Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651961
Ralf Kundel, Nehal Baganal Krishna, Christoph Gärtner, Tobias Meuser, Amr Rizk
Congestion control mechanisms in computer networks rely mainly on a feedback loop having a reaction time equal to the flow RTT. Reducing this feedback time helps the sender to react faster to changing network conditions such as congestion. In this work, we propose reverse-path congestion notification on top of programmable networking switches. Our approach can significantly lower the reaction time, such that the congestion control implementation can adapt much faster to changing network conditions. The proposed approach aims to work with current TCP implementations with no required changes to the communication endpoints. Last, we show how the presented approach could be realized by utilizing off-the-shelf programmable switches.
{"title":"Poster: Reverse-Path Congestion Notification: Accelerating the Congestion Control Feedback Loop","authors":"Ralf Kundel, Nehal Baganal Krishna, Christoph Gärtner, Tobias Meuser, Amr Rizk","doi":"10.1109/ICNP52444.2021.9651961","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651961","url":null,"abstract":"Congestion control mechanisms in computer networks rely mainly on a feedback loop having a reaction time equal to the flow RTT. Reducing this feedback time helps the sender to react faster to changing network conditions such as congestion. In this work, we propose reverse-path congestion notification on top of programmable networking switches. Our approach can significantly lower the reaction time, such that the congestion control implementation can adapt much faster to changing network conditions. The proposed approach aims to work with current TCP implementations with no required changes to the communication endpoints. Last, we show how the presented approach could be realized by utilizing off-the-shelf programmable switches.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122507912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651959
Muhammad Naeem Tahir, M. Katz, Zunera Javed
In recent years, the vehicular ad hoc networking (VANET) concept has supported the development of emerging safety related applications for vehicles based on cooperative awareness between vehicles. This cooperative awareness can be achieved by exploiting wireless sensors and technologies to transmit periodic messages to neighboring vehicles. These messages normally contain information regarding vehicles, such as position, speed, distance between vehicles, etc. For the transfer of safety messages, Wi-Fi and the suit of IEEE 802.11p/WAVE protocols were commonly used initially but now cellular-based LTE and 5G are the emerging technologies for VANETs. In this paper, a comparison is performed considering the European ITS-G5 standard, Wi-Fi, LTE and 5G by exchanging safety messages in VANETs. We have exchanged real-time road weather and traffic observation data to evaluate the performance of the aforementioned wireless technologies in terms of successful message delivery probability. Our results reveal that due to weak communication links and the lack of line of sight (LOS) communication for Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) scenarios, Wi-Fi and 802.11p are outperformed by LTE and 5G networks.
{"title":"Poster: Connected Vehicles using Short-range (Wi-Fi & IEEE 802.11p) and Long-range Cellular Networks (LTE & 5G)","authors":"Muhammad Naeem Tahir, M. Katz, Zunera Javed","doi":"10.1109/ICNP52444.2021.9651959","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651959","url":null,"abstract":"In recent years, the vehicular ad hoc networking (VANET) concept has supported the development of emerging safety related applications for vehicles based on cooperative awareness between vehicles. This cooperative awareness can be achieved by exploiting wireless sensors and technologies to transmit periodic messages to neighboring vehicles. These messages normally contain information regarding vehicles, such as position, speed, distance between vehicles, etc. For the transfer of safety messages, Wi-Fi and the suit of IEEE 802.11p/WAVE protocols were commonly used initially but now cellular-based LTE and 5G are the emerging technologies for VANETs. In this paper, a comparison is performed considering the European ITS-G5 standard, Wi-Fi, LTE and 5G by exchanging safety messages in VANETs. We have exchanged real-time road weather and traffic observation data to evaluate the performance of the aforementioned wireless technologies in terms of successful message delivery probability. Our results reveal that due to weak communication links and the lack of line of sight (LOS) communication for Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) scenarios, Wi-Fi and 802.11p are outperformed by LTE and 5G networks.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121064034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651915
Osama Shahid, Viraaji Mothukuri, Seyedamin Pouriyeh, R. Parizi, H. Shahriar
Billions of IoT devices are connected to networks all around us, enabling cyber-physical systems. These devices can carry and generate user-sensitive data, examples of such devices are smartwatches, medical equipment, and smart home gadgets. Individual IoT devices have some form of intrusion detection system integrated, but once they are all connected, a network threat to one device could mean a threat to many. IoT devices must have a robust intrusion detection system that would keep devices secure over a network. To aid with this, we provide a machine learning solution that adheres to Global Data Protection Regulation by keeping the user data secure locally on the IoT device itself. We propose a Federated Learning (FL) approach that capitalizes on a decentralized and collaborative way of training machine learning models. In this study, we practice federated learning technique to train and create a robust intrusion detection model for the security of IoT devices. We evaluate our proposed approach using three different use-cases to show the security enhancements that improve using the FL technique, resulting in a more reliable performance in this domain.
