Pub Date : 2018-04-23DOI: 10.1109/NOMS.2018.8406112
Sina Rafati Niya, Florian Shupfer, T. Bocek, B. Stiller
Smart Contracts (SC) extend the applicability of Blockchains (BC) in various decentralized use cases. This work demonstrates the design and implementation of a trading application which, employs SC and Ethereum BC. This Decentralized Application (Dapp) provides flexibility in requesting user Identity (ID) directly by seller/hirer and buyers/renter. To provide trust, deposits are paid by two sides while setting up contracts. WiFi- Direct is the chosen Device to Device (D2D) communication protocol which provides high data rates and secure data transmission. Light-Weight SC are introduced in this work which, use D2D communications for sending sold or rented object's or each party's images, and ID data directly to other party instead of storing them in the public BC to reduce the costs. Evaluations in terms of D2D deployment, transaction costs, and privacy, indicate that this system is time-efficient and manages the process in a cost-efficient fashion without the need to store and publish all of the user's ID information in BC.
{"title":"Setting up flexible and light weight trading with enhanced user privacy using smart contracts","authors":"Sina Rafati Niya, Florian Shupfer, T. Bocek, B. Stiller","doi":"10.1109/NOMS.2018.8406112","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406112","url":null,"abstract":"Smart Contracts (SC) extend the applicability of Blockchains (BC) in various decentralized use cases. This work demonstrates the design and implementation of a trading application which, employs SC and Ethereum BC. This Decentralized Application (Dapp) provides flexibility in requesting user Identity (ID) directly by seller/hirer and buyers/renter. To provide trust, deposits are paid by two sides while setting up contracts. WiFi- Direct is the chosen Device to Device (D2D) communication protocol which provides high data rates and secure data transmission. Light-Weight SC are introduced in this work which, use D2D communications for sending sold or rented object's or each party's images, and ID data directly to other party instead of storing them in the public BC to reduce the costs. Evaluations in terms of D2D deployment, transaction costs, and privacy, indicate that this system is time-efficient and manages the process in a cost-efficient fashion without the need to store and publish all of the user's ID information in BC.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89510077","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406225
Christopher E. Dabrowski, K. Mills
Congestion in communication networks can be modeled as a percolation process, where congestion spreads minimally before a critical load and expands rapidly afterwards. Some studies identify predict onset of rapidly expanding congestion in time to alert network managers to take mitigating actions to avoid congestion collapse. The paper specifies five predictors: autocorrelation, variance, threshold, growth persistence, and growth rate. Predictor performance is measured for three simulated network models, under two traffic scenarios: increasing and steady load. Predictors are compared on implementation cost, accuracy, warning time, and persistence. The rates and types of prediction errors are also characterized. Results showed that: (1) predictor performance is influenced by network-model realism; (2) the autocorrelation and variance predictors performed poorly in some situations; (3) the threshold predictor yielded best overall accuracy, with mean warning time exceeding seven minutes for the most realistic network model. The paper also suggests a necessary condition to control false positives.
{"title":"Evaluating predictors of congestion collapse in communication networks","authors":"Christopher E. Dabrowski, K. Mills","doi":"10.1109/NOMS.2018.8406225","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406225","url":null,"abstract":"Congestion in communication networks can be modeled as a percolation process, where congestion spreads minimally before a critical load and expands rapidly afterwards. Some studies identify predict onset of rapidly expanding congestion in time to alert network managers to take mitigating actions to avoid congestion collapse. The paper specifies five predictors: autocorrelation, variance, threshold, growth persistence, and growth rate. Predictor performance is measured for three simulated network models, under two traffic scenarios: increasing and steady load. Predictors are compared on implementation cost, accuracy, warning time, and persistence. The rates and types of prediction errors are also characterized. Results showed that: (1) predictor performance is influenced by network-model realism; (2) the autocorrelation and variance predictors performed poorly in some situations; (3) the threshold predictor yielded best overall accuracy, with mean warning time exceeding seven minutes for the most realistic network model. The paper also suggests a necessary condition to control false positives.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89959361","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406286
Luis Guillen, S. Izumi, Toru Abe, T. Suganuma, H. Muraoka
Due to the increasing need for timely and reliable access to user-generated content, Distributed Storage Systems (DSS) became more relevant in the past years. However, since they have more interconnections among layers compared to traditional network applications, load imbalance issues arise. In this paper, we propose a hybrid approach combining server and link load balancing for multipath routing in DSS. The approach is Software Defined Networking (SDN) based, and uses a process we call on-demand inverse multiplexing. Preliminary results show that, by applying the proposal, the overall throughput considerably increases and resource usage remain balanced.
