Pub Date : 2022-10-31DOI: 10.23919/cnsm55787.2022.9964972
{"title":"CNSM 2022 Cover Page","authors":"","doi":"10.23919/cnsm55787.2022.9964972","DOIUrl":"https://doi.org/10.23919/cnsm55787.2022.9964972","url":null,"abstract":"","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121729555","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9965098
Alexandros Papadopoulos, Antonios Lalas, K. Votis, Dimitrios Tyrovolas, G. Karagiannidis, S. Ioannidis, C. Liaskos
Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.
{"title":"An Open Platform for Simulating the Physical Layer of 6G Communication Systems with Multiple Intelligent Surfaces","authors":"Alexandros Papadopoulos, Antonios Lalas, K. Votis, Dimitrios Tyrovolas, G. Karagiannidis, S. Ioannidis, C. Liaskos","doi":"10.23919/CNSM55787.2022.9965098","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965098","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105288","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964678
L. Simone, M. Mauro, M. Longo, R. Natella, F. Postiglione
We advance a performability assessment of a multi- tenant containerized IP Multimedia Subsystem (cIMS), i.e.: one and the same infrastructure is shared among different providers (or tenants). Specifically, we: i) model each cIMS node (a.k.a. Containerized Network Function - CNF) through the Multi-State System (MSS) formalism to capture the dimensionality of the multi-tenant arrangement, and characterize each tenant through queueing theory attributes to catch latency-dependent performance aspects; ii) afford an availability analysis of cIMS by means of an extended version of the Universal Generating Function (UGF) technique, dubbed Multidimensional UGF (MUGF); iii) solve an optimization problem to retrieve the cIMS deployment minimizing costs while guaranteeing high availability requirements. The whole assessment is supported by an experiment based on the containerized IMS platform Clearwater which we deploy to derive some realistic system parameters by means of fault injection techniques.
{"title":"Performability Assessment of Containerized Multi-Tenant IMS through Multidimensional UGF","authors":"L. Simone, M. Mauro, M. Longo, R. Natella, F. Postiglione","doi":"10.23919/CNSM55787.2022.9964678","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964678","url":null,"abstract":"We advance a performability assessment of a multi- tenant containerized IP Multimedia Subsystem (cIMS), i.e.: one and the same infrastructure is shared among different providers (or tenants). Specifically, we: i) model each cIMS node (a.k.a. Containerized Network Function - CNF) through the Multi-State System (MSS) formalism to capture the dimensionality of the multi-tenant arrangement, and characterize each tenant through queueing theory attributes to catch latency-dependent performance aspects; ii) afford an availability analysis of cIMS by means of an extended version of the Universal Generating Function (UGF) technique, dubbed Multidimensional UGF (MUGF); iii) solve an optimization problem to retrieve the cIMS deployment minimizing costs while guaranteeing high availability requirements. The whole assessment is supported by an experiment based on the containerized IMS platform Clearwater which we deploy to derive some realistic system parameters by means of fault injection techniques.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129675966","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9965187
Sukhyun Nam, Jae-Hyoung Yoo, J. W. Hong
In this study, we present a failure prediction study of VMs and VNFs in an NFV environment. For the proof of concept, we designed a machine learning model to predict the failure with log analysis and observed the cases where the failure-related logs do not exist in the failed VM, but in the server, or in other VMs operating on the same server. Therefore, in this paper, we propose a model which analyzes the logs of all the related VMs and the server and predicts the possibility that any of the VMs operating on the server will fail. To reduce the huge size of the logs collected from the server and VMs, we propose a pre-processing and tagging method that can improve the performance of our model. In addition, we designed a machine learning model using CNN with BERT, which has performed SOTA in various fields of NLP, to receive logs as input and calculate failure probabilities for the next 30 minutes. To validate the proposed model, we collected failure-related logs and normal logs from an OpenStack testbed, and the experimental result shows that the proposed model can predict the failure of VMs operating in the server with an F1 score of 0.74.
