Pub Date : 2022-12-14DOI: 10.1109/NCA57778.2022.10013509
Richard Von Seck, F. Rezabek, Benedikt Jaeger, Sebastian Gallenmüller, G. Carle
Byzantine fault tolerant (BFT) consensus allows the construction of robust, distributed systems via the state-machine replication (SMR) approach. Still, after more than 40 years of research, limitations on performance and scalability for practical systems remain. A large corpus of existing work improves on consensus complexity, performance and introduces a multitude of optimization techniques. The state-of-the-art is complex. On the other hand, many protocols designed for practical deployments are built on strong, common assumptions about underlying communication and authentication primitives. To fulfill these assumptions, often, commodity tools and libraries are employed without further analysis and caution for negative interplay.Instead of contributing to the existing complexity, we choose a different approach. In this paper, we outline the feasibility and potential impact of the optimization of common building blocks of BFT-SMR systems. We systemize existing work in terms of common model assumptions and identify optimization potential. Finally, we choose the building block of networking transport as a representative example and analyze its optimization space, both in context of general BFT-SMR systems and a case study of the HotStuff protocol. We describe behavior, challenges, and desired configuration of network transports for use in byzantine agreement, and identify lossy links as the main catalyst for significant performance differences between protocols and configurations.
{"title":"BFT-Blocks: The Case for Analyzing Networking in Byzantine Fault Tolerant Consensus","authors":"Richard Von Seck, F. Rezabek, Benedikt Jaeger, Sebastian Gallenmüller, G. Carle","doi":"10.1109/NCA57778.2022.10013509","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013509","url":null,"abstract":"Byzantine fault tolerant (BFT) consensus allows the construction of robust, distributed systems via the state-machine replication (SMR) approach. Still, after more than 40 years of research, limitations on performance and scalability for practical systems remain. A large corpus of existing work improves on consensus complexity, performance and introduces a multitude of optimization techniques. The state-of-the-art is complex. On the other hand, many protocols designed for practical deployments are built on strong, common assumptions about underlying communication and authentication primitives. To fulfill these assumptions, often, commodity tools and libraries are employed without further analysis and caution for negative interplay.Instead of contributing to the existing complexity, we choose a different approach. In this paper, we outline the feasibility and potential impact of the optimization of common building blocks of BFT-SMR systems. We systemize existing work in terms of common model assumptions and identify optimization potential. Finally, we choose the building block of networking transport as a representative example and analyze its optimization space, both in context of general BFT-SMR systems and a case study of the HotStuff protocol. We describe behavior, challenges, and desired configuration of network transports for use in byzantine agreement, and identify lossy links as the main catalyst for significant performance differences between protocols and configurations.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740171","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013557
Erick Petersen, Jorge López, N. Kushik, Claude Poletti, D. Zeghlache
We present a novel formalism for describing the evolution of dynamic-link network parameters; it is based on the Cellular Automaton (CA) model. Such formalism is of wide-use for modeling natural (e.g., physical, chemical, etc.) processes. We propose a particular model and survey the related work, with respect to the use of CA to simulate various communication networks. We showcase the flexibility of the proposed approach to model different evolution patterns. These patterns can be used to emulate / simulate different network scenarios (states of the network parameters), and test novel implementations under distinct conditions. Additionally, we propose an algorithm for guaranteeing that the described patterns hold properties of interest, within a bounded time.
{"title":"On using Cellular Automata for Modeling the Evolution of Dynamic-Link Network Parameters","authors":"Erick Petersen, Jorge López, N. Kushik, Claude Poletti, D. Zeghlache","doi":"10.1109/NCA57778.2022.10013557","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013557","url":null,"abstract":"We present a novel formalism for describing the evolution of dynamic-link network parameters; it is based on the Cellular Automaton (CA) model. Such formalism is of wide-use for modeling natural (e.g., physical, chemical, etc.) processes. We propose a particular model and survey the related work, with respect to the use of CA to simulate various communication networks. We showcase the flexibility of the proposed approach to model different evolution patterns. These patterns can be used to emulate / simulate different network scenarios (states of the network parameters), and test novel implementations under distinct conditions. Additionally, we propose an algorithm for guaranteeing that the described patterns hold properties of interest, within a bounded time.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122106878","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}
Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge Computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for the mean GFT is 0 ms in the best case and 9.26 ms in the worst case, providing a uniform user experience, even when mobility is as high as 50% (it means 50% of the players are moving). When there is mobility, the GFT increases by 28.29% in the best case and 37.18% in the worst case, compared to a no-mobility scenario. We also demonstrate that, given computing power, there is a tradeoff between responsiveness and GFT.