{"title":"SDN-based hybrid server and link load balancing in multipath distributed storage systems","authors":"Luis Guillen, S. Izumi, Toru Abe, T. Suganuma, H. Muraoka","doi":"10.1109/NOMS.2018.8406286","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406286","url":null,"abstract":"Due to the increasing need for timely and reliable access to user-generated content, Distributed Storage Systems (DSS) became more relevant in the past years. However, since they have more interconnections among layers compared to traditional network applications, load imbalance issues arise. In this paper, we propose a hybrid approach combining server and link load balancing for multipath routing in DSS. The approach is Software Defined Networking (SDN) based, and uses a process we call on-demand inverse multiplexing. Preliminary results show that, by applying the proposal, the overall throughput considerably increases and resource usage remain balanced.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74908619","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406216
Lu Liu, Ao Xiong, Peng Yu, Lei Feng, Wenjing Li, Xue-song Qiu, Mingxiong Wang
Recently, a new opportunity for on-grid energy saving is enabled by the green network infrastructure sharing. This paper mainly investigates the collaboration between multiple operators to improve the energy utilization in this scenario. Then, an energy-saving management mechanism is proposed to reduce energy consumption and optimize energy utilization. We decompose the problem into two sub problems for base station sleeping and green energy allocation. And the BS sleeping algorithm and the green energy centralized allocation algorithm are respectively proposed to solve them. Comparing with other mechanisms, simulation results show that the proposed energy-saving management mechanism can effectively reduce 65% on-grid energy consumption while guaranteeing the quality of service (QoS) to the user equipment device (UE).
{"title":"Energy-saving management mechanism based on hybrid energy supplies in multi-operator shared LTE networks","authors":"Lu Liu, Ao Xiong, Peng Yu, Lei Feng, Wenjing Li, Xue-song Qiu, Mingxiong Wang","doi":"10.1109/NOMS.2018.8406216","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406216","url":null,"abstract":"Recently, a new opportunity for on-grid energy saving is enabled by the green network infrastructure sharing. This paper mainly investigates the collaboration between multiple operators to improve the energy utilization in this scenario. Then, an energy-saving management mechanism is proposed to reduce energy consumption and optimize energy utilization. We decompose the problem into two sub problems for base station sleeping and green energy allocation. And the BS sleeping algorithm and the green energy centralized allocation algorithm are respectively proposed to solve them. Comparing with other mechanisms, simulation results show that the proposed energy-saving management mechanism can effectively reduce 65% on-grid energy consumption while guaranteeing the quality of service (QoS) to the user equipment device (UE).","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77314754","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406165
Marc-Oliver Pahl, Markus Loipfinger
Machine Learning is recently becoming a universal problem solving tool. However, implementing machine learning (ML) into applications is difficult, time intense, and requires expert knowledge. We encapsulate machine learning as a dataoriented microservice that can simply be used to mash up applications with machine learning capabilities. To illustrate the approach we identify three machine learning algorithms that are relevant for the Internet of Things (IoT): Feed-Forward Neural Networks (FFNN), Deep Believe Networks (DBN), and Recurrent Neural Networks (RNN). We analyze those algorithm's characteristic properties and model them as configurations for dynamically linkable REST ML service modules. Our approach strictly separates the algorithm implementation from its configuration. It allows a simple extension with diverse ML algorithms. Following a service oriented design, we implement the training of our neural networks as a separate module. We evaluate how the performance of our solution compares to directly programming the chosen TensorFlow library. Our approach facilitates the implementation of ML-based data analytics significantly by enabling reuse and sharing of executables and configurations. It enables rapid prototyping and an explorative use of ML.