{"title":"VM Failure Prediction with Log Analysis using BERT-CNN Model","authors":"Sukhyun Nam, Jae-Hyoung Yoo, J. W. Hong","doi":"10.23919/CNSM55787.2022.9965187","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965187","url":null,"abstract":"In this study, we present a failure prediction study of VMs and VNFs in an NFV environment. For the proof of concept, we designed a machine learning model to predict the failure with log analysis and observed the cases where the failure-related logs do not exist in the failed VM, but in the server, or in other VMs operating on the same server. Therefore, in this paper, we propose a model which analyzes the logs of all the related VMs and the server and predicts the possibility that any of the VMs operating on the server will fail. To reduce the huge size of the logs collected from the server and VMs, we propose a pre-processing and tagging method that can improve the performance of our model. In addition, we designed a machine learning model using CNN with BERT, which has performed SOTA in various fields of NLP, to receive logs as input and calculate failure probabilities for the next 30 minutes. To validate the proposed model, we collected failure-related logs and normal logs from an OpenStack testbed, and the experimental result shows that the proposed model can predict the failure of VMs operating in the server with an F1 score of 0.74.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116185735","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964817
L. Bertholdo, Sandro L. A. Ferreira, J. Ceron, L. Granville, Ralph Holz, R. V. Rijswijk-Deij
Internet eXchange Points (IXPs) provide an infrastructure where content providers and consumers can freely exchange network traffic. The main incentive for connecting to an IXP is to decrease costs and improve the user experience by having content closer to consumers. Despite these benefits, several small Content Delivery Networks (CDNs) avoid exchanging traffic on IXPs due to the poor routing quality via IXP paths. In this paper, we investigate how traffic asymmetry affects the quality of paths. IXP asymmetry occurs when traffic is sent (or received) via a direct IXP peering but received (or sent) on an alternative path outside the IXP. We employ a new method to quantify a symmetry rate for an IXP, which we evaluate on five IXPs. Our method covers three times more ASes than alternatives, such as using RIPE ATLAS. Our results show that IXPs have 15% asymmetric paths at a distance of one AS hop, i.e., when sending traffic to a given peer on the IXP, 15% of this traffic will be responded via a transit AS that does not use the IXP path. We also identify deaf neighbors, i.e., ASes that never return traffic to the IXP. We identify egress-only paths as a major cause of asymmetries and show that this occurs only for a small number of ASes. We also quantify the impact of traffic asymmetry at IXPs in terms of latency and show that traditional traffic engineering on IXP prefixes can actually make route quality worse.
{"title":"On the Asymmetry of Internet eXchange Points -Why Should IXPs and CDNs Care?","authors":"L. Bertholdo, Sandro L. A. Ferreira, J. Ceron, L. Granville, Ralph Holz, R. V. Rijswijk-Deij","doi":"10.23919/CNSM55787.2022.9964817","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964817","url":null,"abstract":"Internet eXchange Points (IXPs) provide an infrastructure where content providers and consumers can freely exchange network traffic. The main incentive for connecting to an IXP is to decrease costs and improve the user experience by having content closer to consumers. Despite these benefits, several small Content Delivery Networks (CDNs) avoid exchanging traffic on IXPs due to the poor routing quality via IXP paths. In this paper, we investigate how traffic asymmetry affects the quality of paths. IXP asymmetry occurs when traffic is sent (or received) via a direct IXP peering but received (or sent) on an alternative path outside the IXP. We employ a new method to quantify a symmetry rate for an IXP, which we evaluate on five IXPs. Our method covers three times more ASes than alternatives, such as using RIPE ATLAS. Our results show that IXPs have 15% asymmetric paths at a distance of one AS hop, i.e., when sending traffic to a given peer on the IXP, 15% of this traffic will be responded via a transit AS that does not use the IXP path. We also identify deaf neighbors, i.e., ASes that never return traffic to the IXP. We identify egress-only paths as a major cause of asymmetries and show that this occurs only for a small number of ASes. We also quantify the impact of traffic asymmetry at IXPs in terms of latency and show that traditional traffic engineering on IXP prefixes can actually make route quality worse.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607073","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964591
Amina Hentati, Amin Ebrahimzadeh, R. Glitho, F. Belqasmi, R. Mizouni
Remote robotic surgery is one of the most interesting Tactile Internet (TI) applications. It has a huge potential to deliver healthcare services to remote locations. Moreover, it provides better precision and accuracy to diagnose and operate on patients. Remote robotic surgery requires ultra-low latency and ultra-high reliability. The aforementioned stringent requirements do not apply for all the multimodal data traffic (i.e., audio, video, and haptic) triggered during a surgery session. Hence, customizing resource allocation policies according to the different quality-of-service (QoS) requirements is crucial in order to achieve a cost-effective deployment of such system. In this paper, we focus on resource allocation in a softwarized 5G-enabled TI remote robotic surgery system through the use of Network Functions Virtualization (NFV). Specifically, this work is devoted to the joint placement and scheduling of application components in an NFV-based remote robotic surgery system, while considering haptic and video data. The problem is formulated as an integer linear program (ILP). Due to its complexity, we propose a greedy algorithm to solve the developed ILP in a computationally efficient manner. The simulation results show that our proposed algorithm is close to optimal and outperforms the benchmark solutions in terms of cost and admission rate. Furthermore, our results demonstrate that splitting application traffic to multiple VNF-forwarding graphs (VNF-FGs) with different QoS requirements achieves a significant gain in terms of cost and admission rate compared to modeling the whole application traffic with one VNF-FG having the most stringent requirements.
