Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9913008
Marisangila Alves, Guilherme Piĉgas Koslovski
The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.
{"title":"Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks","authors":"Marisangila Alves, Guilherme Piĉgas Koslovski","doi":"10.1109/ISCC55528.2022.9913008","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913008","url":null,"abstract":"The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134495543","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912808
Argyro Mavrogiorgou, S. Kleftakis, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis
The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.
{"title":"A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare","authors":"Argyro Mavrogiorgou, S. Kleftakis, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis","doi":"10.1109/ISCC55528.2022.9912808","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912808","url":null,"abstract":"The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130983674","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912903
Jianhua Wang, Xiaolin Chang, Ricardo J. Rodríguez, Yixiang Wang
Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.
{"title":"Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning","authors":"Jianhua Wang, Xiaolin Chang, Ricardo J. Rodríguez, Yixiang Wang","doi":"10.1109/ISCC55528.2022.9912903","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912903","url":null,"abstract":"Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134280470","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912956
Ryu Kazama, H. Abe, Chunghan Lee
Congestion control algorithms using recurrent neural network (RNN) for bandwidth prediction are expected to improve throughput. Previous studies involving performance evaluations were conducted only using simulated data. However, simulation and real-world environments are largely different and rarely provide equivalent prediction accuracy. Therefore, we will verify whether our proposed method provides better prediction accuracy in a real-world environment. We measured communications in a real environment and generated training data by converting packet captured data with measurement of prediction accuracy on the generated data. The results showed that the maximum percentage of correct responses was 79.71%, which was comparable to the results obtained using simulated data.
{"title":"Evaluating TCP Throughput Predictability from Packet Traces using Recurrent Neural Network","authors":"Ryu Kazama, H. Abe, Chunghan Lee","doi":"10.1109/ISCC55528.2022.9912956","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912956","url":null,"abstract":"Congestion control algorithms using recurrent neural network (RNN) for bandwidth prediction are expected to improve throughput. Previous studies involving performance evaluations were conducted only using simulated data. However, simulation and real-world environments are largely different and rarely provide equivalent prediction accuracy. Therefore, we will verify whether our proposed method provides better prediction accuracy in a real-world environment. We measured communications in a real environment and generated training data by converting packet captured data with measurement of prediction accuracy on the generated data. The results showed that the maximum percentage of correct responses was 79.71%, which was comparable to the results obtained using simulated data.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132621428","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912972
Thomas Dimakis, M. Louta, Thomas S. Kyriakidis, Alexandros-Apostolos A. Boulogeorgos, Konstantina Banti, Ioanna Karampelia, Nikos Papadimitriou
Long range wide area network (LoRaWAN) represents a promising low power wide area network (LPWAN) technology in the context of internet-of-things (IoT) that has recently attracted intense research interest. Due to the limited energy resources available on LoRaWAN constituent elements and intermittent power supply of gateways in harsh environments, an energy-efficient communication protocol is constituted of utmost importance in order to prolong network lifetime. Motivated by the aforementioned, this work presents a green, robust, and resilient communication protocol, namely GreenLoRaWAN, which increases energy efficiency, scalability and robustness of the LoRaWAN. The proposed protocol is evaluated by means of Monte Carlo simulations; Performance evaluation results acquired are very promising, revealing an important reduction in energy consumption and increase the duration of network lifetime.
