Pub Date : 2024-07-29DOI: 10.1109/TNSM.2024.3435484
Neeraj Sharma;Kalpesh Kapoor
The use of blockchain-based cryptocurrencies has significantly increased over the last ten years; nevertheless, the broader acceptance of these currencies is hindered by scaling challenges. Payment Channel Networks (PCN), which operates as a layer two solution, presents itself as a viable option for augmenting the scalability of a blockchain network. In order to reduce the time and cost associated with the on-chain settlement, users have the option to conduct off-chain transactions through payment channels within their network. The growth of the PCN is expected to be accompanied by a corresponding increase in the number of transactions. However, the current distributed routing algorithms are unable to manage several simultaneous transactions due to deadlocks efficiently. We illustrate the possibility of deadlock in distributed routing algorithms. We prove that routing two transactions in PCN is NP-complete by reducing it from a two-commodity flow problem. In contrast to earlier work that avoided deadlock by exploiting locking or priority queues, our work emphasizes routing algorithms to avoid conditions for deadlock. We enhance the routing choices to minimize the number of saturated links that can cause deadlock. Resource allocation graphs are used to illustrate the necessary and sufficient conditions required for transactions to be in a deadlock. We also show how the dynamic behavior of resources can affect the deadlock situation in future timestamps. The deadlock trilemma and the relation between concurrency, resources, and deadlocks have also been discussed. The experimental evaluation shows that the proposed methodology yields an improvement in transaction count in the Speedy and the Webflow algorithms by 41% and 27%, respectively.
{"title":"Deadlock Prevention in Payment Channel Networks","authors":"Neeraj Sharma;Kalpesh Kapoor","doi":"10.1109/TNSM.2024.3435484","DOIUrl":"10.1109/TNSM.2024.3435484","url":null,"abstract":"The use of blockchain-based cryptocurrencies has significantly increased over the last ten years; nevertheless, the broader acceptance of these currencies is hindered by scaling challenges. Payment Channel Networks (PCN), which operates as a layer two solution, presents itself as a viable option for augmenting the scalability of a blockchain network. In order to reduce the time and cost associated with the on-chain settlement, users have the option to conduct off-chain transactions through payment channels within their network. The growth of the PCN is expected to be accompanied by a corresponding increase in the number of transactions. However, the current distributed routing algorithms are unable to manage several simultaneous transactions due to deadlocks efficiently. We illustrate the possibility of deadlock in distributed routing algorithms. We prove that routing two transactions in PCN is NP-complete by reducing it from a two-commodity flow problem. In contrast to earlier work that avoided deadlock by exploiting locking or priority queues, our work emphasizes routing algorithms to avoid conditions for deadlock. We enhance the routing choices to minimize the number of saturated links that can cause deadlock. Resource allocation graphs are used to illustrate the necessary and sufficient conditions required for transactions to be in a deadlock. We also show how the dynamic behavior of resources can affect the deadlock situation in future timestamps. The deadlock trilemma and the relation between concurrency, resources, and deadlocks have also been discussed. The experimental evaluation shows that the proposed methodology yields an improvement in transaction count in the Speedy and the Webflow algorithms by 41% and 27%, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5164-5177"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870556","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 : 2024-07-29DOI: 10.1109/TNSM.2024.3434955
Dahina Koulougli;Kim Khoa Nguyen;Mohamed Cheriet
Restoring traffic in multi-layer multi-domain networks (MLMD) can be inefficient and expensive due to the reconfiguration of both intra-domain and inter-domain paths under limited resources and information sharing. This often results in traffic loss and resource over-provisioning within the MLMD, leading to sub-optimal restoration throughput and high costs. In this study, we harness FlexEthernet (FlexE) on inter-domain links to maximize the restoration throughput at minimum cost. FlexE link aggregation is an effective technique to deal with the costly impact of alternative domain rerouting that allows diverting traffic over aggregated links parallel to the failed ones, without disrupting the intra-domain connections. Additionally, FlexE helps increase network reutilization by leveraging time division multiplexing (TDM) to flexibly shift affected traffic to underutilized aggregated links. However, scheduling traffic migration in FlexE is a challenging issue that has not been fully investigated in the literature. In this paper, we initially formulate the FlexE-based traffic restoration problem as a mixed integer non-linear program (MINLP) and then introduce an approximation algorithm to efficiently solve this problem in polynomial time. Furthermore, we propose a supervised learning approach to predict the optimal restoration policy for large-size instances. Experimental results show that our solution restores up to 14% more traffic than a state-of-the-art approach.
