Krzysztof Rusek, J. Suárez-Varela, Albert Mestres, P. Barlet-Ros, A. Cabellos-Aparicio
Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.
{"title":"Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN","authors":"Krzysztof Rusek, J. Suárez-Varela, Albert Mestres, P. Barlet-Ros, A. Cabellos-Aparicio","doi":"10.1145/3314148.3314357","DOIUrl":"https://doi.org/10.1145/3314148.3314357","url":null,"abstract":"Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.","PeriodicalId":346870,"journal":{"name":"Proceedings of the 2019 ACM Symposium on SDN Research","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124972742","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}
Current networks more and more rely on virtualized middleboxes to flexibly provide security, protocol optimization, and policy compliance functionalities. As such, delivering these services requires that the traffic be steered through the desired sequence of virtual appliances. Current solutions introduce a new logically centralized entity, often called orchestrator, needing to build its own holistic view of the whole network so to decide where to direct the traffic. We advocate that such a centralized orchestration is not necessary and that, on the contrary, the same objectives can be achieved by augmenting the network layer routing so to include the notion of service and its chaining. In this paper, we support our claim by designing such a system called NFV Router. We also present an implementation and an early evaluation, showing that we can easily steer traffic through available resources. The proposed approach offers as well valuable features such as incremental deploya-bility, multi-domain service chaining, failure resiliency, and easy maintenance.
{"title":"Distributed Function Chaining with Anycast Routing","authors":"Adrien Wion, M. Bouet, L. Iannone, V. Conan","doi":"10.1145/3314148.3314355","DOIUrl":"https://doi.org/10.1145/3314148.3314355","url":null,"abstract":"Current networks more and more rely on virtualized middleboxes to flexibly provide security, protocol optimization, and policy compliance functionalities. As such, delivering these services requires that the traffic be steered through the desired sequence of virtual appliances. Current solutions introduce a new logically centralized entity, often called orchestrator, needing to build its own holistic view of the whole network so to decide where to direct the traffic. We advocate that such a centralized orchestration is not necessary and that, on the contrary, the same objectives can be achieved by augmenting the network layer routing so to include the notion of service and its chaining. In this paper, we support our claim by designing such a system called NFV Router. We also present an implementation and an early evaluation, showing that we can easily steer traffic through available resources. The proposed approach offers as well valuable features such as incremental deploya-bility, multi-domain service chaining, failure resiliency, and easy maintenance.","PeriodicalId":346870,"journal":{"name":"Proceedings of the 2019 ACM Symposium on SDN Research","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127266009","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}
{"title":"Proceedings of the 2019 ACM Symposium on SDN Research","authors":"","doi":"10.1145/3314148","DOIUrl":"https://doi.org/10.1145/3314148","url":null,"abstract":"","PeriodicalId":346870,"journal":{"name":"Proceedings of the 2019 ACM Symposium on SDN Research","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449546","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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