Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012720
Maroua Ben Attia, K. Nguyen, M. Cheriet
Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.
{"title":"Concurrent Traffic Queuing Game in Smart Home","authors":"Maroua Ben Attia, K. Nguyen, M. Cheriet","doi":"10.23919/CNSM46954.2019.9012720","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012720","url":null,"abstract":"Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116083643","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012659
Hadi Razzaghi Kouchaksaraei, H. Karl
Services in Network Function Virtualization (NFV) can have a variety of requirements such as data rates, latencies, and cost that can change during the lifecycle of services. To meet these requirements, various hardware and software resources are suggested for implementing Virtualized Network Functions (VNFs). However, meeting all service requirements using one implementation option is not always possible. For example, to improve the performance of VNFs, using acceleration hardware is proposed. Although acceleration hardware can improve the performance of a network function, as they are expensive appliances, they increase the cost of services; this might not be desirable for a particular service user or load that can be handled by cheaper resources. Dynamically provisioning services can solve this problem in which different implementations of VNFs are switched on the fly as service requirements change. In this paper, we analyse this service provisioning approach in terms of performance, cost, and management overhead by experimenting an example VNF.
{"title":"Quantitative Analysis of Dynamically Provisioned Heterogeneous Network Services","authors":"Hadi Razzaghi Kouchaksaraei, H. Karl","doi":"10.23919/CNSM46954.2019.9012659","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012659","url":null,"abstract":"Services in Network Function Virtualization (NFV) can have a variety of requirements such as data rates, latencies, and cost that can change during the lifecycle of services. To meet these requirements, various hardware and software resources are suggested for implementing Virtualized Network Functions (VNFs). However, meeting all service requirements using one implementation option is not always possible. For example, to improve the performance of VNFs, using acceleration hardware is proposed. Although acceleration hardware can improve the performance of a network function, as they are expensive appliances, they increase the cost of services; this might not be desirable for a particular service user or load that can be handled by cheaper resources. Dynamically provisioning services can solve this problem in which different implementations of VNFs are switched on the fly as service requirements change. In this paper, we analyse this service provisioning approach in terms of performance, cost, and management overhead by experimenting an example VNF.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129348903","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012668
H. Mai, G. Doyen, Wissam Mallouli, Edgardo Montes de Oca, O. Festor
Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.
{"title":"Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions","authors":"H. Mai, G. Doyen, Wissam Mallouli, Edgardo Montes de Oca, O. Festor","doi":"10.23919/CNSM46954.2019.9012668","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012668","url":null,"abstract":"Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566473","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012683
K. Batbayar, Emmanouil Dimogerontakis, Roc Meseguer, L. Navarro, R. Sadre
Modern large-scale networked services, such as video streaming, are typically deployed at multiple locations in the network to provide redundancy and load balancing. Different techniques are used to provide performance monitoring information so that client nodes can select the best service instance. One of them is collaborative sensing, where clients share measurement results on the observed service performance to build a common ground of knowledge with low overhead. Clients can then use this common ground to select the most suitable service provider. However, collaborative algorithms are susceptible to false measurements sent by malfunctioning or malicious nodes, which decreases the accuracy of the performance sensing process. We propose Sense-Share, a simple light-weight and resilient collaborative sensing framework based on the similarity of the client nodes’ perception of service performance. Our experimental evaluation in different topologies shows that service performance sensing using Sense-Share achieves, on average, 94% similarity to non-collaborative brute force performance sensing, tolerating faulty nodes. Furthermore, our approach effectively distributes the service monitoring requests over the service nodes and exploits direct inter-node communication to share measurements, resulting in reduced monitoring overhead.