{"title":"Optimized FlexEthernet for Inter-Domain Traffic Restoration","authors":"Dahina Koulougli;Kim Khoa Nguyen;Mohamed Cheriet","doi":"10.1109/TNSM.2024.3434955","DOIUrl":"10.1109/TNSM.2024.3434955","url":null,"abstract":"Restoring traffic in multi-layer multi-domain networks (MLMD) can be inefficient and expensive due to the reconfiguration of both intra-domain and inter-domain paths under limited resources and information sharing. This often results in traffic loss and resource over-provisioning within the MLMD, leading to sub-optimal restoration throughput and high costs. In this study, we harness FlexEthernet (FlexE) on inter-domain links to maximize the restoration throughput at minimum cost. FlexE link aggregation is an effective technique to deal with the costly impact of alternative domain rerouting that allows diverting traffic over aggregated links parallel to the failed ones, without disrupting the intra-domain connections. Additionally, FlexE helps increase network reutilization by leveraging time division multiplexing (TDM) to flexibly shift affected traffic to underutilized aggregated links. However, scheduling traffic migration in FlexE is a challenging issue that has not been fully investigated in the literature. In this paper, we initially formulate the FlexE-based traffic restoration problem as a mixed integer non-linear program (MINLP) and then introduce an approximation algorithm to efficiently solve this problem in polynomial time. Furthermore, we propose a supervised learning approach to predict the optimal restoration policy for large-size instances. Experimental results show that our solution restores up to 14% more traffic than a state-of-the-art approach.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5001-5017"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870558","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 : 2024-07-29DOI: 10.1109/TNSM.2024.3435769
Jakob Miserez;Didier Colle;Mario Pickavet;Wouter Tavernier
Next-generation Internet will require strict end-to-end delay guarantees to support upcoming latency-sensitive applications. The IEEE 802.1 Time-Sensitive Networking (TSN) standard has become the de-facto solution for Ethernet-based L2 networks to support applications with strict latency, jitter and packet loss requirements. The IETF DetNet Working Group tries to expand on TSN to support real-time applications over larger-scale L3 networks. This paper proposes control and routing strategies that provide latency guarantees in L3 networks without requiring time synchronization among nodes. The proposed strategies include a link-state routing protocol and several exploration-based protocols that exploit queue-level information and network calculus to provide latency guarantees. Additionally, the use of queueing delay budgets enables independence among flows, while enabling fine-grained routing. This allows to make better routing decisions and to support applications with diverse latency requirements. Moreover, traffic shaping is only required at the network ingress. The strategies are evaluated extensively and compared in a simulation environment in multiple large-scale scenarios, considering acceptance rate, network utilization, path dissemination time, control overhead, and memory consumption, as well as how these metrics evolve w.r.t. different network scales. Experimental results demonstrate that representative delay-constrained traffic demands can be accommodated adequately by queue-level link-state routing protocols only in smaller-scale networks. In larger-scale network scenarios, breadth-first exploration-based protocols are required to provide stable performance w.r.t. acceptance rate and path dissemination times at the cost of only linearly increasing control overhead and memory footprint.
{"title":"Exploiting Queue Information for Scalable Delay-Constrained Routing in Deterministic Networks","authors":"Jakob Miserez;Didier Colle;Mario Pickavet;Wouter Tavernier","doi":"10.1109/TNSM.2024.3435769","DOIUrl":"10.1109/TNSM.2024.3435769","url":null,"abstract":"Next-generation Internet will require strict end-to-end delay guarantees to support upcoming latency-sensitive applications. The IEEE 802.1 Time-Sensitive Networking (TSN) standard has become the de-facto solution for Ethernet-based L2 networks to support applications with strict latency, jitter and packet loss requirements. The IETF DetNet Working Group tries to expand on TSN to support real-time applications over larger-scale L3 networks. This paper proposes control and routing strategies that provide latency guarantees in L3 networks without requiring time synchronization among nodes. The proposed strategies include a link-state routing protocol and several exploration-based protocols that exploit queue-level information and network calculus to provide latency guarantees. Additionally, the use of queueing delay budgets enables independence among flows, while enabling fine-grained routing. This allows to make better routing decisions and to support applications with diverse latency requirements. Moreover, traffic shaping is only required at the network ingress. The strategies are evaluated extensively and compared in a simulation environment in multiple large-scale scenarios, considering acceptance rate, network utilization, path dissemination time, control overhead, and memory consumption, as well as how these metrics evolve w.r.t. different network scales. Experimental results demonstrate that representative delay-constrained traffic demands can be accommodated adequately by queue-level link-state routing protocols only in smaller-scale networks. In larger-scale network scenarios, breadth-first exploration-based protocols are required to provide stable performance w.r.t. acceptance rate and path dissemination times at the cost of only linearly increasing control overhead and memory footprint.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5260-5272"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873177","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 : 2024-07-26DOI: 10.1109/TNSM.2024.3434457
Nidhi Sharma;Krishan Kumar
Driven by the vision of 6G, the need for diverse computation-intensive and delay-sensitive tasks continues to rise. The integration of mobile edge computing with the ultra-dense network is not only capable of handling traffic from a large number of smart devices but also delivers substantial processing capabilities to the users. This combined network is expected as an effective solution for meeting the latency-critical requirement and will enhance the quality of user experience. Nevertheless, when a massive number of devices offload tasks to edge servers, the problem of channel interference, network load and energy shortage of user devices (UDs) would increase. Therefore, we investigate the joint uplink and downlink resource allocation and task offloading optimization problem in terms of minimizing the overall task delay while sustaining the UD battery life. Thus, to achieve long-term gains while making quick decisions, we propose an improved double deep Q-network scheme named Prioritized double deep Q-network. In this, the prioritized experience replay has been improved by considering the experience freshness factor along with temporal difference error to achieve fast and efficient learning. Extensive numerical results prove the efficacy of the proposed scheme by analyzing delay and energy consumption. Especially, our scheme can considerably decrease the delay by 11.86%, 26.22%, 48.56%, and 61.04% compared to the OELO scheme, DQN scheme, LOS, and EOS, respectively, when the number of UDs varied from 30 to 180.
