Pub Date : 2023-06-16DOI: 10.1109/LNET.2023.3286933
Zhanwei Yu;Yi Zhao;Tao Deng;Lei You;Di Yuan
We address reducing carbon footprint (CF) in the context of edge computing. The carbon intensity of electricity supply largely varies spatially as well as temporally. We consider optimal task scheduling and offloading, as well as battery charging to minimize the total CF. We formulate this optimization problem as a mixed integer linear programming model. However, we demonstrate that, via a graph-based reformulation, the problem can be cast as a minimum-cost flow problem, and global optimum can be admitted in polynomial time. Numerical results using real-world data show that optimization can reduce up to 83.3% of the total CF.
{"title":"Less Carbon Footprint in Edge Computing by Joint Task Offloading and Energy Sharing","authors":"Zhanwei Yu;Yi Zhao;Tao Deng;Lei You;Di Yuan","doi":"10.1109/LNET.2023.3286933","DOIUrl":"https://doi.org/10.1109/LNET.2023.3286933","url":null,"abstract":"We address reducing carbon footprint (CF) in the context of edge computing. The carbon intensity of electricity supply largely varies spatially as well as temporally. We consider optimal task scheduling and offloading, as well as battery charging to minimize the total CF. We formulate this optimization problem as a mixed integer linear programming model. However, we demonstrate that, via a graph-based reformulation, the problem can be cast as a minimum-cost flow problem, and global optimum can be admitted in polynomial time. Numerical results using real-world data show that optimization can reduce up to 83.3% of the total CF.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"245-249"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10154013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1109/LNET.2023.3286443
Leon Fernandez;Gunnar Karlsson
Service providers are adopting open-source technology and open standards in their next-generation networks. This gives them great flexibility and spurs innovation. But it also means that they must ensure proper interoperability between components; otherwise, vulnerabilities might get introduced in their networks. Unfortunately, state-of-the-art vulnerability scanning tools are unable to handle the complexity of service provider networks. In this letter we show how interoperability issues between seemingly reliable components introduce an injection vulnerability that allows us to control a firewall-protected network management system. We also extend the state-of-the-art in black-box fuzzing to give service providers a tool for combating similar issues.
{"title":"Black-Box Fuzzing for Security in Managed Networks: An Outline","authors":"Leon Fernandez;Gunnar Karlsson","doi":"10.1109/LNET.2023.3286443","DOIUrl":"10.1109/LNET.2023.3286443","url":null,"abstract":"Service providers are adopting open-source technology and open standards in their next-generation networks. This gives them great flexibility and spurs innovation. But it also means that they must ensure proper interoperability between components; otherwise, vulnerabilities might get introduced in their networks. Unfortunately, state-of-the-art vulnerability scanning tools are unable to handle the complexity of service provider networks. In this letter we show how interoperability issues between seemingly reliable components introduce an injection vulnerability that allows us to control a firewall-protected network management system. We also extend the state-of-the-art in black-box fuzzing to give service providers a tool for combating similar issues.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"241-244"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90752375","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 : 2023-06-15DOI: 10.1109/LNET.2023.3286581
Olga Chukhno;Olga Galinina;Sergey Andreev;Antonella Molinaro;Antonio Iera
This letter presents a practice-inspired methodology for characterizing the user and content dynamics of extended reality (XR) services over wireless networks. The proposed approach is based on a fluid approximation to capture the time and space dynamics of XR content exchange during its transient phase while considering both radio communication and edge computing resources. Hence, our methodology provides an effective tool to support resource assignment for radio and computing in 5G and beyond networks, especially under non-stationary processes with time-varying traffic arrivals, such as those with a periodic arrival rate function.
{"title":"User and Content Dynamics of Edge-Aided Immersive Reality Services","authors":"Olga Chukhno;Olga Galinina;Sergey Andreev;Antonella Molinaro;Antonio Iera","doi":"10.1109/LNET.2023.3286581","DOIUrl":"10.1109/LNET.2023.3286581","url":null,"abstract":"This letter presents a practice-inspired methodology for characterizing the user and content dynamics of extended reality (XR) services over wireless networks. The proposed approach is based on a fluid approximation to capture the time and space dynamics of XR content exchange during its transient phase while considering both radio communication and edge computing resources. Hence, our methodology provides an effective tool to support resource assignment for radio and computing in 5G and beyond networks, especially under non-stationary processes with time-varying traffic arrivals, such as those with a periodic arrival rate function.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"227-231"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10153602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84087656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-14DOI: 10.1109/LNET.2023.3286104
Dimitrios Michael Manias;Ali Chouman;Joe Naoum-Sawaya;Abdallah Shami
In the realm of network management and orchestration, such as Virtual Network Function (VNF) lifecycle management, the dynamicity of 5G networks raises the importance of reliability and robustness when determining optimal VNF placement. Specifically, after a fault has occurred, the set of services that must maintain a certain level of performance and quality depends on the interaction between VNFs. This letter proposes a novel robust optimization model for VNF placement during post-fault status, while addressing the resilience and reliability of the 5G network in testing. The model results are compared with a deterministic placement solution with varying demand uncertainties.