{"title":"Detecting Network Attacks using Federated Learning for IoT Devices","authors":"Osama Shahid, Viraaji Mothukuri, Seyedamin Pouriyeh, R. Parizi, H. Shahriar","doi":"10.1109/ICNP52444.2021.9651915","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651915","url":null,"abstract":"Billions of IoT devices are connected to networks all around us, enabling cyber-physical systems. These devices can carry and generate user-sensitive data, examples of such devices are smartwatches, medical equipment, and smart home gadgets. Individual IoT devices have some form of intrusion detection system integrated, but once they are all connected, a network threat to one device could mean a threat to many. IoT devices must have a robust intrusion detection system that would keep devices secure over a network. To aid with this, we provide a machine learning solution that adheres to Global Data Protection Regulation by keeping the user data secure locally on the IoT device itself. We propose a Federated Learning (FL) approach that capitalizes on a decentralized and collaborative way of training machine learning models. In this study, we practice federated learning technique to train and create a robust intrusion detection model for the security of IoT devices. We evaluate our proposed approach using three different use-cases to show the security enhancements that improve using the FL technique, resulting in a more reliable performance in this domain.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122179563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651911
Sourav Panda, K. Ramakrishnan, L. Bhuyan
Data center workload fluctuations need periodic, but careful scheduling to minimize power consumption while meeting the task completion time requirements. Existing data center scheduling systems tightly pack containers to save power. However, with the growth of multi-tiered applications, there is a significant need to account for the affinity between application components, to minimize communication overheads and latency. Centralized container scheduling systems using graph partitioning algorithms cause a significant number of task migrations, with associated downtime.We design pMACH, a novel distributed container scheduling scheme for optimizing both power and task completion time in data centers. It minimizes task migrations and packs frequently communicating containers together without overloading servers. pMACH operates at peak energy efficiency, thus reducing energy consumption while also providing greater headroom for unpredictable workload spikes. We also propose in-network monitoring using smartNICs (sNIC) to measure the communications and then perform scheduling in a hierarchical, parallelized framework to achieve high performance and scalability. pMACH is based on incremental partitioning and it leverages the previous scheduling decision to significantly reduce the number of containers moved between servers, avoiding application downtime.Both testbed measurements and large-scale trace-driven simulations show that pMACH saves at least 13.44% more power compared to previous scheduling systems. It speeds task completion, reducing the 95th percentile by a factor of 1.76-2.11 compared to existing container scheduling schemes. Compared to other static graph-based approaches, our incremental partitioning technique reduces migrations per epoch by 82%.
{"title":"pMACH: Power and Migration Aware Container scHeduling","authors":"Sourav Panda, K. Ramakrishnan, L. Bhuyan","doi":"10.1109/ICNP52444.2021.9651911","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651911","url":null,"abstract":"Data center workload fluctuations need periodic, but careful scheduling to minimize power consumption while meeting the task completion time requirements. Existing data center scheduling systems tightly pack containers to save power. However, with the growth of multi-tiered applications, there is a significant need to account for the affinity between application components, to minimize communication overheads and latency. Centralized container scheduling systems using graph partitioning algorithms cause a significant number of task migrations, with associated downtime.We design pMACH, a novel distributed container scheduling scheme for optimizing both power and task completion time in data centers. It minimizes task migrations and packs frequently communicating containers together without overloading servers. pMACH operates at peak energy efficiency, thus reducing energy consumption while also providing greater headroom for unpredictable workload spikes. We also propose in-network monitoring using smartNICs (sNIC) to measure the communications and then perform scheduling in a hierarchical, parallelized framework to achieve high performance and scalability. pMACH is based on incremental partitioning and it leverages the previous scheduling decision to significantly reduce the number of containers moved between servers, avoiding application downtime.Both testbed measurements and large-scale trace-driven simulations show that pMACH saves at least 13.44% more power compared to previous scheduling systems. It speeds task completion, reducing the 95th percentile by a factor of 1.76-2.11 compared to existing container scheduling schemes. Compared to other static graph-based approaches, our incremental partitioning technique reduces migrations per epoch by 82%.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124104363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651956
Shunsuke Higuchi, Y. Koizumi, Junji Takemasa, A. Tagami, T. Hasegawa
This paper proposes an IP forwarding information base (FIB) encoding leveraging an emerging data structure called a learned index , which uses machine learning to associate key-position pairs in a key-value store. A learned index for FIB lookups is expected to yield a more compact representation and faster lookups compared to existing FIBs based on tries or hash tables, at the cost of efficient FIB updates, which is difficult to support with a learned index. We optimize our implementation for lookup speed, exploiting that for efficient FIB lookups it is enough to approximate the key-position pairs with a piece-wise linear function, instead of having to learn the key-position pairs. The experiments using real BGP routing information snapshots suggest that the size of the proposed FIB is compact and lookup speed is sufficiently fast regardless of the length of matched prefixes.
