The rapid proliferation of Unmanned Aircraft Systems (UAS) introduces new threats to national security. UAS technologies have dramatically revolutionized legitimate business operations while providing powerful weaponizing systems to malicious actors and criminals. Due to their inherited wireless capabilities, they are an easy target for cyber threats. In response to this challenge, the implementation of many Intrusion Detection Systems (IDS), which support anomaly detection on UAS, have been proposed in the past. However, such systems often require offline training with heavy processing, making them unsuitable for UAS deployment. This is pertinent for drone systems that support dynamic changes in mission operational tasks. This paper presents a novel system architecture that utilizes sensing systems capabilities available on existing IoT infrastructure for supporting rapid infield adaptive models’ training and parameters estimation services for UAS. We have devised a cluster-oriented distributed training algorithm based on LSTM with mini-batch gradient descent, with hundreds of IoT platforms per cluster collaboratively performing model parameters estimation tasks. The proposed architecture is based on deploying a multilayer system that facilitates secure dissemination of power consumption behavioral patterns for the flight sensing system between the UAS layer and the IoT layer. The model was implemented and deployed on a real IoT-enabled platform based on NXP-Kinetis K64–120 MHz. Furthermore, model training and validation were performed by applying various datasets contaminated with different percentages of malicious data. Our anomaly detection model achieved high prediction accuracy with an ROC-AUC score of 0.9332. The model maintains minimal power consumption overheads and low training time during the processing of a data batch.
{"title":"IoTDL2AIDS: Toward IoT-Based System Architecture Supporting Distributed LSTM Learning for Adaptive IDS on UAS","authors":"Amar Rasheed;Mohamed Baza;Gautam Srivastava;Narashimha Karpoor;Cihan Varol","doi":"10.1109/TNSM.2024.3448312","DOIUrl":"10.1109/TNSM.2024.3448312","url":null,"abstract":"The rapid proliferation of Unmanned Aircraft Systems (UAS) introduces new threats to national security. UAS technologies have dramatically revolutionized legitimate business operations while providing powerful weaponizing systems to malicious actors and criminals. Due to their inherited wireless capabilities, they are an easy target for cyber threats. In response to this challenge, the implementation of many Intrusion Detection Systems (IDS), which support anomaly detection on UAS, have been proposed in the past. However, such systems often require offline training with heavy processing, making them unsuitable for UAS deployment. This is pertinent for drone systems that support dynamic changes in mission operational tasks. This paper presents a novel system architecture that utilizes sensing systems capabilities available on existing IoT infrastructure for supporting rapid infield adaptive models’ training and parameters estimation services for UAS. We have devised a cluster-oriented distributed training algorithm based on LSTM with mini-batch gradient descent, with hundreds of IoT platforms per cluster collaboratively performing model parameters estimation tasks. The proposed architecture is based on deploying a multilayer system that facilitates secure dissemination of power consumption behavioral patterns for the flight sensing system between the UAS layer and the IoT layer. The model was implemented and deployed on a real IoT-enabled platform based on NXP-Kinetis K64–120 MHz. Furthermore, model training and validation were performed by applying various datasets contaminated with different percentages of malicious data. Our anomaly detection model achieved high prediction accuracy with an ROC-AUC score of 0.9332. The model maintains minimal power consumption overheads and low training time during the processing of a data batch.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6059-6081"},"PeriodicalIF":4.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187219","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}
{"title":"Packet Loss in Real-Time Communications: Can ML Tame its Unpredictable Nature?","authors":"Tailai Song, Gianluca Perna, Paolo Garza, Michela Meo, Maurizio Matteo Munafò","doi":"10.1109/tnsm.2024.3442616","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3442616","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"13 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187222","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 emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we concern about the privacy protection in the MEC system with a curious edge server. We present a deep reinforcement learning (DRL)-driven computation offloading strategy designed to concurrently optimize privacy protection and computation cost. We investigate the potential privacy breaches resulting from offloading patterns, propose an attack model of privacy theft, and correspondingly define an analytical measure to assess privacy protection levels. In pursuit of an ideal computation offloading approach, we propose an algorithm, SAC-PP, which integrates actor-critic, off-policy, and maximum entropy to improve the efficiency of learning processes. We explore the sensitivity of SAC-PP to hyperparameters and the results demonstrate its stability, which facilitates application and deployment in real environments. The relationship between privacy protection and computation cost is analyzed with different reward factors. Compared with benchmarks, the empirical results from simulations illustrate that the proposed computation offloading approach exhibits enhanced learning speed and overall performance.
