Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012693
F. Zaker, Marin Litoiu, Mark Shtern
In this paper, we propose and implement a distributed autonomic manager to maintain service level agreements (SLA) for each application’ scenario. The proposed autonomic manager seeks to support SLAs by configuring bandwidth ratios for each application scenario using overlay network before provisioning more computing resources. The most important aspect of the proposed autonomic manager is scalability which allows us to deal with geographically distributed cloud-based applications and large volume of computation. This can be useful in look ahead optimization and when using complex models, such as machine learning. Through experiments on Amazon AWS cloud, we demonstrate the elasticity of the autonomic manager.
{"title":"Look Ahead Distributed Planning For Application Management In Cloud","authors":"F. Zaker, Marin Litoiu, Mark Shtern","doi":"10.23919/CNSM46954.2019.9012693","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012693","url":null,"abstract":"In this paper, we propose and implement a distributed autonomic manager to maintain service level agreements (SLA) for each application’ scenario. The proposed autonomic manager seeks to support SLAs by configuring bandwidth ratios for each application scenario using overlay network before provisioning more computing resources. The most important aspect of the proposed autonomic manager is scalability which allows us to deal with geographically distributed cloud-based applications and large volume of computation. This can be useful in look ahead optimization and when using complex models, such as machine learning. Through experiments on Amazon AWS cloud, we demonstrate the elasticity of the autonomic manager.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"41 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":"124290101","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/CNSM46954.2019.9012749
Anssi Tauriainen
Automation of mobile network fault diagnostics and troubleshooting is critical for successful transformation to new network technologies such as 5G and core Network Function Virtualization (NFV). This paper presents a decision tree-based call detail record (CDR) labeling process, which is used to construct an automated end-to-end diagnostics system for mobile network faults. The presented diagnostics system will enable the utilization of automated troubleshooting systems, and the execution of automated corrective actions in third party systems such as Self-Organizing Network (SON) and NFV domain orchestrator.
{"title":"Can you hear me now? A call detail record based end-to-end diagnostics system for mobile networks","authors":"Anssi Tauriainen","doi":"10.23919/CNSM46954.2019.9012749","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012749","url":null,"abstract":"Automation of mobile network fault diagnostics and troubleshooting is critical for successful transformation to new network technologies such as 5G and core Network Function Virtualization (NFV). This paper presents a decision tree-based call detail record (CDR) labeling process, which is used to construct an automated end-to-end diagnostics system for mobile network faults. The presented diagnostics system will enable the utilization of automated troubleshooting systems, and the execution of automated corrective actions in third party systems such as Self-Organizing Network (SON) and NFV domain orchestrator.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"35 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":"123131510","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/CNSM46954.2019.9012718
Satoru Kobayashi, Kazuki Otomo, K. Fukuda
To detect root causes of failures in large-scale networks, we need to extract contextual information from operational data automatically. Correlation-based methods are widely used for this purpose, but they have a problem of spurious correlation, which buries truly important information. In this work, we propose a method for extracting contextual information in network logs by combining a graph-based causal inference algorithm and a pruning method based on domain knowledge (i.e., network protocols and topologies). Applying the proposed method to a set of log data collected from a nation-wide R & E network, we demonstrate that the pruning method reduced processing time by 74% compared with a single-handed causal analysis method, and it detected more useful information for troubleshooting compared with an existing area-based method.