{"title":"Mobility-aware Multi-Access Edge Computing for Multiplayer Augmented and Virtual Reality Gaming","authors":"Ramesh Singh, Radhika Sukapuram, Suchetana Chakraborty","doi":"10.1109/NCA57778.2022.10013599","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013599","url":null,"abstract":"Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge Computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for the mean GFT is 0 ms in the best case and 9.26 ms in the worst case, providing a uniform user experience, even when mobility is as high as 50% (it means 50% of the players are moving). When there is mobility, the GFT increases by 28.29% in the best case and 37.18% in the worst case, compared to a no-mobility scenario. We also demonstrate that, given computing power, there is a tradeoff between responsiveness and GFT.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131288922","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013560
Lucas H. Vicente, Samih Eisa, M. Pardal
Millions of tourists each year use smartphone applications to discover points of interest. Despite relying heavily on location sensing, most of them are susceptible to location spoofing, but not all. CROSS City is a smart tourism application that rewards users for completing tourist itineraries and uses location certificates to prevent attacks. In this case, the location verification relies on the periodic collection of public Wi-Fi network observations by multiple users to make sure the travelers actually went to the points of interest.In this paper, we introduce the Location-Certification-as-a-Service (LoCaaS) approach, supported by a cloud-native and improved location certification system, capable of producing and validating time-bound location proofs using network data collected by tourists’ mobile devices. We show that the system can efficiently compute the stable and transient networks for a given location that are used, respectively, to validate the location of a tourist and to prove the time-of-visit. The system was deployed to the Google Cloud Platform and was validated with performance experiments and a real-world deployment.
{"title":"LoCaaS: Location-Certification-as-a-Service","authors":"Lucas H. Vicente, Samih Eisa, M. Pardal","doi":"10.1109/NCA57778.2022.10013560","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013560","url":null,"abstract":"Millions of tourists each year use smartphone applications to discover points of interest. Despite relying heavily on location sensing, most of them are susceptible to location spoofing, but not all. CROSS City is a smart tourism application that rewards users for completing tourist itineraries and uses location certificates to prevent attacks. In this case, the location verification relies on the periodic collection of public Wi-Fi network observations by multiple users to make sure the travelers actually went to the points of interest.In this paper, we introduce the Location-Certification-as-a-Service (LoCaaS) approach, supported by a cloud-native and improved location certification system, capable of producing and validating time-bound location proofs using network data collected by tourists’ mobile devices. We show that the system can efficiently compute the stable and transient networks for a given location that are used, respectively, to validate the location of a tourist and to prove the time-of-visit. The system was deployed to the Google Cloud Platform and was validated with performance experiments and a real-world deployment.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053174","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013504
Maya Kapoor, Siddharth Krishnan, Thomas Moyer
Deep packet inspection is a primary tool for security specialists, surveillance analysts, and network engineers to lawfully intercept and analyze network traffic. In order to process this data or select streams of interest from the large amount of data flowing in today’s internet, solutions must be capable of identifying network traffic as quickly and accurately as possible. The ever-increasing diversity of data as well as sheer size has rendered the current regular expression matching and filtering solutions ineffective. We propose locality-sensitive hash embedding techniques Alpine and Palm for packet analysis. The fixed size of hashes as well as the adaptability of distance measures is proven to address the network traffic classification problem in our experiments and improves scalability over current state-of-the-art, automata-based search engines. In this paper, we analyze the system’s ability to classify network traffic by many data layer protocols and traffic types with over 99% accuracy. The model is also proven effective in areas where the regular expressions are inapplicable, such as traffic profiling. Finally, we provide real benchmarks of the system’s ability to scale to large signature and hash sets with much improved performance, demonstrating real-world applicability and generalizability of locality-sensitive hashing to deep packet inspection technology.