{"title":"SAC-PP: Jointly Optimizing Privacy Protection and Computation Offloading for Mobile Edge Computing","authors":"Shigen Shen;Xuanbin Hao;Zhengjun Gao;Guowen Wu;Yizhou Shen;Hong Zhang;Qiying Cao;Shui Yu","doi":"10.1109/TNSM.2024.3447753","DOIUrl":"10.1109/TNSM.2024.3447753","url":null,"abstract":"The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we concern about the privacy protection in the MEC system with a curious edge server. We present a deep reinforcement learning (DRL)-driven computation offloading strategy designed to concurrently optimize privacy protection and computation cost. We investigate the potential privacy breaches resulting from offloading patterns, propose an attack model of privacy theft, and correspondingly define an analytical measure to assess privacy protection levels. In pursuit of an ideal computation offloading approach, we propose an algorithm, SAC-PP, which integrates actor-critic, off-policy, and maximum entropy to improve the efficiency of learning processes. We explore the sensitivity of SAC-PP to hyperparameters and the results demonstrate its stability, which facilitates application and deployment in real environments. The relationship between privacy protection and computation cost is analyzed with different reward factors. Compared with benchmarks, the empirical results from simulations illustrate that the proposed computation offloading approach exhibits enhanced learning speed and overall performance.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6190-6203"},"PeriodicalIF":4.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187218","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 : 2024-08-21DOI: 10.1109/TNSM.2024.3447532
Mahsa Raeiszadeh;Amin Ebrahimzadeh;Roch H. Glitho;Johan Eker;Raquel A. F. Mini
To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today’s Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.
{"title":"Real-Time Adaptive Anomaly Detection in Industrial IoT Environments","authors":"Mahsa Raeiszadeh;Amin Ebrahimzadeh;Roch H. Glitho;Johan Eker;Raquel A. F. Mini","doi":"10.1109/TNSM.2024.3447532","DOIUrl":"10.1109/TNSM.2024.3447532","url":null,"abstract":"To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today’s Industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6839-6856"},"PeriodicalIF":4.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187223","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 : 2024-08-20DOI: 10.1109/TNSM.2024.3445972
Kumar Prateek;Soumyadev Maity;Neetesh Saxena
Energy Internet is well-known nowadays for enabling bidirectional V2G communication; however, with communication and computation abilities, V2G systems become vulnerable to cyber-attacks and unauthorised access. An authentication protocol verifies the identity of an entity, establishes trust, and allows access to authorized resources while preventing unauthorized access. Research challenges for vehicle-to-grid authentication protocols include quantum security, privacy, resilience to attacks, and interoperability. The majority of authentication protocols in V2G systems are based on public-key cryptography and depend on some hard problems like integer factorization and discrete logs to guarantee security, which can be easily broken by a quantum adversary. Besides, ensuring both information security and entity privacy is equally crucial in V2G scenarios. Consequently, this work proposes a quantum-secured privacy-preserving key authentication and communication (QSKA) protocol using superdense coding and a hash function for unconditionally secure V2G communication and privacy. QSKA uses a password-based authentication mechanism, enabling V2G entities to securely transfer passwords using superdense coding. The QSKA security verification is performed in proof-assistant Coq. The security analysis and performance evaluation of the QSKA show its resiliency against well-known security attacks and reveal its enhanced reliability and efficiency with respect to state-of-the-art protocols in terms of computation, communication, and energy overhead.
{"title":"QSKA: A Quantum Secured Privacy-Preserving Mutual Authentication Scheme for Energy Internet-Based Vehicle-to-Grid Communication","authors":"Kumar Prateek;Soumyadev Maity;Neetesh Saxena","doi":"10.1109/TNSM.2024.3445972","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3445972","url":null,"abstract":"Energy Internet is well-known nowadays for enabling bidirectional V2G communication; however, with communication and computation abilities, V2G systems become vulnerable to cyber-attacks and unauthorised access. An authentication protocol verifies the identity of an entity, establishes trust, and allows access to authorized resources while preventing unauthorized access. Research challenges for vehicle-to-grid authentication protocols include quantum security, privacy, resilience to attacks, and interoperability. The majority of authentication protocols in V2G systems are based on public-key cryptography and depend on some hard problems like integer factorization and discrete logs to guarantee security, which can be easily broken by a quantum adversary. Besides, ensuring both information security and entity privacy is equally crucial in V2G scenarios. Consequently, this work proposes a quantum-secured privacy-preserving key authentication and communication (QSKA) protocol using superdense coding and a hash function for unconditionally secure V2G communication and privacy. QSKA uses a password-based authentication mechanism, enabling V2G entities to securely transfer passwords using superdense coding. The QSKA security verification is performed in proof-assistant Coq. The security analysis and performance evaluation of the QSKA show its resiliency against well-known security attacks and reveal its enhanced reliability and efficiency with respect to state-of-the-art protocols in terms of computation, communication, and energy overhead.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6810-6826"},"PeriodicalIF":4.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859253","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}
SDN has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table’s storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. When suspicious activity is identified, FloRa promptly activates the machine-learning based detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49% which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.