{"title":"Remote Robotic Surgery: Joint Placement and Scheduling of VNF-FGs","authors":"Amina Hentati, Amin Ebrahimzadeh, R. Glitho, F. Belqasmi, R. Mizouni","doi":"10.23919/CNSM55787.2022.9964591","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964591","url":null,"abstract":"Remote robotic surgery is one of the most interesting Tactile Internet (TI) applications. It has a huge potential to deliver healthcare services to remote locations. Moreover, it provides better precision and accuracy to diagnose and operate on patients. Remote robotic surgery requires ultra-low latency and ultra-high reliability. The aforementioned stringent requirements do not apply for all the multimodal data traffic (i.e., audio, video, and haptic) triggered during a surgery session. Hence, customizing resource allocation policies according to the different quality-of-service (QoS) requirements is crucial in order to achieve a cost-effective deployment of such system. In this paper, we focus on resource allocation in a softwarized 5G-enabled TI remote robotic surgery system through the use of Network Functions Virtualization (NFV). Specifically, this work is devoted to the joint placement and scheduling of application components in an NFV-based remote robotic surgery system, while considering haptic and video data. The problem is formulated as an integer linear program (ILP). Due to its complexity, we propose a greedy algorithm to solve the developed ILP in a computationally efficient manner. The simulation results show that our proposed algorithm is close to optimal and outperforms the benchmark solutions in terms of cost and admission rate. Furthermore, our results demonstrate that splitting application traffic to multiple VNF-forwarding graphs (VNF-FGs) with different QoS requirements achieves a significant gain in terms of cost and admission rate compared to modeling the whole application traffic with one VNF-FG having the most stringent requirements.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131049346","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964533
Joël Roman Ky, B. Mathieu, Abdelkader Lahmadi, R. Boutaba
Cloud gaming applications have gained great adoption on the Internet particularly benefiting from the wide availability of broadband access networks. However, they still fail to meet users’ quality requirements when accessed using cellular networks due to common wireless channel degradations. Machine Learning (ML) techniques can be leveraged to detect such anomalies during users’ cloud gaming sessions. In this respect, unsupervised ML approaches are particularly interesting since they do not require labeled datasets. In this work, we investigate these approaches to understand their performance and their robustness. Our dataset consists of game sessions played on the public Google Stadia Cloud Gaming servers. The game sessions are played using a 4G network emulation replicating the capacity variations sampled on a commercial 4G network. We compare different models ranging from traditional approaches to deep learning and we evaluate their default performance while varying the level of contamination in their training datasets. Our experiments show that Auto-Encoders models achieve the best performance without contamination while the OC-SVM and the Isolation Forest are the most robust to data contamination.