{"title":"GreenLoRaWAN: An energy efficient and resilient LoRaWAN communication protocol","authors":"Thomas Dimakis, M. Louta, Thomas S. Kyriakidis, Alexandros-Apostolos A. Boulogeorgos, Konstantina Banti, Ioanna Karampelia, Nikos Papadimitriou","doi":"10.1109/ISCC55528.2022.9912972","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912972","url":null,"abstract":"Long range wide area network (LoRaWAN) represents a promising low power wide area network (LPWAN) technology in the context of internet-of-things (IoT) that has recently attracted intense research interest. Due to the limited energy resources available on LoRaWAN constituent elements and intermittent power supply of gateways in harsh environments, an energy-efficient communication protocol is constituted of utmost importance in order to prolong network lifetime. Motivated by the aforementioned, this work presents a green, robust, and resilient communication protocol, namely GreenLoRaWAN, which increases energy efficiency, scalability and robustness of the LoRaWAN. The proposed protocol is evaluated by means of Monte Carlo simulations; Performance evaluation results acquired are very promising, revealing an important reduction in energy consumption and increase the duration of network lifetime.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133092890","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}
Effective adversarial attacks simulation is essential for the deployment of ensemble Intrusion Detection Systems (en- semble IDSs) in Satellite-Terrestrial Integrated Network (STIN). This is because it can automatically generate a large amount of adversarial samples to evaluate the robustness of different classifiers. Based on the result, it can further guide the STIN engineers to select proper classifiers in ensemble IDSs. Moreover, it can help the IDSs improve detect performance by their self- learning property in the adversarial attack process. However, the existing adversarial attack approaches suffer from the problems of low success rate and high overhead of communication and calculation due to the limited computing resources and long communication links of STIN. This results in their inefficiency in STIN. To address the above problems, we provide StinAttack as a robustness evaluation scheme for STIN. First, StinAttack provides a comprehensive and automatic robustness evaluation framework for IDSs in STIN with only few times interactions between terrestrial and satellite nodes. Second, StinAttack proposes an effective adversarial attack simulation based on lightweight gradient evaluation for ensemble IDSs. Third, we conduct experiments on 11 typical IDSs, 4 baseline popular adversarial attacks and our StinAttack. Experimental results show that our approach can effectively attack ensemble IDSs and the evaluation results based on real STIN dataset are instructive for designing secure networks.
{"title":"StinAttack: A Lightweight and Effective Adversarial Attack Simulation to Ensemble IDSs for Satellite- Terrestrial Integrated Network","authors":"Shangyuan Zhuang, Jiyan Sun, Hangsheng Zhang, Xiaohui Kuang, Ling Pang, Haitao Liu, Yinlong Liu","doi":"10.1109/ISCC55528.2022.9912891","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912891","url":null,"abstract":"Effective adversarial attacks simulation is essential for the deployment of ensemble Intrusion Detection Systems (en- semble IDSs) in Satellite-Terrestrial Integrated Network (STIN). This is because it can automatically generate a large amount of adversarial samples to evaluate the robustness of different classifiers. Based on the result, it can further guide the STIN engineers to select proper classifiers in ensemble IDSs. Moreover, it can help the IDSs improve detect performance by their self- learning property in the adversarial attack process. However, the existing adversarial attack approaches suffer from the problems of low success rate and high overhead of communication and calculation due to the limited computing resources and long communication links of STIN. This results in their inefficiency in STIN. To address the above problems, we provide StinAttack as a robustness evaluation scheme for STIN. First, StinAttack provides a comprehensive and automatic robustness evaluation framework for IDSs in STIN with only few times interactions between terrestrial and satellite nodes. Second, StinAttack proposes an effective adversarial attack simulation based on lightweight gradient evaluation for ensemble IDSs. Third, we conduct experiments on 11 typical IDSs, 4 baseline popular adversarial attacks and our StinAttack. Experimental results show that our approach can effectively attack ensemble IDSs and the evaluation results based on real STIN dataset are instructive for designing secure networks.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132482834","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912953
Arpad Müller, Wisam Abbasi, A. Saracino
In this paper, we demonstrate an application of privacy-preserving face recognition combined with an Attribute-Based Access Control framework to regulate access from subjects to critical resources while preserving the subject's privacy. The demonstrator exploits a mechanism that dynamically computes the best trade-off between ensured privacy and data utility, based on image acquisition conditions, and a decision engine based on XACML policies to express complex and dynamic conditions. The demonstrator can handle the dynamic association of new identities, as well as modification of access conditions. Attendees of the demo session can interact with the demo in a variety of ways, including modifying the camera input, but also through the customization of rules as well as the privacy parameter.