{"title":"FloRa: Flow Table Low-Rate Overflow Reconnaissance and Detection in SDN","authors":"Ankur Mudgal;Abhishek Verma;Munesh Singh;Kshira Sagar Sahoo;Erik Elmroth;Monowar Bhuyan","doi":"10.1109/TNSM.2024.3446178","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3446178","url":null,"abstract":"SDN has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table’s storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. When suspicious activity is identified, FloRa promptly activates the machine-learning based detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49% which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6670-6683"},"PeriodicalIF":4.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858919","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 : 2024-08-19DOI: 10.1109/tnsm.2024.3444275
Yong Xiao, Jianxun Liu, Guosheng Kang, Buqing Cao
{"title":"Group Feature Aggregation for Web Service Recommendations","authors":"Yong Xiao, Jianxun Liu, Guosheng Kang, Buqing Cao","doi":"10.1109/tnsm.2024.3444275","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3444275","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"27 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187225","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 : 2024-08-19DOI: 10.1109/TNSM.2024.3445369
Xin Wang;Jianhui Lv;Adam Slowik;B. D. Parameshachari;Keqin Li;Chien-Ming Chen;Saru Kumari
The exponential growth of mobile data traffic in next generation networks has led to a significant increase in energy consumption, posing critical challenges for network operators. We propose DLLF-2EN, a novel energy-efficient framework that integrates deep learning-based load forecasting, an advanced power consumption model, and a comprehensive energy-saving strategy to address this issue. The load forecasting technique utilizes deep convolutional neural network and long short-term memory model, which is based on deep learning. This model is capable of capturing the spatiotemporal dependencies present in network traffic data. The power consumption model accurately characterizes the base stations’ static and dynamic power consumption components, facilitating the assessment of energy efficiency under various network scenarios. The energy-saving strategy combines base station sleep mode with discontinuous transmission and reception, as well as lightweight transmission of common signals, dynamically adapting the network operation based on the predicted traffic load. Furthermore, DLLF-2EN incorporates an intelligent power management system that leverages machine learning algorithms to continuously monitor the network, analyze collected data, and make optimal energy-saving decisions in real-time. Simulation demonstrate that the superior performance of DLLF-2EN in terms of load forecasting accuracy and energy efficiency compared to state-of-the-art baseline methods. The proposed framework represents a comprehensive solution for energy-efficient and sustainable next generation mobile networks, addressing the critical challenges of minimizing energy consumption while meeting the growing demands for high-quality mobile services.
{"title":"DLLF-2EN: Energy-Efficient Next Generation Mobile Network With Deep Learning-Based Load Forecasting","authors":"Xin Wang;Jianhui Lv;Adam Slowik;B. D. Parameshachari;Keqin Li;Chien-Ming Chen;Saru Kumari","doi":"10.1109/TNSM.2024.3445369","DOIUrl":"10.1109/TNSM.2024.3445369","url":null,"abstract":"The exponential growth of mobile data traffic in next generation networks has led to a significant increase in energy consumption, posing critical challenges for network operators. We propose DLLF-2EN, a novel energy-efficient framework that integrates deep learning-based load forecasting, an advanced power consumption model, and a comprehensive energy-saving strategy to address this issue. The load forecasting technique utilizes deep convolutional neural network and long short-term memory model, which is based on deep learning. This model is capable of capturing the spatiotemporal dependencies present in network traffic data. The power consumption model accurately characterizes the base stations’ static and dynamic power consumption components, facilitating the assessment of energy efficiency under various network scenarios. The energy-saving strategy combines base station sleep mode with discontinuous transmission and reception, as well as lightweight transmission of common signals, dynamically adapting the network operation based on the predicted traffic load. Furthermore, DLLF-2EN incorporates an intelligent power management system that leverages machine learning algorithms to continuously monitor the network, analyze collected data, and make optimal energy-saving decisions in real-time. Simulation demonstrate that the superior performance of DLLF-2EN in terms of load forecasting accuracy and energy efficiency compared to state-of-the-art baseline methods. The proposed framework represents a comprehensive solution for energy-efficient and sustainable next generation mobile networks, addressing the critical challenges of minimizing energy consumption while meeting the growing demands for high-quality mobile services.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6515-6526"},"PeriodicalIF":4.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187226","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}
Edge computing technologies with container-based microservice architectures promise to provide stable and low-latency services for large-scale and complex edge applications. However, due to the limited CPU and storage resources in edge computing scenarios, the coarse-grained service deployment on edge nodes causes performance bottlenecks. In addition, the effective deployment of microservices is tightly correlated with request routing, but the current research ignores the joint optimization of multi-instance deployment and routing. In this paper, we first model the problem of jointly optimizing service deployment and routing in a dynamically changing environment with multi-edge network collaboration based on a queuing network analysis. Secondly, we design heuristic algorithms to scale microservice instances horizontally in dynamic user request states. In addition, we propose a reinforcement learning algorithm based on reward shaping (RSPPO) to minimize user waiting delay and edge network resource consumption. We also solve the microservice deployment and request routing problem for multi-edge collaboration to achieve load balancing among edge nodes. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm.
