Pub Date : 2024-08-01DOI: 10.1109/TNSM.2024.3436887
Yuhan Su;Yuchen Lin;Sicong Liu;Minghui Liwang;Xinqin Liao;Tingzhu Wu;Zhong Chen;Xianbin Wang
Given the exponential surge in data traffic and the proliferation of connected smart devices, traditional radio frequency (RF)-based wireless communication systems have to confront mounting challenges of spectrum scarcity and access congestion, particularly for networks operated in low-frequency bands. Visible light communication (VLC) technology has emerged as a promising solution, but it has own limitations, including coverage constraints and limited uplink capability, necessitating hybrid systems that leverage VLC and RF. This paper focuses on an indoor hybrid VLC-RF system extending VLC to Wi-Fi’s public spectrum, enabling VLC’s uplink via RF while enhancing system capacity. Yet, integrating VLC-RF with Wi-Fi introduces new challenges due to the coexistence of VLC-RF with existing Wi-Fi systems. To address these challenges, we propose an intelligent coexistence approach, dynamically adjusts duty cycles to ensure fairness and performance optimization between VLC-RF and Wi-Fi. Moreover, a spectrum multiplexing algorithm is introduced in the coexistence approach to enable the hybrid VLC-RF system’s multiplexing transmission on public spectrum, while preserving Wi-Fi system transmission integrity without interference, thereby further optimizing resource utilization. Extensive simulations on a meticulously constructed system-level platform validate our approach, showcasing its efficacy in enhancing system performance while maintaining equitable transmission between hybrid VLC-RF and Wi-Fi systems.
{"title":"Coexistence of Hybrid VLC-RF and Wi-Fi for Indoor Wireless Communication Systems: An Intelligent Approach","authors":"Yuhan Su;Yuchen Lin;Sicong Liu;Minghui Liwang;Xinqin Liao;Tingzhu Wu;Zhong Chen;Xianbin Wang","doi":"10.1109/TNSM.2024.3436887","DOIUrl":"10.1109/TNSM.2024.3436887","url":null,"abstract":"Given the exponential surge in data traffic and the proliferation of connected smart devices, traditional radio frequency (RF)-based wireless communication systems have to confront mounting challenges of spectrum scarcity and access congestion, particularly for networks operated in low-frequency bands. Visible light communication (VLC) technology has emerged as a promising solution, but it has own limitations, including coverage constraints and limited uplink capability, necessitating hybrid systems that leverage VLC and RF. This paper focuses on an indoor hybrid VLC-RF system extending VLC to Wi-Fi’s public spectrum, enabling VLC’s uplink via RF while enhancing system capacity. Yet, integrating VLC-RF with Wi-Fi introduces new challenges due to the coexistence of VLC-RF with existing Wi-Fi systems. To address these challenges, we propose an intelligent coexistence approach, dynamically adjusts duty cycles to ensure fairness and performance optimization between VLC-RF and Wi-Fi. Moreover, a spectrum multiplexing algorithm is introduced in the coexistence approach to enable the hybrid VLC-RF system’s multiplexing transmission on public spectrum, while preserving Wi-Fi system transmission integrity without interference, thereby further optimizing resource utilization. Extensive simulations on a meticulously constructed system-level platform validate our approach, showcasing its efficacy in enhancing system performance while maintaining equitable transmission between hybrid VLC-RF and Wi-Fi systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6465-6479"},"PeriodicalIF":4.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887054","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 : 2024-08-01DOI: 10.1109/TNSM.2024.3437217
Mahdieh Ahmadi;Arash Moayyedi;Muhammad Sulaiman;Mohammad A. Salahuddin;Raouf Boutaba;Aladdin Saleh
The virtualization and distribution of 5G Radio Access Network (RAN) functions across radio unit (RU), distributed unit (DU), and centralized unit (CU) in conjunction with multi-access edge computing (MEC) enable the creation of network slices tailored for various applications with distinct quality of service (QoS) demands. Nonetheless, given the dynamic nature of slice requests and limited network resources, optimizing long-term revenue for infrastructure providers (InPs) through real-time admission and embedding of slice requests poses a significant challenge. Prior works have employed Deep Reinforcement Learning (DRL) to address this issue, but these approaches require re-training with the slightest topology changes due to node/link failure or overlook the joint consideration of slice admission and embedding problems. This paper proposes a novel method, utilizing multi-agent DRL and Graph Attention Networks (GATs), to overcome these limitations. Specifically, we develop topology-independent admission and slicing agents that are scalable and generalizable across diverse metropolitan networks. Results demonstrate substantial revenue gains-up to 35.2% compared to heuristics and 19.5% when compared to other DRL-based methods. Moreover, our approach showcases robust performance in different network failure scenarios and substrate networks not seen during training without the need for re-training or re-tuning. Additionally, we bring interpretability by analyzing attention maps, which enables InPs to identify network bottlenecks, increase capacity at critical nodes, and gain a clear understanding of the model decision-making process.