{"title":"Deep Packet Inspection at Scale: Search Optimization Through Locality-Sensitive Hashing","authors":"Maya Kapoor, Siddharth Krishnan, Thomas Moyer","doi":"10.1109/NCA57778.2022.10013504","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013504","url":null,"abstract":"Deep packet inspection is a primary tool for security specialists, surveillance analysts, and network engineers to lawfully intercept and analyze network traffic. In order to process this data or select streams of interest from the large amount of data flowing in today’s internet, solutions must be capable of identifying network traffic as quickly and accurately as possible. The ever-increasing diversity of data as well as sheer size has rendered the current regular expression matching and filtering solutions ineffective. We propose locality-sensitive hash embedding techniques Alpine and Palm for packet analysis. The fixed size of hashes as well as the adaptability of distance measures is proven to address the network traffic classification problem in our experiments and improves scalability over current state-of-the-art, automata-based search engines. In this paper, we analyze the system’s ability to classify network traffic by many data layer protocols and traffic types with over 99% accuracy. The model is also proven effective in areas where the regular expressions are inapplicable, such as traffic profiling. Finally, we provide real benchmarks of the system’s ability to scale to large signature and hash sets with much improved performance, demonstrating real-world applicability and generalizability of locality-sensitive hashing to deep packet inspection technology.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129343462","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013505
Ibtissem Brahmi, Monia Hamdi, Inès Rahmany, F. Zarai
It becomes very common to use cell phones in public transportation and the cars. Vehicular networking has a major problem which is the degradation of signal quality due to interference and the large number of mobile devices. Artificial intelligence (AI) is a promising technique for next-generation wireless networks. Deep learning is a type of AI derived from machine learning; here the machine can learn by itself, unlike programming where it is content to execute rules to the letter predetermined. In addition, AI can be explored in order to solve various problems. In this paper, we tackle the problem of resource allocation in a vehicular small cell network (VSCN). Indeed, we propose a new mechanism based on deep reinforcement learning denoted Resource Allocation based Deep Reinforcement Learning (RA-DRL). The main goal of our proposed method is to maximize the total system sum rate (throughput) while guaranteeing minimum interferences, Quality of Service (QoS) and the demand for all users. Simulation results demonstrate that our proposed RA-DRL algorithm exhibits better performance comparing to the other methods, by maximizing the total system sum rate while maintaining inter-VSCs interferences and a minimum latency
{"title":"Deep Reinforcement Learning for Downlink Resource Allocation in Vehicular Small Cell Networks","authors":"Ibtissem Brahmi, Monia Hamdi, Inès Rahmany, F. Zarai","doi":"10.1109/NCA57778.2022.10013505","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013505","url":null,"abstract":"It becomes very common to use cell phones in public transportation and the cars. Vehicular networking has a major problem which is the degradation of signal quality due to interference and the large number of mobile devices. Artificial intelligence (AI) is a promising technique for next-generation wireless networks. Deep learning is a type of AI derived from machine learning; here the machine can learn by itself, unlike programming where it is content to execute rules to the letter predetermined. In addition, AI can be explored in order to solve various problems. In this paper, we tackle the problem of resource allocation in a vehicular small cell network (VSCN). Indeed, we propose a new mechanism based on deep reinforcement learning denoted Resource Allocation based Deep Reinforcement Learning (RA-DRL). The main goal of our proposed method is to maximize the total system sum rate (throughput) while guaranteeing minimum interferences, Quality of Service (QoS) and the demand for all users. Simulation results demonstrate that our proposed RA-DRL algorithm exhibits better performance comparing to the other methods, by maximizing the total system sum rate while maintaining inter-VSCs interferences and a minimum latency","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125462653","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013629
Rafael Figueiredo, Samih Eisa, M. Pardal
Location is an important attribute for many mobile applications but it needs to be verified. For example, a user of a tourism application that gives out rewards can falsify his location to pretend that he has visited many attractions and thus receive benefits without deserving them. To counter these attacks, the system asks users to prove their location through witnesses, i.e., other devices that happen to be at the location at the same time and that can be partially trusted. However, for this approach to be effective, it is important to keep track of the witness behavior over time. Many crowdsourcing applications, like Waze, build up reputations for their users, and rely on user co-location and redundant inputs for data verification.In this work, we present SureRepute, a reputation system capable of withstanding reputation attacks while still maintaining user privacy. The results show that the system is able to protect itself and its configuration is flexible, allowing different trade-offs between security and usability, as required in real-world applications. The experiments show how the reputation system can be easily integrated into existing applications without producing a significant overhead in response times.
{"title":"SureRepute: Reputation System for Crowdsourced Location Witnesses","authors":"Rafael Figueiredo, Samih Eisa, M. Pardal","doi":"10.1109/NCA57778.2022.10013629","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013629","url":null,"abstract":"Location is an important attribute for many mobile applications but it needs to be verified. For example, a user of a tourism application that gives out rewards can falsify his location to pretend that he has visited many attractions and thus receive benefits without deserving them. To counter these attacks, the system asks users to prove their location through witnesses, i.e., other devices that happen to be at the location at the same time and that can be partially trusted. However, for this approach to be effective, it is important to keep track of the witness behavior over time. Many crowdsourcing applications, like Waze, build up reputations for their users, and rely on user co-location and redundant inputs for data verification.In this work, we present SureRepute, a reputation system capable of withstanding reputation attacks while still maintaining user privacy. The results show that the system is able to protect itself and its configuration is flexible, allowing different trade-offs between security and usability, as required in real-world applications. The experiments show how the reputation system can be easily integrated into existing applications without producing a significant overhead in response times.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128816289","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013583
M. Lavassani, J. Åkerberg, M. Björkman
Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.