{"title":"Sense-Share: A Framework for Resilient Collaborative Service Performance Monitoring","authors":"K. Batbayar, Emmanouil Dimogerontakis, Roc Meseguer, L. Navarro, R. Sadre","doi":"10.23919/CNSM46954.2019.9012683","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012683","url":null,"abstract":"Modern large-scale networked services, such as video streaming, are typically deployed at multiple locations in the network to provide redundancy and load balancing. Different techniques are used to provide performance monitoring information so that client nodes can select the best service instance. One of them is collaborative sensing, where clients share measurement results on the observed service performance to build a common ground of knowledge with low overhead. Clients can then use this common ground to select the most suitable service provider. However, collaborative algorithms are susceptible to false measurements sent by malfunctioning or malicious nodes, which decreases the accuracy of the performance sensing process. We propose Sense-Share, a simple light-weight and resilient collaborative sensing framework based on the similarity of the client nodes’ perception of service performance. Our experimental evaluation in different topologies shows that service performance sensing using Sense-Share achieves, on average, 94% similarity to non-collaborative brute force performance sensing, tolerating faulty nodes. Furthermore, our approach effectively distributes the service monitoring requests over the service nodes and exploits direct inter-node communication to share measurements, resulting in reduced monitoring overhead.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679284","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012678
J. Haxhibeqiri, I. Moerman, J. Hoebeke
Wireless netWorks are becoming more complex while applications on top are becoming more demanding. To maintain network performance in terms of latency, throughput and reliability, continuous verification of the performance, possibly followed by on-the-fly network (re)configuration is needed. To achieve this, the way wireless network monitoring is being done needs to be reconsidered and should evolve towards more timely, low overhead and fine-grained monitoring. This paper shows hoiv in-band network telemetry (INT) monitoring can achieve these objectives. An INT-enabled node architecture is designed as well as novel INT options. By means of an implementation on WiFi Linux devices, the concept is validated by tracking the behavior of a real network.
{"title":"Low Overhead, Fine-grained End-to-end Monitoring of Wireless Networks using In-band Telemetry","authors":"J. Haxhibeqiri, I. Moerman, J. Hoebeke","doi":"10.23919/CNSM46954.2019.9012678","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012678","url":null,"abstract":"Wireless netWorks are becoming more complex while applications on top are becoming more demanding. To maintain network performance in terms of latency, throughput and reliability, continuous verification of the performance, possibly followed by on-the-fly network (re)configuration is needed. To achieve this, the way wireless network monitoring is being done needs to be reconsidered and should evolve towards more timely, low overhead and fine-grained monitoring. This paper shows hoiv in-band network telemetry (INT) monitoring can achieve these objectives. An INT-enabled node architecture is designed as well as novel INT options. By means of an implementation on WiFi Linux devices, the concept is validated by tracking the behavior of a real network.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127594936","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012672
Othmane Belmoukadam, Muhammad Jawad Khokhar, C. Barakat
Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. Terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience [1]. Previous studies tried to link MOS (Mean Opinion Score) to video bit rate for different screen types (e.g., CIF, QCIF, and HD) [2]. We leverage such studies and formulate a QoE-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE (Quality of Experience) over all users of a provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on data sets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and KKT (Karush Kuhn Tucker) conditions for a subset of constraints. Numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with TCP look-alike strategies implementing max-min fairness. Later, we use a MPEG/DASH implementation in the context of ns-3 and show that coupling our approach with a rate adaptation algorithm (e.g., [3]) can help increasing QoE while reducing both resolution switches and number of interruptions.
{"title":"On Accounting for Screen Resolution in Adaptive Video Streaming: A QoE-Driven Bandwidth Sharing Framework","authors":"Othmane Belmoukadam, Muhammad Jawad Khokhar, C. Barakat","doi":"10.23919/CNSM46954.2019.9012672","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012672","url":null,"abstract":"Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. Terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience [1]. Previous studies tried to link MOS (Mean Opinion Score) to video bit rate for different screen types (e.g., CIF, QCIF, and HD) [2]. We leverage such studies and formulate a QoE-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE (Quality of Experience) over all users of a provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on data sets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and KKT (Karush Kuhn Tucker) conditions for a subset of constraints. Numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with TCP look-alike strategies implementing max-min fairness. Later, we use a MPEG/DASH implementation in the context of ns-3 and show that coupling our approach with a rate adaptation algorithm (e.g., [3]) can help increasing QoE while reducing both resolution switches and number of interruptions.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129216474","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012722
Mihir Joshi, Parmeet Singh, A. N. Zincir-Heywood
The aim of this work is to detect compromised users of tweets based on their writing styles. In this paper, we use Siamese Networks to learn a representation of user tweets that allows us to classify them based on a limited amount of ground truth data. We propose the employment of this classification model to identify compromised user accounts of tweets.