{"title":"Learned FIB: Fast IP Forwarding without Longest Prefix Matching","authors":"Shunsuke Higuchi, Y. Koizumi, Junji Takemasa, A. Tagami, T. Hasegawa","doi":"10.1109/ICNP52444.2021.9651956","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651956","url":null,"abstract":"This paper proposes an IP forwarding information base (FIB) encoding leveraging an emerging data structure called a learned index , which uses machine learning to associate key-position pairs in a key-value store. A learned index for FIB lookups is expected to yield a more compact representation and faster lookups compared to existing FIBs based on tries or hash tables, at the cost of efficient FIB updates, which is difficult to support with a learned index. We optimize our implementation for lookup speed, exploiting that for efficient FIB lookups it is enough to approximate the key-position pairs with a piece-wise linear function, instead of having to learn the key-position pairs. The experiments using real BGP routing information snapshots suggest that the size of the proposed FIB is compact and lookup speed is sufficiently fast regardless of the length of matched prefixes.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651926
Jiali Chen, Yang Qin
With the development of blockchain technology, people always expect that blockchain can be applied to other fields. However, the low Transaction Processing Speed (TPS) and broadcast delay of blockchain still restrict the application of blockchain. To solve these problems, we propose a new scheme named GVScheme to improve the scalability of blockchain network. GVScheme introduces the role of guarantor based on trust value mechanism. The guarantor node will guarantee the block spread in the network. When the node receives the guarantee block from the guarantor node, the order of verification block and propagation block will be determined according to the trust value of the guarantor. By reducing the block verification time, the block propagation delay in the network will also be reduced. It is worth mentioning that our scheme keeps the minimum modification to the blockchain, and may even be directly applied to the blockchain network. Simulation results show that GVScheme can effectively reduce block propagation delay and the fork rate in blockchain network. When the block size and the number of nodes increase, GVScheme also shows great performance. Thus, under the same fork rate, the blockchain using GVScheme can allow less mining interval and larger block size limit.
{"title":"Reducing Block Propagation Delay in Blockchain Networks via Guarantee Verification","authors":"Jiali Chen, Yang Qin","doi":"10.1109/ICNP52444.2021.9651926","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651926","url":null,"abstract":"With the development of blockchain technology, people always expect that blockchain can be applied to other fields. However, the low Transaction Processing Speed (TPS) and broadcast delay of blockchain still restrict the application of blockchain. To solve these problems, we propose a new scheme named GVScheme to improve the scalability of blockchain network. GVScheme introduces the role of guarantor based on trust value mechanism. The guarantor node will guarantee the block spread in the network. When the node receives the guarantee block from the guarantor node, the order of verification block and propagation block will be determined according to the trust value of the guarantor. By reducing the block verification time, the block propagation delay in the network will also be reduced. It is worth mentioning that our scheme keeps the minimum modification to the blockchain, and may even be directly applied to the blockchain network. Simulation results show that GVScheme can effectively reduce block propagation delay and the fork rate in blockchain network. When the block size and the number of nodes increase, GVScheme also shows great performance. Thus, under the same fork rate, the blockchain using GVScheme can allow less mining interval and larger block size limit.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651981
Ying He, Yuhang Wang, Qiuzhen Lin, Jianqiang Li, V. Leung
With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computing and caching resources. In fact, vehicular networks are nonstationary, and how to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important, however, really challenging. In this paper, we propose a general framework that can enable fast-adaptive edge resource allocation for dynamic vehicular environment. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs). We combine hierarchical reinforcement learning with meta learning, which makes our proposed framework available to quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. The extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios. This is consistent with the real-world situations and can significantly improve the performance of resource allocation in dynamic vehicular networks.