{"title":"Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic","authors":"M. Lavassani, J. Åkerberg, M. Björkman","doi":"10.1109/NCA57778.2022.10013583","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013583","url":null,"abstract":"Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117196008","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 : 2022-12-14DOI: 10.1109/NCA57778.2022.10013588
Priyanka Kamboj, Sujata Pal
Energy efficiency is considered a significant concern in the deployment and operation of data networks. The network devices need an enormous amount of energy to function, which leads to an increase in energy consumption in the data center networks (DCNs). Software-defined networking (SDN) solves the problem by adjusting the energy consumption proportionate to the amount of traffic. The network devices with low load can be turned into switch-OFF mode after transferring the traffic to another device. This energy-saving approach by analyzing the user’s traffic demand increases the overall network utilization. In this work, we study the energy optimization problem using multipath routing in SDN-enabled data center networks (SD-DCN). We formulate the energy optimization as an integer linear program (ILP) problem to minimize the active Open vSwitch (OVS) switches in the network. To solve the problem in polynomial time, we propose a heuristic approach to route the traffic flows in the SD-DCN. The proposed approach is tested over data center network topologies – Fat-Tree and BCube. The simulation results show that our proposed approach presents an enhancement of 24%, 16%, and 15% in average delay, throughput, and energy savings in the Fat-Tree topology compared to the benchmark schemes. Further, our proposed approach achieves 17%, 19%, and 17% enhancement in average delay, throughput, and energy savings in the BCube topology compared to the benchmark schemes.
能源效率被认为是数据网络部署和运行中的一个重要问题。网络设备需要大量的能量才能正常工作，这导致了数据中心网络能耗的增加。软件定义网络SDN (software defined networking)解决了这个问题，它根据业务量的大小调整能耗。对于低负载的网络设备，可以将流量转移到其他设备后切换为switch-OFF模式。这种通过分析用户流量需求的节能方法提高了网络的整体利用率。在这项工作中，我们研究了在支持sdn的数据中心网络(SD-DCN)中使用多路径路由的能量优化问题。我们将能量优化化为一个整数线性规划(ILP)问题，以最小化网络中活跃的开放式虚拟交换机(OVS)交换机。为了在多项式时间内解决这个问题，我们提出了一种启发式的方法来路由SD-DCN中的交通流。提出的方法在数据中心网络拓扑- Fat-Tree和BCube上进行了测试。仿真结果表明，与基准方案相比，我们提出的方法在Fat-Tree拓扑中的平均延迟、吞吐量和节能方面分别提高了24%、16%和15%。此外，与基准方案相比，我们提出的方法在BCube拓扑中的平均延迟、吞吐量和节能方面分别提高了17%、19%和17%。
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Pub Date : 2022-12-14DOI: 10.1109/NCA57778.2022.10013565
Gabriele Gambigliani Zoccoli, Francesco Pollicino, Dario Stabili, Mirco Marchetti
This paper proposes SixPack v2, an enhanced version of the SixPack attack that allows to evade even state-of-the-art misbehavior detection systems. As the original SixPack, SixPack v2 is a dynamic attack targeting other C-ITS entities by simulating the sudden activation of the braking system with consequent activation of the Anti-lock Braking System. SixPack v2 achieves better evasion by improving the main phases of the attack (FakeBrake, Recovery, and Rejoin) through a novel path-reconstruction algorithm that generates a more realistic representation of the real vehicle trajectory. We experimentally evaluate the evasion capabilities of SixPack v2 using the F2MD framework on the LuSTMini city scenario, and we compared the detection performance of the F2MD framework on both versions of SixPack. Results show that SixPack v2 evades detection with a significantly higher likelihood with respect to the initial version of the attack, even against the latest version of F2MD.
{"title":"SixPack v2: enhancing SixPack to avoid last generation misbehavior detectors in VANETs","authors":"Gabriele Gambigliani Zoccoli, Francesco Pollicino, Dario Stabili, Mirco Marchetti","doi":"10.1109/NCA57778.2022.10013565","DOIUrl":"https://doi.org/10.1109/NCA57778.2022.10013565","url":null,"abstract":"This paper proposes SixPack v2, an enhanced version of the SixPack attack that allows to evade even state-of-the-art misbehavior detection systems. As the original SixPack, SixPack v2 is a dynamic attack targeting other C-ITS entities by simulating the sudden activation of the braking system with consequent activation of the Anti-lock Braking System. SixPack v2 achieves better evasion by improving the main phases of the attack (FakeBrake, Recovery, and Rejoin) through a novel path-reconstruction algorithm that generates a more realistic representation of the real vehicle trajectory. We experimentally evaluate the evasion capabilities of SixPack v2 using the F2MD framework on the LuSTMini city scenario, and we compared the detection performance of the F2MD framework on both versions of SixPack. Results show that SixPack v2 evades detection with a significantly higher likelihood with respect to the initial version of the attack, even against the latest version of F2MD.","PeriodicalId":251728,"journal":{"name":"2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114660920","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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