNSM.2024.3428496","DOIUrl":"10.1109/TNSM.2024.3428496","url":null,"abstract":"In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5691-5706"},"PeriodicalIF":4.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717384","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 : 2024-07-12DOI: 10.1109/TNSM.2024.3427139
Jin Tian;Junfeng Tian;Ruizhong Du
Sharding is a popular technology for blockchain systems that addresses scalability while ensuring security and decentralization. However, there are still many issues. Firstly, the existing sharding solutions exhibit a high percentage of cross-shard transactions, which place a substantial burden on system resources and result in a significant degradation of performance. Secondly, none of these solutions adequately accounts for the inherent heterogeneity among nodes, and the interoperability of different nodes is constrained by security concerns, thereby impeding the practical advancement of blockchain applications. In this paper, a novel subjective logical trust-based tree sharding system, MSLTChain, is introduced to alleviate the processing workload of cross-shard transactions. The proposal encompasses a tree sharding structure and a trust management model, enabling the processing and validation of cross-shard transactions within the parent shard. Moreover, an adaptive algorithm is incorporated to dynamically fine-tune scalability, further augmenting system throughput. A subjective logical trust model is employed to portray the heterogeneity between nodes and enhance the system’s security level. The paper also conducts a comprehensive theoretical analysis, evaluating the security, scalability, and performance aspects. Finally, the experimental findings substantiate the capability of MSLTChain to satisfy the dual imperatives of scalability and security within the context of sharding blockchain.
{"title":"MSLTChain: A Trust Model Based on the Multi-Dimensional Subjective Logic for Tree Sharding Blockchain System","authors":"Jin Tian;Junfeng Tian;Ruizhong Du","doi":"10.1109/TNSM.2024.3427139","DOIUrl":"10.1109/TNSM.2024.3427139","url":null,"abstract":"Sharding is a popular technology for blockchain systems that addresses scalability while ensuring security and decentralization. However, there are still many issues. Firstly, the existing sharding solutions exhibit a high percentage of cross-shard transactions, which place a substantial burden on system resources and result in a significant degradation of performance. Secondly, none of these solutions adequately accounts for the inherent heterogeneity among nodes, and the interoperability of different nodes is constrained by security concerns, thereby impeding the practical advancement of blockchain applications. In this paper, a novel subjective logical trust-based tree sharding system, MSLTChain, is introduced to alleviate the processing workload of cross-shard transactions. The proposal encompasses a tree sharding structure and a trust management model, enabling the processing and validation of cross-shard transactions within the parent shard. Moreover, an adaptive algorithm is incorporated to dynamically fine-tune scalability, further augmenting system throughput. A subjective logical trust model is employed to portray the heterogeneity between nodes and enhance the system’s security level. The paper also conducts a comprehensive theoretical analysis, evaluating the security, scalability, and performance aspects. Finally, the experimental findings substantiate the capability of MSLTChain to satisfy the dual imperatives of scalability and security within the context of sharding blockchain.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5566-5581"},"PeriodicalIF":4.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611368","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}
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