{"title":"Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation","authors":"Mahdieh Ahmadi;Arash Moayyedi;Muhammad Sulaiman;Mohammad A. Salahuddin;Raouf Boutaba;Aladdin Saleh","doi":"10.1109/TNSM.2024.3437217","DOIUrl":"10.1109/TNSM.2024.3437217","url":null,"abstract":"The virtualization and distribution of 5G Radio Access Network (RAN) functions across radio unit (RU), distributed unit (DU), and centralized unit (CU) in conjunction with multi-access edge computing (MEC) enable the creation of network slices tailored for various applications with distinct quality of service (QoS) demands. Nonetheless, given the dynamic nature of slice requests and limited network resources, optimizing long-term revenue for infrastructure providers (InPs) through real-time admission and embedding of slice requests poses a significant challenge. Prior works have employed Deep Reinforcement Learning (DRL) to address this issue, but these approaches require re-training with the slightest topology changes due to node/link failure or overlook the joint consideration of slice admission and embedding problems. This paper proposes a novel method, utilizing multi-agent DRL and Graph Attention Networks (GATs), to overcome these limitations. Specifically, we develop topology-independent admission and slicing agents that are scalable and generalizable across diverse metropolitan networks. Results demonstrate substantial revenue gains-up to 35.2% compared to heuristics and 19.5% when compared to other DRL-based methods. Moreover, our approach showcases robust performance in different network failure scenarios and substrate networks not seen during training without the need for re-training or re-tuning. Additionally, we bring interpretability by analyzing attention maps, which enables InPs to identify network bottlenecks, increase capacity at critical nodes, and gain a clear understanding of the model decision-making process.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5384-5399"},"PeriodicalIF":4.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880559","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 : 2024-08-01DOI: 10.1109/TNSM.2024.3436674
Md. Masuduzzaman;Tariq Rahim;Anik Islam;Soo Young Shin
This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.
{"title":"UAV-Employed Intelligent Approach to Identify Injured Soldier on Blockchain-Integrated Internet of Battlefield Things","authors":"Md. Masuduzzaman;Tariq Rahim;Anik Islam;Soo Young Shin","doi":"10.1109/TNSM.2024.3436674","DOIUrl":"10.1109/TNSM.2024.3436674","url":null,"abstract":"This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5197-5214"},"PeriodicalIF":4.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880635","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 : 2024-08-01DOI: 10.1109/TNSM.2024.3437165
Bita Fatemipour;Zhe Zhang;Marc St-Hilaire
Data centers have undergone significant expansions in recent years, as cloud service providers seek to improve the quality of service and reduce operational costs. Cloud providers are investing heavily in inter-data center wide-area networks, which help to transport traffic between geographically distributed data centers. However, efficient workload management in complex large-scale networks with a dynamic environment is challenging. In this regard, researchers have developed various solutions to address different challenges for data transfer in inter-data center networks. In this paper, we present a comprehensive review of recent strategies and optimization schemes proposed in the literature to optimize data transfer in geographically distributed data centers. This review paper examines the challenges of data delivery and classifies recent existing solutions for addressing the issues based on communication patterns, objectives, proposed communication frameworks, and evaluation methods. In this study, we provide valuable insights into the current challenges and identify several promising research directions that require significant research endeavors in the future. The findings of this study are useful for researchers and practitioners interested in optimizing data transfer in inter-data center networks.