{"title":"Causal analysis of network logs with layered protocols and topology knowledge","authors":"Satoru Kobayashi, Kazuki Otomo, K. Fukuda","doi":"10.23919/CNSM46954.2019.9012718","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012718","url":null,"abstract":"To detect root causes of failures in large-scale networks, we need to extract contextual information from operational data automatically. Correlation-based methods are widely used for this purpose, but they have a problem of spurious correlation, which buries truly important information. In this work, we propose a method for extracting contextual information in network logs by combining a graph-based causal inference algorithm and a pruning method based on domain knowledge (i.e., network protocols and topologies). Applying the proposed method to a set of log data collected from a nation-wide R & E network, we demonstrate that the pruning method reduced processing time by 74% compared with a single-handed causal analysis method, and it detected more useful information for troubleshooting compared with an existing area-based method.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"40 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":"130459077","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/CNSM46954.2019.9012731
Guy Saadon, Yoram Haddad, N. Simoni
In network management architectures of 5G and IoT networks, standardization groups often consider the network resource virtualization layer between the physical network and the SDN controller, as a means to allow deployment and placement of network services with their virtual network functions. However, the following question arises: is this layer enough to react to real-time changes originating from customers or the network without interrupting the service? We consider that a dynamic architecture should allow different and evolving assemblies to be provisioned during a session, in order to meet modification requests without requiring total redesign of the network service. Therefore, in this study, we propose an enhanced architecture. This novel architecture adds a network virtualization layer above the SDN controller with its associated orchestrator. Then, efficiently distributing orchestration among the different layers ensures network autonomy. In this context, we show how real-time service modifications and network failures are handled without losing the existing services and how network management gains additional dynamicity and flexibility.
{"title":"Dynamic architecture based on network virtualization and distributed orchestration for management of autonomic network","authors":"Guy Saadon, Yoram Haddad, N. Simoni","doi":"10.23919/CNSM46954.2019.9012731","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012731","url":null,"abstract":"In network management architectures of 5G and IoT networks, standardization groups often consider the network resource virtualization layer between the physical network and the SDN controller, as a means to allow deployment and placement of network services with their virtual network functions. However, the following question arises: is this layer enough to react to real-time changes originating from customers or the network without interrupting the service? We consider that a dynamic architecture should allow different and evolving assemblies to be provisioned during a session, in order to meet modification requests without requiring total redesign of the network service. Therefore, in this study, we propose an enhanced architecture. This novel architecture adds a network virtualization layer above the SDN controller with its associated orchestrator. Then, efficiently distributing orchestration among the different layers ensures network autonomy. In this context, we show how real-time service modifications and network failures are handled without losing the existing services and how network management gains additional dynamicity and flexibility.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"6 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":"133430984","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/CNSM46954.2019.9012686
M. Franco, B. Rodrigues, B. Stiller
Cyberattacks are the cause of several damages on governments and companies in the last years. Such damage includes not only leaks of sensitive information, but also economic loss due to downtime of services. The security market size worth billions of dollars, which represents investments to acquire protection services and training response teams to operate such services, determines a considerable part of the investment in technologies around the world. Although a vast number of protection services are available, it is neither trivial for network operators nor end-users to choose one of them in order to prevent or mitigate an imminent attack. As the next-generation cybersecurity solutions are on the horizon, systems that simplify their adoption are still required in support of security management tasks. Thus, this paper introduces MENTOR, a support tool for cyber-security, focusing on the recommendation of protection services. MENTOR is able to (${a}$) to deal with different demands from the user and (${b}$) to recommend the adequate protection service in order to provide a proper level of cybersecurity in different scenarios. Four similarity measurements are implemented in order to prove the feasibility of the MENTOR’${s}$ engine. An evaluation determines the performance and accuracy of each measurement used during the recommendation process.