{"title":"Resilient and Robust QoS-Preserving Post-Fault VNF Placement","authors":"Dimitrios Michael Manias;Ali Chouman;Joe Naoum-Sawaya;Abdallah Shami","doi":"10.1109/LNET.2023.3286104","DOIUrl":"10.1109/LNET.2023.3286104","url":null,"abstract":"In the realm of network management and orchestration, such as Virtual Network Function (VNF) lifecycle management, the dynamicity of 5G networks raises the importance of reliability and robustness when determining optimal VNF placement. Specifically, after a fault has occurred, the set of services that must maintain a certain level of performance and quality depends on the interaction between VNFs. This letter proposes a novel robust optimization model for VNF placement during post-fault status, while addressing the resilience and reliability of the 5G network in testing. The model results are compared with a deterministic placement solution with varying demand uncertainties.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"270-274"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76955116","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 : 2023-06-12DOI: 10.1109/LNET.2023.3285295
Salwa Mostafa;Mohamed K. Abdel-Aziz;Mehdi Bennis
We consider a cooperative-navigation problem in a partially observable MADRL framework. We investigate how agents cooperate to learn a communication protocol given a very large state space while generalizing to a new environment. The proposed solution leverages the notion of structured observation and abstraction, in which the raw-pixel observations are converted into a relational graph that is then used for learning abstraction. Abstraction is performed based on compression using a relational graph autoencoder (RGAE) and a multilayer perceptron (MLP) to remove irrelevant information. The results show the effectiveness of the proposed MLP and RGAE in learning better policies with better generalization capabilities. It is also shown that communication among agents is instrumental in improving the navigation task performance.
{"title":"Cooperative Navigation via Relational Graphs and State Abstraction","authors":"Salwa Mostafa;Mohamed K. Abdel-Aziz;Mehdi Bennis","doi":"10.1109/LNET.2023.3285295","DOIUrl":"10.1109/LNET.2023.3285295","url":null,"abstract":"We consider a cooperative-navigation problem in a partially observable MADRL framework. We investigate how agents cooperate to learn a communication protocol given a very large state space while generalizing to a new environment. The proposed solution leverages the notion of structured observation and abstraction, in which the raw-pixel observations are converted into a relational graph that is then used for learning abstraction. Abstraction is performed based on compression using a relational graph autoencoder (RGAE) and a multilayer perceptron (MLP) to remove irrelevant information. The results show the effectiveness of the proposed MLP and RGAE in learning better policies with better generalization capabilities. It is also shown that communication among agents is instrumental in improving the navigation task performance.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"184-188"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74494199","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 : 2023-06-09DOI: 10.1109/LNET.2023.3284665
Yi Shi;Maice Costa;Tugba Erpek;Yalin E. Sagduyu
Reinforcement learning (RL) is applied for dynamic admission control and resource allocation in NextG radio access network slicing. When sharing the spectrum with an incumbent user (that dynamically occupies frequency-time blocks), communication and computational resources are allocated to slicing requests, each with priority (weight), throughput, latency, and computational requirements. RL maximizes the total weight of granted requests over time beyond myopic, greedy, random, and first come, first served solutions. As the state-action space grows, Deep Q-network effectively admits requests and allocates resources as a low-complexity solution that is robust to sensing errors in detecting the incumbent user activity.
{"title":"Deep Reinforcement Learning for NextG Radio Access Network Slicing With Spectrum Coexistence","authors":"Yi Shi;Maice Costa;Tugba Erpek;Yalin E. Sagduyu","doi":"10.1109/LNET.2023.3284665","DOIUrl":"https://doi.org/10.1109/LNET.2023.3284665","url":null,"abstract":"Reinforcement learning (RL) is applied for dynamic admission control and resource allocation in NextG radio access network slicing. When sharing the spectrum with an incumbent user (that dynamically occupies frequency-time blocks), communication and computational resources are allocated to slicing requests, each with priority (weight), throughput, latency, and computational requirements. RL maximizes the total weight of granted requests over time beyond myopic, greedy, random, and first come, first served solutions. As the state-action space grows, Deep Q-network effectively admits requests and allocates resources as a low-complexity solution that is robust to sensing errors in detecting the incumbent user activity.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 3","pages":"149-153"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49979548","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 : 2023-06-07DOI: 10.1109/LNET.2023.3283936
Turgay Pamuklu;Aisha Syed;W. Sean Kennedy;Melike Erol-Kantarci
Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this letter, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV.
{"title":"Heterogeneous GNN-RL-Based Task Offloading for UAV-Aided Smart Agriculture","authors":"Turgay Pamuklu;Aisha Syed;W. Sean Kennedy;Melike Erol-Kantarci","doi":"10.1109/LNET.2023.3283936","DOIUrl":"10.1109/LNET.2023.3283936","url":null,"abstract":"Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this letter, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"213-217"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79710523","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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