{"title":"A Survey on Replica Transfer Optimization Schemes in Geographically Distributed Data Centers","authors":"Bita Fatemipour;Zhe Zhang;Marc St-Hilaire","doi":"10.1109/TNSM.2024.3437165","DOIUrl":"10.1109/TNSM.2024.3437165","url":null,"abstract":"Data centers have undergone significant expansions in recent years, as cloud service providers seek to improve the quality of service and reduce operational costs. Cloud providers are investing heavily in inter-data center wide-area networks, which help to transport traffic between geographically distributed data centers. However, efficient workload management in complex large-scale networks with a dynamic environment is challenging. In this regard, researchers have developed various solutions to address different challenges for data transfer in inter-data center networks. In this paper, we present a comprehensive review of recent strategies and optimization schemes proposed in the literature to optimize data transfer in geographically distributed data centers. This review paper examines the challenges of data delivery and classifies recent existing solutions for addressing the issues based on communication patterns, objectives, proposed communication frameworks, and evaluation methods. In this study, we provide valuable insights into the current challenges and identify several promising research directions that require significant research endeavors in the future. The findings of this study are useful for researchers and practitioners interested in optimizing data transfer in inter-data center networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6301-6317"},"PeriodicalIF":4.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880636","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 : 2024-08-01DOI: 10.1109/TNSM.2024.3435516
Pin-Hsuan Chiang;Shi-Chun Tsai
In the current landscape of network technology, it is indisputable that the Domain Name System (DNS) plays a vital role but also encounters significant security challenges. Despite the potential of recent advancements in deep learning and machine learning, concept drift is often not addressed. In this work, we designed a DNS anomaly detection system leveraging client-domain associations. We propose the Modified Deterministic Sampling Classifier with weighted Bagging (MDSCB) method, a chunk-based ensemble learning approach addressing concept drift and data imbalance. It integrates weighted bagging, resampling, random feature selection, and a retention strategy for classifier updates, enhancing adaptability and efficiency. We conducted experiments using multiple real-world and synthetic datasets for evaluation. Empirical studies show that our detection system can help identify malicious domains that are difficult for firewalls to detect timely. Moreover, MDSCB outperforms other methods in terms of performance and efficiency.
{"title":"Detection of Malicious Domains With Concept Drift Using Ensemble Learning","authors":"Pin-Hsuan Chiang;Shi-Chun Tsai","doi":"10.1109/TNSM.2024.3435516","DOIUrl":"10.1109/TNSM.2024.3435516","url":null,"abstract":"In the current landscape of network technology, it is indisputable that the Domain Name System (DNS) plays a vital role but also encounters significant security challenges. Despite the potential of recent advancements in deep learning and machine learning, concept drift is often not addressed. In this work, we designed a DNS anomaly detection system leveraging client-domain associations. We propose the Modified Deterministic Sampling Classifier with weighted Bagging (MDSCB) method, a chunk-based ensemble learning approach addressing concept drift and data imbalance. It integrates weighted bagging, resampling, random feature selection, and a retention strategy for classifier updates, enhancing adaptability and efficiency. We conducted experiments using multiple real-world and synthetic datasets for evaluation. Empirical studies show that our detection system can help identify malicious domains that are difficult for firewalls to detect timely. Moreover, MDSCB outperforms other methods in terms of performance and efficiency.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6796-6809"},"PeriodicalIF":4.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880637","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 : 2024-07-31DOI: 10.1109/TNSM.2024.3436049
Hyunmin Noh;Seunggyu Ji;Yunmin Go;Gi Seok Park;Hwangjun Song
In this paper, we propose a resilient and fast block transmission system for Hyperledger Fabric in multi-cloud environments. The goal of the proposed system is to improve the scalability, transaction throughput, and resilience of Hyperledger Fabric by minimizing the block synchronization time among nodes. To achieve this goal, the proposed system is designed to deliver blocks quickly and reliably to all the participating nodes in time-varying multi-cloud environments. The proposed system includes the delay estimating process with O(N) control message overhead over the P2P network, the effective bandwidth estimating process for block transmission, the Gaussian Mixture Model-based clustering and cluster leader selecting process, and hybrid P2P multicast tree constructing process. In addition, a control message format and delivery process are proposed to efficiently provide hybrid P2P multicast tree and neighbor nodes information to all the participating nodes. And we propose a pull-based local block loss recovery process that can receive lost blocks from multi-node without complicated scheduling using a rateless code. The proposed system is fully implemented by using well-known open sources (e.g., Hyperledger Fabric, Docker, Containernet, and Mininet) and Go/C/Python. Experiment results show that the proposed system can reduce the maximum block arriving time among all the participating nodes by approximately 50%~95% compared to the existing algorithms. This improves not only blockchain transaction per second, but also resilience to various network-layer vulnerabilities and attacks that may occur when the block propagation delay increases.