{"title":"Demo: Usage Control using Controlled Privacy Aware Face Recognition","authors":"Arpad Müller, Wisam Abbasi, A. Saracino","doi":"10.1109/ISCC55528.2022.9912953","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912953","url":null,"abstract":"In this paper, we demonstrate an application of privacy-preserving face recognition combined with an Attribute-Based Access Control framework to regulate access from subjects to critical resources while preserving the subject's privacy. The demonstrator exploits a mechanism that dynamically computes the best trade-off between ensured privacy and data utility, based on image acquisition conditions, and a decision engine based on XACML policies to express complex and dynamic conditions. The demonstrator can handle the dynamic association of new identities, as well as modification of access conditions. Attendees of the demo session can interact with the demo in a variety of ways, including modifying the camera input, but also through the customization of rules as well as the privacy parameter.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020598","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912995
D. Nguyen, L. Ngan, Lai Huyen Thuong, Truong Thu Huong
With the ability to provide an “immersive experience”, 360-degree video-based applications are becoming more and more popular nowadays. In this paper, we propose LL-VAS, a novel adaptation method for low-latency 360-degree video streaming over mobile networks. By applying tile-based streaming, the proposed method allows 360-degree video streaming over resource-constrained mobile networks. In addition, by actively monitoring network throughput at the tile level, the proposed method can detect reductions in network throughput, and adapt video content in a timely manner to avoid re-buffering. Trace-driven experiments show that the proposed method can significantly decrease the number of re-buffering and re-buffering time under strong network throughput fluctuations and small buffer size when compared to reference methods.
{"title":"LL-VAS: Adaptation Method for Low-Latency 360-degree Video Streaming over Mobile Networks","authors":"D. Nguyen, L. Ngan, Lai Huyen Thuong, Truong Thu Huong","doi":"10.1109/ISCC55528.2022.9912995","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912995","url":null,"abstract":"With the ability to provide an “immersive experience”, 360-degree video-based applications are becoming more and more popular nowadays. In this paper, we propose LL-VAS, a novel adaptation method for low-latency 360-degree video streaming over mobile networks. By applying tile-based streaming, the proposed method allows 360-degree video streaming over resource-constrained mobile networks. In addition, by actively monitoring network throughput at the tile level, the proposed method can detect reductions in network throughput, and adapt video content in a timely manner to avoid re-buffering. Trace-driven experiments show that the proposed method can significantly decrease the number of re-buffering and re-buffering time under strong network throughput fluctuations and small buffer size when compared to reference methods.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130072239","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912842
Yaqiang Zhang, Rengang Li, Yaqian Zhao, Ruyang Li
Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.
{"title":"Deep Reinforcement Learning based Mobility-Aware Service Migration for Multi-access Edge Computing Environment","authors":"Yaqiang Zhang, Rengang Li, Yaqian Zhao, Ruyang Li","doi":"10.1109/ISCC55528.2022.9912842","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912842","url":null,"abstract":"Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134491463","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912774
F. Arpanaei, Shayan Hajipour, H. Beyranvand, J. A. Hernández, D. Larrabeiti
This paper proposes a shared precomputed restoration (SPR) mechanism for link failures in translucent elastic optical networks (EONs). We present SPR with a heuristic algorithm that aims to minimize a cost function. The cost function depends on the number of transceivers and frequency slots (FSs) used to establish a working/protection lightpath (LP) with a tunable parameter that determines the weight of the number of transceivers and FSs in the cost function. SPR precalculates a protection LP for all links of each working LP. As a result, non-link-disjoint working LPs can share protection spectrum and transceivers on newly eased conditions. Like shared backup path protection (SBPP) and dedicated protection (1+1), SPR guarantees single link failure recovery. Our simulation results reveal that SPR outperforms SBPP in terms of recovered bandwidth in multiple link failures. Furthermore, SPR uses fewer transceivers compared to SBPP and 1+1 protection.
{"title":"A Comparative Study on Shared Precomputed Restoration and Shared Backup Path Protection in EONs","authors":"F. Arpanaei, Shayan Hajipour, H. Beyranvand, J. A. Hernández, D. Larrabeiti","doi":"10.1109/ISCC55528.2022.9912774","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912774","url":null,"abstract":"This paper proposes a shared precomputed restoration (SPR) mechanism for link failures in translucent elastic optical networks (EONs). We present SPR with a heuristic algorithm that aims to minimize a cost function. The cost function depends on the number of transceivers and frequency slots (FSs) used to establish a working/protection lightpath (LP) with a tunable parameter that determines the weight of the number of transceivers and FSs in the cost function. SPR precalculates a protection LP for all links of each working LP. As a result, non-link-disjoint working LPs can share protection spectrum and transceivers on newly eased conditions. Like shared backup path protection (SBPP) and dedicated protection (1+1), SPR guarantees single link failure recovery. Our simulation results reveal that SPR outperforms SBPP in terms of recovered bandwidth in multiple link failures. Furthermore, SPR uses fewer transceivers compared to SBPP and 1+1 protection.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133896156","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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