{"title":"MENTOR: The Design and Evaluation of a Protection Services Recommender System","authors":"M. Franco, B. Rodrigues, B. Stiller","doi":"10.23919/CNSM46954.2019.9012686","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012686","url":null,"abstract":"Cyberattacks are the cause of several damages on governments and companies in the last years. Such damage includes not only leaks of sensitive information, but also economic loss due to downtime of services. The security market size worth billions of dollars, which represents investments to acquire protection services and training response teams to operate such services, determines a considerable part of the investment in technologies around the world. Although a vast number of protection services are available, it is neither trivial for network operators nor end-users to choose one of them in order to prevent or mitigate an imminent attack. As the next-generation cybersecurity solutions are on the horizon, systems that simplify their adoption are still required in support of security management tasks. Thus, this paper introduces MENTOR, a support tool for cyber-security, focusing on the recommendation of protection services. MENTOR is able to (${a}$) to deal with different demands from the user and (${b}$) to recommend the adequate protection service in order to provide a proper level of cybersecurity in different scenarios. Four similarity measurements are implemented in order to prove the feasibility of the MENTOR’${s}$ engine. An evaluation determines the performance and accuracy of each measurement used during the recommendation process.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"57 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":"116357292","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/CNSM46954.2019.9012716
Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, A. Gepperth, Sven Reissmann
We present a processing pipeline for flow-based throughput classification based on a machine learning component using deep neural networks (DNNs) that is trained to predict the likely bit rate of a real-world network traffic flow ahead of time. The DNN is trained and evaluated on a flow data stream as well as on a reference dataset collected from a university data center. Predicted bit rates are quantized into three classes instead of the common binary classification into “mice” and “elephant” flows. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. We employ t-SNE (a state-of-the-art data visualization algorithm) to visualize network traffic data, thus enabling us to analyze and understand the characteristics of network traffic data and relations between communication flows at a glance. Additionally, an architecture for flow-based routing utilizing the developed pipeline is proposed as a possible use-case.
{"title":"Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic","authors":"Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, A. Gepperth, Sven Reissmann","doi":"10.23919/CNSM46954.2019.9012716","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012716","url":null,"abstract":"We present a processing pipeline for flow-based throughput classification based on a machine learning component using deep neural networks (DNNs) that is trained to predict the likely bit rate of a real-world network traffic flow ahead of time. The DNN is trained and evaluated on a flow data stream as well as on a reference dataset collected from a university data center. Predicted bit rates are quantized into three classes instead of the common binary classification into “mice” and “elephant” flows. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. We employ t-SNE (a state-of-the-art data visualization algorithm) to visualize network traffic data, thus enabling us to analyze and understand the characteristics of network traffic data and relations between communication flows at a glance. Additionally, an architecture for flow-based routing utilizing the developed pipeline is proposed as a possible use-case.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"20 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":"114199278","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/CNSM46954.2019.9012710
Duc C. Le, A. N. Zincir-Heywood
This work presents an emerging problem in real-world applications of machine learning (ML) in cybersecurity, particularly in botnet detection, where the dynamics and the evolution in the deployment environments may render the ML solutions inadequate. We propose an approach to tackle this challenge using Genetic Programming (GP) - an evolutionary computation based approach. Preliminary results show that GP is able to evolve pre-trained classifiers to work under evolved (expanded) feature space conditions. This indicates the potential use of such an approach for botnet detection under non-stationary environments, where much less data and training time are required to obtain a reliable classifier as new network conditions arise.
{"title":"Learning From Evolving Network Data for Dependable Botnet Detection","authors":"Duc C. Le, A. N. Zincir-Heywood","doi":"10.23919/CNSM46954.2019.9012710","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012710","url":null,"abstract":"This work presents an emerging problem in real-world applications of machine learning (ML) in cybersecurity, particularly in botnet detection, where the dynamics and the evolution in the deployment environments may render the ML solutions inadequate. We propose an approach to tackle this challenge using Genetic Programming (GP) - an evolutionary computation based approach. Preliminary results show that GP is able to evolve pre-trained classifiers to work under evolved (expanded) feature space conditions. This indicates the potential use of such an approach for botnet detection under non-stationary environments, where much less data and training time are required to obtain a reliable classifier as new network conditions arise.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"64 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":"114216772","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/cnsm46954.2019.9012723
{"title":"CNSM 2019 Cover Page","authors":"","doi":"10.23919/cnsm46954.2019.9012723","DOIUrl":"https://doi.org/10.23919/cnsm46954.2019.9012723","url":null,"abstract":"","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"81 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":"122583166","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/CNSM46954.2019.9012719
Iman Vakilinia, Shahin Vakilinia, S. Badsha, Engin Arslan, S. Sengupta
Blockchain technology has provided a solid system to develop incentivization algorithms using the smart contract. Blockchain applies the distributed ledger to store transaction histories, and the information is stored across a network of computers instead of on a single server. This facilitates the development of a new set of applications such as distributed file storage systems where users can rent out their storage in return for a premium. The distributed file storage systems provide more privacy and security compared to the centralized storage models as there is no need to have a trusted party. New schemes have been developed for distributed file storage systems on top of the blockchain platform, however, the problem of task/service allocation in these models have not been studied before. In this paper, we study the task/service allocation in the distributed file storage systems considering the challenge of computation cost. First, we formalize the problem of task/service allocation in a decentralized storage network, and then we discuss different approaches to allocate storage tasks to storage servers in an efficient manner. Moreover, we study the benefits of the cooperation (a.k.a pooling) in the storage and retrieval markets of distributed storage networks. The evaluation results show the benefit of our proposed pooling based approach in storage and retrieval markets.