{"title":"Resilient and Fast Block Transmission System for Scalable Hyperledger Fabric Blockchain in Multi-Cloud Environments","authors":"Hyunmin Noh;Seunggyu Ji;Yunmin Go;Gi Seok Park;Hwangjun Song","doi":"10.1109/TNSM.2024.3436049","DOIUrl":"10.1109/TNSM.2024.3436049","url":null,"abstract":"In this paper, we propose a resilient and fast block transmission system for Hyperledger Fabric in multi-cloud environments. The goal of the proposed system is to improve the scalability, transaction throughput, and resilience of Hyperledger Fabric by minimizing the block synchronization time among nodes. To achieve this goal, the proposed system is designed to deliver blocks quickly and reliably to all the participating nodes in time-varying multi-cloud environments. The proposed system includes the delay estimating process with O(N) control message overhead over the P2P network, the effective bandwidth estimating process for block transmission, the Gaussian Mixture Model-based clustering and cluster leader selecting process, and hybrid P2P multicast tree constructing process. In addition, a control message format and delivery process are proposed to efficiently provide hybrid P2P multicast tree and neighbor nodes information to all the participating nodes. And we propose a pull-based local block loss recovery process that can receive lost blocks from multi-node without complicated scheduling using a rateless code. The proposed system is fully implemented by using well-known open sources (e.g., Hyperledger Fabric, Docker, Containernet, and Mininet) and Go/C/Python. Experiment results show that the proposed system can reduce the maximum block arriving time among all the participating nodes by approximately 50%~95% compared to the existing algorithms. This improves not only blockchain transaction per second, but also resilience to various network-layer vulnerabilities and attacks that may occur when the block propagation delay increases.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5118-5134"},"PeriodicalIF":4.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873178","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.2024.3435544
Alex S. Santos;Eonassis Oliveira Santos;Sabidur Rahman;Lena Wosinska;Juliana de Santi;Gustavo B. Figueiredo
Network operators must deal with Classes of Service (CoS), which have several quality requirements, such as latency, bandwidth/capacity, priority, etc. Besides, it is observed an increase in the volume of traffic that is offered to the transport network. This traffic can be affected by network natural disasters or human-made attacks. In this case, network operators must decide which services to restore, considering their different requirements. In this work, we present a Lightpath Selection Algorithm (LSA) that aims to select lightpaths to be restored after a resource crunch. This algorithm has a multicriteria decision approach considering CoS, Bandwidth, number of Hops, and Holding time. Moreover, service degradation is also considered for those lightpaths that can not be restored with full bandwidth. Results show that our proposed algorithm can improve network restorability and availability without penalizing low-level CoSs.
{"title":"Multi-Criteria Decision Approach for Lightpath Restoration After Resource Crunch","authors":"Alex S. Santos;Eonassis Oliveira Santos;Sabidur Rahman;Lena Wosinska;Juliana de Santi;Gustavo B. Figueiredo","doi":"10.1109/TNSM.2024.3435544","DOIUrl":"10.1109/TNSM.2024.3435544","url":null,"abstract":"Network operators must deal with Classes of Service (CoS), which have several quality requirements, such as latency, bandwidth/capacity, priority, etc. Besides, it is observed an increase in the volume of traffic that is offered to the transport network. This traffic can be affected by network natural disasters or human-made attacks. In this case, network operators must decide which services to restore, considering their different requirements. In this work, we present a Lightpath Selection Algorithm (LSA) that aims to select lightpaths to be restored after a resource crunch. This algorithm has a multicriteria decision approach considering CoS, Bandwidth, number of Hops, and Holding time. Moreover, service degradation is also considered for those lightpaths that can not be restored with full bandwidth. Results show that our proposed algorithm can improve network restorability and availability without penalizing low-level CoSs.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5521-5531"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873445","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.2024.3435869
Jiaze Shang;Tianbo Lu;Pengfei Zhao
Selfish mining, one of the most renowned attack in Bitcoin, involves a selfish miner withholding discovered blocks and broadcasting them at an opportune moment to gain higher rewards than honest mining. However, selfish mining and its variants rely on two assumptions: the attacker solely engages in infiltration mining within the victim pool (attack assumption) and the system operates in a perfect network environment (network assumption). In this paper, we propose a novel attack called Selfish in Innocent Mining (SIM). SIM expands the range of attacker’s behaviors by incorporating selfish mining into the traditional framework of innocent and infiltration mining, without increasing the attacker’s computational power. Initially, we analyze all possible states of chains in the system and their transition probabilities in the context of the SIM attack using Markov Chain. We determine the attacker’s rewards in one victim pool, multiple victim pools, and the miner’s dilemma within different cases. Subsequently, we examine the impact of an imperfect network environment on the attacker’s rewards within the SIM framework, focusing on the influence of unintentional fork rates on rewards. Our quantitative analysis demonstrates that the attacker’s rewards in SIM exceed those in power-adjusting withholding (PAW) by $1.9times $ and $2.7times $ in different network environments, respectively. The attacker’s rewards threshold reduced to 12.38% compared to other benchmarks.