{"title":"Compromised Tweet Detection Using Siamese Networks and fastText Representations","authors":"Mihir Joshi, Parmeet Singh, A. N. Zincir-Heywood","doi":"10.23919/CNSM46954.2019.9012722","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012722","url":null,"abstract":"The aim of this work is to detect compromised users of tweets based on their writing styles. In this paper, we use Siamese Networks to learn a representation of user tweets that allows us to classify them based on a limited amount of ground truth data. We propose the employment of this classification model to identify compromised user accounts of tweets.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358777","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012738
Lionel Metongnon, R. Sadre, E. C. Ezin
The Internet of Things (IoT) is not one single entity, but a collection of different devices, communication technologies, protocols and services. IoT systems can span a large number of individually managed networks that are interconnected through the Internet and host the different components of an IoT application, such as sensor devices, storage servers and data processing services. Protecting such a complex multiparty system from abuse becomes a very challenging task. New difficulties arise everyday when policies are updated or new collaborations and federations appear between entities. Moreover, hacked IoT devices can also become the source of powerful attacks, as the Mirai malware has demonstrated, and therefore a danger for the other involved parties. In this paper, we propose an approach to improve the management and protection of collaborating IoT systems using distributed intrusion detection and permission-based access control. Our approach is based on interconnected middleboxes that monitor the communication between the various IoT networks and are able to stop incoming as well as outgoing attacks. We evaluate our approach through experiments with different types of attacks.
{"title":"Distributed Middlebox Architecture for IoT Protection","authors":"Lionel Metongnon, R. Sadre, E. C. Ezin","doi":"10.23919/CNSM46954.2019.9012738","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012738","url":null,"abstract":"The Internet of Things (IoT) is not one single entity, but a collection of different devices, communication technologies, protocols and services. IoT systems can span a large number of individually managed networks that are interconnected through the Internet and host the different components of an IoT application, such as sensor devices, storage servers and data processing services. Protecting such a complex multiparty system from abuse becomes a very challenging task. New difficulties arise everyday when policies are updated or new collaborations and federations appear between entities. Moreover, hacked IoT devices can also become the source of powerful attacks, as the Mirai malware has demonstrated, and therefore a danger for the other involved parties. In this paper, we propose an approach to improve the management and protection of collaborating IoT systems using distributed intrusion detection and permission-based access control. Our approach is based on interconnected middleboxes that monitor the communication between the various IoT networks and are able to stop incoming as well as outgoing attacks. We evaluate our approach through experiments with different types of attacks.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205500","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012679
O. Oyebode, Rita Orji
Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country’s diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms; hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.
{"title":"Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and Machine Learning","authors":"O. Oyebode, Rita Orji","doi":"10.23919/CNSM46954.2019.9012679","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012679","url":null,"abstract":"Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country’s diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms; hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314622","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012669
A. Lazaris, V. Prasanna
The ability to generate network traffic predictions at short time scales is crucial for many network management tasks such as traffic engineering, anomaly detection, and traffic matrix estimation. However, building models that are able to predict the traffic from modern networks at short time scales is not a trivial task due to the diversity of the network traffic sources. In this paper, we present a framework for network-wide link-level traffic prediction using Long Short-Term Memory (LSTM) neural networks. Our proposed framework leverages link statistics that can be easily collected either by the controller of a Software Defined Network (SDN), or by SNMP measurements in a legacy network, in order to predict future link throughputs. We implement several variations of LSTMs and compare their performance with traditional baseline models. Our evaluation study using real network traces from a Tier-1 ISP illustrates that LSTMs can predict link throughputs with very high accuracy outperforming the baselines for various traffic aggregation levels and time scales.
{"title":"Deep Learning Models For Aggregated Network Traffic Prediction","authors":"A. Lazaris, V. Prasanna","doi":"10.23919/CNSM46954.2019.9012669","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012669","url":null,"abstract":"The ability to generate network traffic predictions at short time scales is crucial for many network management tasks such as traffic engineering, anomaly detection, and traffic matrix estimation. However, building models that are able to predict the traffic from modern networks at short time scales is not a trivial task due to the diversity of the network traffic sources. In this paper, we present a framework for network-wide link-level traffic prediction using Long Short-Term Memory (LSTM) neural networks. Our proposed framework leverages link statistics that can be easily collected either by the controller of a Software Defined Network (SDN), or by SNMP measurements in a legacy network, in order to predict future link throughputs. We implement several variations of LSTMs and compare their performance with traditional baseline models. Our evaluation study using real network traces from a Tier-1 ISP illustrates that LSTMs can predict link throughputs with very high accuracy outperforming the baselines for various traffic aggregation levels and time scales.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123746092","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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