{"title":"Pooling Approach for Task Allocation in the Blockchain Based Decentralized Storage Network","authors":"Iman Vakilinia, Shahin Vakilinia, S. Badsha, Engin Arslan, S. Sengupta","doi":"10.23919/CNSM46954.2019.9012719","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012719","url":null,"abstract":"Blockchain technology has provided a solid system to develop incentivization algorithms using the smart contract. Blockchain applies the distributed ledger to store transaction histories, and the information is stored across a network of computers instead of on a single server. This facilitates the development of a new set of applications such as distributed file storage systems where users can rent out their storage in return for a premium. The distributed file storage systems provide more privacy and security compared to the centralized storage models as there is no need to have a trusted party. New schemes have been developed for distributed file storage systems on top of the blockchain platform, however, the problem of task/service allocation in these models have not been studied before. In this paper, we study the task/service allocation in the distributed file storage systems considering the challenge of computation cost. First, we formalize the problem of task/service allocation in a decentralized storage network, and then we discuss different approaches to allocate storage tasks to storage servers in an efficient manner. Moreover, we study the benefits of the cooperation (a.k.a pooling) in the storage and retrieval markets of distributed storage networks. The evaluation results show the benefit of our proposed pooling based approach in storage and retrieval markets.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"19 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120852830","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/CNSM46954.2019.9012677
Walter E. Santo, Rubens Souza Munhos Junior, A. Ribeiro, D. Silva, R. Santos
There is an increasing number of Internet of Things (IoT) devices in the border of computer networks, requiring local processing and lightweight virtualization to deal with issues such as heterogeneity, Quality of Service (QoS) management, scalability, mobility, federation, and interoperability. Fog computing can provide the computational resources required by IoT devices to process their data. Low energy consumption and total cost of ownership are among the desirable properties for auxiliar infrastructures such as those deployed for fog computing, which do not require large computational power though. There is a noteworthy trend of undergoing research efforts towards the definition of software and hardware architectures for fog computing in this context. In this sense, this paper presents a Systematic Literature Mapping with the purpose of understanding and identifying metrics and gaps in current literature about orchestration of container-based applications, especially those hosted in clusters of Single Board Computer (SBC) platforms, such as Raspberry Pi, which have been used for deploying fog computing environments.
{"title":"Systematic Mapping on Orchestration of Container-based Applications in Fog Computing","authors":"Walter E. Santo, Rubens Souza Munhos Junior, A. Ribeiro, D. Silva, R. Santos","doi":"10.23919/CNSM46954.2019.9012677","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012677","url":null,"abstract":"There is an increasing number of Internet of Things (IoT) devices in the border of computer networks, requiring local processing and lightweight virtualization to deal with issues such as heterogeneity, Quality of Service (QoS) management, scalability, mobility, federation, and interoperability. Fog computing can provide the computational resources required by IoT devices to process their data. Low energy consumption and total cost of ownership are among the desirable properties for auxiliar infrastructures such as those deployed for fog computing, which do not require large computational power though. There is a noteworthy trend of undergoing research efforts towards the definition of software and hardware architectures for fog computing in this context. In this sense, this paper presents a Systematic Literature Mapping with the purpose of understanding and identifying metrics and gaps in current literature about orchestration of container-based applications, especially those hosted in clusters of Single Board Computer (SBC) platforms, such as Raspberry Pi, which have been used for deploying fog computing environments.","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":"129517597","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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