{"title":"SIM: Achieving High Profit Through Integration of Selfish Strategy Into Innocent Mining","authors":"Jiaze Shang;Tianbo Lu;Pengfei Zhao","doi":"10.1109/TNSM.2024.3435869","DOIUrl":"10.1109/TNSM.2024.3435869","url":null,"abstract":"Selfish mining, one of the most renowned attack in Bitcoin, involves a selfish miner withholding discovered blocks and broadcasting them at an opportune moment to gain higher rewards than honest mining. However, selfish mining and its variants rely on two assumptions: the attacker solely engages in infiltration mining within the victim pool (attack assumption) and the system operates in a perfect network environment (network assumption). In this paper, we propose a novel attack called Selfish in Innocent Mining (SIM). SIM expands the range of attacker’s behaviors by incorporating selfish mining into the traditional framework of innocent and infiltration mining, without increasing the attacker’s computational power. Initially, we analyze all possible states of chains in the system and their transition probabilities in the context of the SIM attack using Markov Chain. We determine the attacker’s rewards in one victim pool, multiple victim pools, and the miner’s dilemma within different cases. Subsequently, we examine the impact of an imperfect network environment on the attacker’s rewards within the SIM framework, focusing on the influence of unintentional fork rates on rewards. Our quantitative analysis demonstrates that the attacker’s rewards in SIM exceed those in power-adjusting withholding (PAW) by \u0000<inline-formula> <tex-math>$1.9times $ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$2.7times $ </tex-math></inline-formula>\u0000 in different network environments, respectively. The attacker’s rewards threshold reduced to 12.38% compared to other benchmarks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6153-6173"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870315","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.2024.3435505
Jorge Pueyo;Daniel Camps-Mur;Miguel Catalan-Cid
3GPP Integrated Access and Backhaul (IAB) allows operators to deploy outdoor mm-wave access networks in a cost-efficient manner, by reusing the same spectrum in access and backhaul. In IAB networks the performance bottleneck is the wireless backhaul segment, where efficient forwarding strategies are needed to effectively use the available capacity. In addition, the performance of the mm-wave IAB backhaul segment is contingent on the availability of line of sight (LoS) conditions in the selected deployment sites. To mitigate LoS dependence, in this paper, we propose to complement the mm-wave backhaul segment of IAB networks with additional Sub6 backhaul links, which contribute to the capacity and robustness of the backhaul network. We refer to IAB networks combining Sub6 and mm-wave links in the backhaul as Sub6 enhanced IAB networks. In this context, the main contribution of this paper is PHaul, a forwarding engine for Sub6 enhanced IAB networks that accomodates different traffic engineering criteria, and combines an offline path selection heuristic with an online Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO). By leveraging a network digital twin of the IAB wireless backhaul, PHaul periodically samples the input traffic of the backhaul network and updates flow to path mappings, with execution times below 10 seconds in realistic backhaul topologies. We present an exhaustive performance evaluation, where we demonstrate that PHaul can achieve gains of up to 36% in throughput efficiency and of up to 20% in fairness, when compared against two alternative heuristics in a wide range of network configurations. We also demonstrate that PHaul is robust to differences between the network topologies considered in the training and inference phases, which can occur in practice due to link failures.