{"title":"A Novel Latency-Aware Resource Allocation and Offloading Strategy With Improved Prioritization and DDQN for Edge-Enabled UDNs","authors":"Nidhi Sharma;Krishan Kumar","doi":"10.1109/TNSM.2024.3434457","DOIUrl":"10.1109/TNSM.2024.3434457","url":null,"abstract":"Driven by the vision of 6G, the need for diverse computation-intensive and delay-sensitive tasks continues to rise. The integration of mobile edge computing with the ultra-dense network is not only capable of handling traffic from a large number of smart devices but also delivers substantial processing capabilities to the users. This combined network is expected as an effective solution for meeting the latency-critical requirement and will enhance the quality of user experience. Nevertheless, when a massive number of devices offload tasks to edge servers, the problem of channel interference, network load and energy shortage of user devices (UDs) would increase. Therefore, we investigate the joint uplink and downlink resource allocation and task offloading optimization problem in terms of minimizing the overall task delay while sustaining the UD battery life. Thus, to achieve long-term gains while making quick decisions, we propose an improved double deep Q-network scheme named Prioritized double deep Q-network. In this, the prioritized experience replay has been improved by considering the experience freshness factor along with temporal difference error to achieve fast and efficient learning. Extensive numerical results prove the efficacy of the proposed scheme by analyzing delay and energy consumption. Especially, our scheme can considerably decrease the delay by 11.86%, 26.22%, 48.56%, and 61.04% compared to the OELO scheme, DQN scheme, LOS, and EOS, respectively, when the number of UDs varied from 30 to 180.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6260-6272"},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779624","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 : 2024-07-26DOI: 10.1109/TNSM.2024.3434328
Forough Shahab Samani;Rolf Stadler
We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to reallocate resources. We develop and evaluate the framework using a laboratory testbed where we run information and computing services on a service mesh, supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, cost-related objectives, and service differentiation. Our framework supports the design of a control agent for a given management objective. The management objective is defined first and then mapped onto available control actions. Several types of control actions can be executed simultaneously, which allows for efficient resource utilization. Second, the framework separates the learning of the system model and the operating region from the learning of the control policy. By first learning the system model and the operating region from testbed traces, we can instantiate a simulator and train the agent for different management objectives. Third, the use of a simulator shortens the training time by orders of magnitude compared with training the agent on the testbed. We evaluate the learned policies on the testbed and show the effectiveness of our approach in several scenarios. In one scenario, we design a controller that achieves the management objectives with 50% less system resources than Kubernetes HPA autoscaling.
{"title":"A Framework for Dynamically Meeting Performance Objectives on a Service Mesh","authors":"Forough Shahab Samani;Rolf Stadler","doi":"10.1109/TNSM.2024.3434328","DOIUrl":"10.1109/TNSM.2024.3434328","url":null,"abstract":"We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to reallocate resources. We develop and evaluate the framework using a laboratory testbed where we run information and computing services on a service mesh, supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, cost-related objectives, and service differentiation. Our framework supports the design of a control agent for a given management objective. The management objective is defined first and then mapped onto available control actions. Several types of control actions can be executed simultaneously, which allows for efficient resource utilization. Second, the framework separates the learning of the system model and the operating region from the learning of the control policy. By first learning the system model and the operating region from testbed traces, we can instantiate a simulator and train the agent for different management objectives. Third, the use of a simulator shortens the training time by orders of magnitude compared with training the agent on the testbed. We evaluate the learned policies on the testbed and show the effectiveness of our approach in several scenarios. In one scenario, we design a controller that achieves the management objectives with 50% less system resources than Kubernetes HPA autoscaling.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"5992-6007"},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10612769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779625","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 : 2024-07-26DOI: 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}
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