{"title":"Joint Optimization of Microservice Deployment and Routing in Edge via Multi-Objective Deep Reinforcement Learning","authors":"Menglan Hu;Hao Wang;Xiaohui Xu;Jianwen He;Yi Hu;Tianping Deng;Kai Peng","doi":"10.1109/TNSM.2024.3443872","DOIUrl":"10.1109/TNSM.2024.3443872","url":null,"abstract":"Edge computing technologies with container-based microservice architectures promise to provide stable and low-latency services for large-scale and complex edge applications. However, due to the limited CPU and storage resources in edge computing scenarios, the coarse-grained service deployment on edge nodes causes performance bottlenecks. In addition, the effective deployment of microservices is tightly correlated with request routing, but the current research ignores the joint optimization of multi-instance deployment and routing. In this paper, we first model the problem of jointly optimizing service deployment and routing in a dynamically changing environment with multi-edge network collaboration based on a queuing network analysis. Secondly, we design heuristic algorithms to scale microservice instances horizontally in dynamic user request states. In addition, we propose a reinforcement learning algorithm based on reward shaping (RSPPO) to minimize user waiting delay and edge network resource consumption. We also solve the microservice deployment and request routing problem for multi-edge collaboration to achieve load balancing among edge nodes. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6364-6381"},"PeriodicalIF":4.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187224","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}
By using the intelligent edge computing technologies, a large number of computing tasks of end devices in Industrial Internet of Things (IIoT) can be offloaded to edge servers, which can effectively alleviate the burden and enhance the performance of IIoT. However, in large-scale multi-service-oriented IIoT scenarios, offloading service resources are heterogeneous and offloading requirements are mutually exclusive and time-varying, which reduce the offloading efficiency. In this paper, we propose a cloud-edge-end collaboration intelligent service computation offloading scheme based on Digital Twin (DT) driven Edge Coalition Formation (DECF) approach to improve the offloading efficiency and the total utility of edge servers, respectively. Firstly, we establish a DT model to obtain accurate digital representations of heterogeneous end devices and network state parameters in dynamic and complex IIoT scenarios. The DT model can capture time-varying requirements in a low latency manner. Secondly, we formulate two optimization problems to maximize the offloading throughput and total system utility. Finally, we convert the multi-objective optimization problems to a Stackelberg coalition game model and develop a distributed coalition formation approach to balance the two optimizing objectives. Simulation results indicate that, compared with the nearest coalition scheme and non-coalition scheme, the proposed approach achieves offloading throughput improvements of 11.5% and 148%, and enhances the overall utility by 12% and 170%, respectively.
{"title":"Cloud-Edge-End Collaborative Intelligent Service Computation Offloading: A Digital Twin Driven Edge Coalition Approach for Industrial IoT","authors":"Xiaohuan Li;Bitao Chen;Junchuan Fan;Jiawen Kang;Jin Ye;Xun Wang;Dusit Niyato","doi":"10.1109/TNSM.2024.3441231","DOIUrl":"10.1109/TNSM.2024.3441231","url":null,"abstract":"By using the intelligent edge computing technologies, a large number of computing tasks of end devices in Industrial Internet of Things (IIoT) can be offloaded to edge servers, which can effectively alleviate the burden and enhance the performance of IIoT. However, in large-scale multi-service-oriented IIoT scenarios, offloading service resources are heterogeneous and offloading requirements are mutually exclusive and time-varying, which reduce the offloading efficiency. In this paper, we propose a cloud-edge-end collaboration intelligent service computation offloading scheme based on Digital Twin (DT) driven Edge Coalition Formation (DECF) approach to improve the offloading efficiency and the total utility of edge servers, respectively. Firstly, we establish a DT model to obtain accurate digital representations of heterogeneous end devices and network state parameters in dynamic and complex IIoT scenarios. The DT model can capture time-varying requirements in a low latency manner. Secondly, we formulate two optimization problems to maximize the offloading throughput and total system utility. Finally, we convert the multi-objective optimization problems to a Stackelberg coalition game model and develop a distributed coalition formation approach to balance the two optimizing objectives. Simulation results indicate that, compared with the nearest coalition scheme and non-coalition scheme, the proposed approach achieves offloading throughput improvements of 11.5% and 148%, and enhances the overall utility by 12% and 170%, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6318-6330"},"PeriodicalIF":4.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10639522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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