{"title":"PHaul: A PPO-Based Forwarding Agent for Sub6-Enhanced Integrated Access and Backhaul Networks","authors":"Jorge Pueyo;Daniel Camps-Mur;Miguel Catalan-Cid","doi":"10.1109/TNSM.2024.3435505","DOIUrl":"10.1109/TNSM.2024.3435505","url":null,"abstract":"3GPP Integrated Access and Backhaul (IAB) allows operators to deploy outdoor mm-wave access networks in a cost-efficient manner, by reusing the same spectrum in access and backhaul. In IAB networks the performance bottleneck is the wireless backhaul segment, where efficient forwarding strategies are needed to effectively use the available capacity. In addition, the performance of the mm-wave IAB backhaul segment is contingent on the availability of line of sight (LoS) conditions in the selected deployment sites. To mitigate LoS dependence, in this paper, we propose to complement the mm-wave backhaul segment of IAB networks with additional Sub6 backhaul links, which contribute to the capacity and robustness of the backhaul network. We refer to IAB networks combining Sub6 and mm-wave links in the backhaul as Sub6 enhanced IAB networks. In this context, the main contribution of this paper is PHaul, a forwarding engine for Sub6 enhanced IAB networks that accomodates different traffic engineering criteria, and combines an offline path selection heuristic with an online Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO). By leveraging a network digital twin of the IAB wireless backhaul, PHaul periodically samples the input traffic of the backhaul network and updates flow to path mappings, with execution times below 10 seconds in realistic backhaul topologies. We present an exhaustive performance evaluation, where we demonstrate that PHaul can achieve gains of up to 36% in throughput efficiency and of up to 20% in fairness, when compared against two alternative heuristics in a wide range of network configurations. We also demonstrate that PHaul is robust to differences between the network topologies considered in the training and inference phases, which can occur in practice due to link failures.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6273-6289"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870557","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 distribution of computing resources in the Computing Power Network (CPN) is uneven, leading to an imbalance in resource supply and demand within domains, necessitating cross-domain resource scheduling. To address the cross-domain resource scheduling challenge in CPN, this paper presents an Improved Multi-Agent Actor-Critic (IMAAC) resource scheduling approach leveraging Data Processing Unit (DPU) offloading. Initially, we introduce a cross-domain resource scheduling architecture tailored for CPN by leveraging DPU offloading. Specifically, we delegate certain functionalities of the Multi-Agent Deep Reinforcement Learning (MADRL) Agent to DPUs, aiming to mitigate communication costs incurred during the generation of cross-domain scheduling decisions. Second, we introduce the parallel experience ensemble and multi-head attention mechanism in the Multi-Agent Actor-Critic (MAAC) framework to compress the state-space dimensionality of agent association across domains. Finally, we introduce the parallelized dual-policy network structure to mitigate training instability and convergence challenges within the actor and critic networks. Experimental results showcase that IMAAC achieves noteworthy reductions of 5.98%~13.56%, 23.54%~33.55%, and 41.17%~58.88% in total system delay, energy consumption, and the number of discarded tasks, respectively, compared to benchmark experiments.
{"title":"DPU-Enhanced Multi-Agent Actor-Critic Algorithm for Cross-Domain Resource Scheduling in Computing Power Network","authors":"Shuaichao Wang;Shaoyong Guo;Jiakai Hao;Yinlin Ren;Feng Qi","doi":"10.1109/TNSM.2024.3434997","DOIUrl":"10.1109/TNSM.2024.3434997","url":null,"abstract":"The distribution of computing resources in the Computing Power Network (CPN) is uneven, leading to an imbalance in resource supply and demand within domains, necessitating cross-domain resource scheduling. To address the cross-domain resource scheduling challenge in CPN, this paper presents an Improved Multi-Agent Actor-Critic (IMAAC) resource scheduling approach leveraging Data Processing Unit (DPU) offloading. Initially, we introduce a cross-domain resource scheduling architecture tailored for CPN by leveraging DPU offloading. Specifically, we delegate certain functionalities of the Multi-Agent Deep Reinforcement Learning (MADRL) Agent to DPUs, aiming to mitigate communication costs incurred during the generation of cross-domain scheduling decisions. Second, we introduce the parallel experience ensemble and multi-head attention mechanism in the Multi-Agent Actor-Critic (MAAC) framework to compress the state-space dimensionality of agent association across domains. Finally, we introduce the parallelized dual-policy network structure to mitigate training instability and convergence challenges within the actor and critic networks. Experimental results showcase that IMAAC achieves noteworthy reductions of 5.98%~13.56%, 23.54%~33.55%, and 41.17%~58.88% in total system delay, energy consumption, and the number of discarded tasks, respectively, compared to benchmark experiments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6008-6025"},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870559","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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