{"title":"Assessing Unsupervised Machine Learning solutions for Anomaly Detection in Cloud Gaming Sessions","authors":"Joël Roman Ky, B. Mathieu, Abdelkader Lahmadi, R. Boutaba","doi":"10.23919/CNSM55787.2022.9964533","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964533","url":null,"abstract":"Cloud gaming applications have gained great adoption on the Internet particularly benefiting from the wide availability of broadband access networks. However, they still fail to meet users’ quality requirements when accessed using cellular networks due to common wireless channel degradations. Machine Learning (ML) techniques can be leveraged to detect such anomalies during users’ cloud gaming sessions. In this respect, unsupervised ML approaches are particularly interesting since they do not require labeled datasets. In this work, we investigate these approaches to understand their performance and their robustness. Our dataset consists of game sessions played on the public Google Stadia Cloud Gaming servers. The game sessions are played using a 4G network emulation replicating the capacity variations sampled on a commercial 4G network. We compare different models ranging from traditional approaches to deep learning and we evaluate their default performance while varying the level of contamination in their training datasets. Our experiments show that Auto-Encoders models achieve the best performance without contamination while the OC-SVM and the Isolation Forest are the most robust to data contamination.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134322458","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9965056
Dimitrios Spatharakis, Ioannis Dimolitsas, E. Vlahakis, Dimitrios Dechouniotis, N. Athanasopoulos, S. Papavassiliou
Maximizing the performance of modern applications requires timely resource management of the virtualized resources. However, proactively deploying resources for meeting specific application requirements subject to a dynamic workload profile of incoming requests is extremely challenging. To this end, the fundamental problems of task scheduling and resource autoscaling must be jointly addressed. This paper presents a scalable architecture compatible with the decentralized nature of Kubernetes [1], to solve both. Exploiting the stability guarantees of a novel AIMD-like task scheduling solution, we dynamically redirect the incoming requests towards the containerized application. To cope with dynamic workloads, a prediction mechanism allows us to estimate the number of incoming requests. Additionally, a Machine Learning-based (ML) Application Profiling Modeling is introduced to address the scaling, by co-designing the theoretically-computed service rates obtained from the AIMD algorithm with the current performance metrics. The proposed solution is compared with the state-of-the-art autoscaling techniques under a realistic dataset in a small edge infrastructure and the trade-off between resource utilization and QoS violations are analyzed. Our solution provides better resource utilization by reducing CPU cores by 8% with only an acceptable increase in QoS violations.
{"title":"Distributed Resource Autoscaling in Kubernetes Edge Clusters","authors":"Dimitrios Spatharakis, Ioannis Dimolitsas, E. Vlahakis, Dimitrios Dechouniotis, N. Athanasopoulos, S. Papavassiliou","doi":"10.23919/CNSM55787.2022.9965056","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965056","url":null,"abstract":"Maximizing the performance of modern applications requires timely resource management of the virtualized resources. However, proactively deploying resources for meeting specific application requirements subject to a dynamic workload profile of incoming requests is extremely challenging. To this end, the fundamental problems of task scheduling and resource autoscaling must be jointly addressed. This paper presents a scalable architecture compatible with the decentralized nature of Kubernetes [1], to solve both. Exploiting the stability guarantees of a novel AIMD-like task scheduling solution, we dynamically redirect the incoming requests towards the containerized application. To cope with dynamic workloads, a prediction mechanism allows us to estimate the number of incoming requests. Additionally, a Machine Learning-based (ML) Application Profiling Modeling is introduced to address the scaling, by co-designing the theoretically-computed service rates obtained from the AIMD algorithm with the current performance metrics. The proposed solution is compared with the state-of-the-art autoscaling techniques under a realistic dataset in a small edge infrastructure and the trade-off between resource utilization and QoS violations are analyzed. Our solution provides better resource utilization by reducing CPU cores by 8% with only an acceptable increase in QoS violations.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822541","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9965166
Takanori Hara, Masahiro Sasabe
Network functions virtualization (NFV) realizes diverse and flexible network services by executing network functions on generic hardware as virtual network functions (VNFs). A certain network service is regarded as a sequence of VNFs, called service chain. The service chaining (SC) problem aims at finding an appropriate service path from an origin node to a destination node while executing the VNFs at the intermediate nodes in the required order under resource constraints on nodes and links. The SC problem belongs to the complexity class NP-hard. In our previous work, we modeled the SC problem as an integer linear program (ILP) based on the capacitated shortest path tour problem (CSPTP) where the CSPTP is an extended version of the SPTP with the node and link capacity constraints. We also developed the Lagrangian heuristics to achieve the balance between optimality and computational complexity. In this paper, we further propose a deep reinforcement learning (DRL) framework with the graph neural network (GNN) to realize the CSPTP-based SC adaptive to changes in service demand and/or network topology. Numerical results show that (1) the proposed framework achieves almost the same optimality as the ILP for the CSPTP-based SC and (2) it also works well without retraining even when the service demand changes or the network is partly damaged.