{"title":"A Fast-adaptive Edge Resource Allocation Strategy for Dynamic Vehicular Networks","authors":"Ying He, Yuhang Wang, Qiuzhen Lin, Jianqiang Li, V. Leung","doi":"10.1109/ICNP52444.2021.9651981","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651981","url":null,"abstract":"With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computing and caching resources. In fact, vehicular networks are nonstationary, and how to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important, however, really challenging. In this paper, we propose a general framework that can enable fast-adaptive edge resource allocation for dynamic vehicular environment. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs). We combine hierarchical reinforcement learning with meta learning, which makes our proposed framework available to quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. The extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios. This is consistent with the real-world situations and can significantly improve the performance of resource allocation in dynamic vehicular networks.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131129445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651917
Usman Ahmed, Chun-Wei Lin, Gautam Srivastava
This paper proposes a framework that can be employed to mitigate adversarial evasion attacks on Android malware classifiers. It extracts multiple discriminating feature subsets from a single Android app such that each subset has the potential to classify a huge dataset of malicious and benign Android apps independently. Moreover, it incorporates an ensemble of ML classifiers where each classifier is trained on different features subset. Finally, the ensemble model formulates a collaborative classification decision that is resilient against adversarial evasion attacks. Results showed that the designed model achieves good performance compared to the existing models.
{"title":"Generative Ensemble Learning for Mitigating Adversarial Malware Detection in IoT","authors":"Usman Ahmed, Chun-Wei Lin, Gautam Srivastava","doi":"10.1109/ICNP52444.2021.9651917","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651917","url":null,"abstract":"This paper proposes a framework that can be employed to mitigate adversarial evasion attacks on Android malware classifiers. It extracts multiple discriminating feature subsets from a single Android app such that each subset has the potential to classify a huge dataset of malicious and benign Android apps independently. Moreover, it incorporates an ensemble of ML classifiers where each classifier is trained on different features subset. Finally, the ensemble model formulates a collaborative classification decision that is resilient against adversarial evasion attacks. Results showed that the designed model achieves good performance compared to the existing models.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132079056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651955
Wei-Peng Tan, Binwei Wu
With the development of 5G, innovative applications requiring bounded transmission delays and zero packet loss emerge, e.g., AR, industrial automation, and smart grid. In this circumstance, time-sensitive networking (TSN) is proposed, which addresses the deterministic transmission in the local area networks. Nevertheless, TSN is essentially a Layer 2 technique, which cannot provide deterministic transmission on a large geographic area. To solve this problem, this paper proposes a hierarchical network for the end-to-end deterministic transmission. In the proposed network, we leverage CQF (i.e., one of the most efficient TSN mechanisms) in the access networks which aggregates the traffic from end-devices. Meanwhile, in the core network, we exploit the DIP (i.e., a well-known deterministic networking mechanism for backbone networks) for long-distance deterministic transmission. We design the cycle alignment mechanism to enable seamless and deterministic transmission among hierarchical networks. A joint schedule is also formulated, which introduces the traffic shaping at the network edge to maximize the network throughput. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission, even in the highly-load scenarios.
{"title":"Long-distance Deterministic Transmission among TSN Networks: Converging CQF and DIP","authors":"Wei-Peng Tan, Binwei Wu","doi":"10.1109/ICNP52444.2021.9651955","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651955","url":null,"abstract":"With the development of 5G, innovative applications requiring bounded transmission delays and zero packet loss emerge, e.g., AR, industrial automation, and smart grid. In this circumstance, time-sensitive networking (TSN) is proposed, which addresses the deterministic transmission in the local area networks. Nevertheless, TSN is essentially a Layer 2 technique, which cannot provide deterministic transmission on a large geographic area. To solve this problem, this paper proposes a hierarchical network for the end-to-end deterministic transmission. In the proposed network, we leverage CQF (i.e., one of the most efficient TSN mechanisms) in the access networks which aggregates the traffic from end-devices. Meanwhile, in the core network, we exploit the DIP (i.e., a well-known deterministic networking mechanism for backbone networks) for long-distance deterministic transmission. We design the cycle alignment mechanism to enable seamless and deterministic transmission among hierarchical networks. A joint schedule is also formulated, which introduces the traffic shaping at the network edge to maximize the network throughput. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission, even in the highly-load scenarios.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132867879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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