{"title":"Deep Reinforcement Learning with Graph Neural Networks for Capacitated Shortest Path Tour based Service Chaining","authors":"Takanori Hara, Masahiro Sasabe","doi":"10.23919/CNSM55787.2022.9965166","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965166","url":null,"abstract":"Network functions virtualization (NFV) realizes diverse and flexible network services by executing network functions on generic hardware as virtual network functions (VNFs). A certain network service is regarded as a sequence of VNFs, called service chain. The service chaining (SC) problem aims at finding an appropriate service path from an origin node to a destination node while executing the VNFs at the intermediate nodes in the required order under resource constraints on nodes and links. The SC problem belongs to the complexity class NP-hard. In our previous work, we modeled the SC problem as an integer linear program (ILP) based on the capacitated shortest path tour problem (CSPTP) where the CSPTP is an extended version of the SPTP with the node and link capacity constraints. We also developed the Lagrangian heuristics to achieve the balance between optimality and computational complexity. In this paper, we further propose a deep reinforcement learning (DRL) framework with the graph neural network (GNN) to realize the CSPTP-based SC adaptive to changes in service demand and/or network topology. Numerical results show that (1) the proposed framework achieves almost the same optimality as the ILP for the CSPTP-based SC and (2) it also works well without retraining even when the service demand changes or the network is partly damaged.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126534913","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 : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964897
D. Mafioletti, M. Martinello, M. Ribeiro, M. Ruffini, Frank Slyne
Cryptographic hash functions are widely used to provide from digital time stamping to authenticity and digital signatures, mapping an extensive collection of messages into a small set of message digests and help to secure network connection and data, consequently consuming CPU resources. P4 enables data plane customisation using a high-level programming language to facilitate in-network computing development across diverse hardware targets, including Network Interface Cards (NICs). Currently, most P4 targets do not implement secure hash functions due to a lack of hardware instructions or the absence of formal functions to expose their native hardware-based implementation. Moreover, many applications and protocols cannot be instantiated using in-network computing due to stringent requirements based on these hash functions. In order to empower the security and other hash-based applications, in this paper we propose and implement a P4 shared object library for a secure hash algorithm 2 (SHA-2). Our goal is to enable SHA-2 to be used as an embedded Network Function (eNF), overcoming the lack of support in a SmartNIC architecture, in order to address the latency and throughput requirements of Service Function Chain (SFC) forwarding performance within the Network Function Virtualization (NFV) paradigm. Thus, our prototype is evaluated against kernel-level Open vSwitch (OvS) and user-space Data Plane Development Kit (DPDK) implementations. The outcomes demonstrate different tradeoffs over each platform, from the randomness added by the OS to the high cost of executing the aforesaid function using a network programmable device, leading us to highlight the best choice for each specific application.
{"title":"To Embed or Not to Embed SHA in Programmable Network Interface Cards","authors":"D. Mafioletti, M. Martinello, M. Ribeiro, M. Ruffini, Frank Slyne","doi":"10.23919/CNSM55787.2022.9964897","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964897","url":null,"abstract":"Cryptographic hash functions are widely used to provide from digital time stamping to authenticity and digital signatures, mapping an extensive collection of messages into a small set of message digests and help to secure network connection and data, consequently consuming CPU resources. P4 enables data plane customisation using a high-level programming language to facilitate in-network computing development across diverse hardware targets, including Network Interface Cards (NICs). Currently, most P4 targets do not implement secure hash functions due to a lack of hardware instructions or the absence of formal functions to expose their native hardware-based implementation. Moreover, many applications and protocols cannot be instantiated using in-network computing due to stringent requirements based on these hash functions. In order to empower the security and other hash-based applications, in this paper we propose and implement a P4 shared object library for a secure hash algorithm 2 (SHA-2). Our goal is to enable SHA-2 to be used as an embedded Network Function (eNF), overcoming the lack of support in a SmartNIC architecture, in order to address the latency and throughput requirements of Service Function Chain (SFC) forwarding performance within the Network Function Virtualization (NFV) paradigm. Thus, our prototype is evaluated against kernel-level Open vSwitch (OvS) and user-space Data Plane Development Kit (DPDK) implementations. The outcomes demonstrate different tradeoffs over each platform, from the randomness added by the OS to the high cost of executing the aforesaid function using a network programmable device, leading us to highlight the best choice for each specific application.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638083","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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