{"title":"Machine learning as a reusable microservice","authors":"Marc-Oliver Pahl, Markus Loipfinger","doi":"10.1109/NOMS.2018.8406165","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406165","url":null,"abstract":"Machine Learning is recently becoming a universal problem solving tool. However, implementing machine learning (ML) into applications is difficult, time intense, and requires expert knowledge. We encapsulate machine learning as a dataoriented microservice that can simply be used to mash up applications with machine learning capabilities. To illustrate the approach we identify three machine learning algorithms that are relevant for the Internet of Things (IoT): Feed-Forward Neural Networks (FFNN), Deep Believe Networks (DBN), and Recurrent Neural Networks (RNN). We analyze those algorithm's characteristic properties and model them as configurations for dynamically linkable REST ML service modules. Our approach strictly separates the algorithm implementation from its configuration. It allows a simple extension with diverse ML algorithms. Following a service oriented design, we implement the training of our neural networks as a separate module. We evaluate how the performance of our solution compares to directly programming the chosen TensorFlow library. Our approach facilitates the implementation of ML-based data analytics significantly by enabling reuse and sharing of executables and configurations. It enables rapid prototyping and an explorative use of ML.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80707213","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406246
H. Mai, Tan N. Nguyen, G. Doyen, R. Cogranne, Wissam Mallouli, Edgardo Montes de Oca, O. Festor
Named Data Networking (NDN) is the most mature proposal of the Information Centric Networking paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to tackle security issues inherent to IP networks natively, newly introduced security attacks in its transitional phase threaten NDN's practical deployment. Therefore, a security monitoring plane for NDN is indispensable before any potential deployment of this novel architecture in an operating context by any provider. We propose an approach for the monitoring and anomaly detection in NDN nodes leveraging Bayesian Network techniques. A list of monitored metrics is introduced as a quantitative measure to feature the behavior of an NDN node. By leveraging the hypothesis testing theory, a micro detector is developed to detect whenever the metric significantly changes from its normal behavior. A Bayesian network structure that correlates alarms from micro detectors is designed based on the expert knowledge of the NDN specification and the NFD implementation. The relevance and performance of our security monitoring approach are demonstrated by considering the Content Poisoning Attack (CPA), one of the most critical attacks in NDN, through numerous experiment data collected from a real NDN deployment.
{"title":"Towards a security monitoring plane for named data networking and its application against content poisoning attack","authors":"H. Mai, Tan N. Nguyen, G. Doyen, R. Cogranne, Wissam Mallouli, Edgardo Montes de Oca, O. Festor","doi":"10.1109/NOMS.2018.8406246","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406246","url":null,"abstract":"Named Data Networking (NDN) is the most mature proposal of the Information Centric Networking paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to tackle security issues inherent to IP networks natively, newly introduced security attacks in its transitional phase threaten NDN's practical deployment. Therefore, a security monitoring plane for NDN is indispensable before any potential deployment of this novel architecture in an operating context by any provider. We propose an approach for the monitoring and anomaly detection in NDN nodes leveraging Bayesian Network techniques. A list of monitored metrics is introduced as a quantitative measure to feature the behavior of an NDN node. By leveraging the hypothesis testing theory, a micro detector is developed to detect whenever the metric significantly changes from its normal behavior. A Bayesian network structure that correlates alarms from micro detectors is designed based on the expert knowledge of the NDN specification and the NFD implementation. The relevance and performance of our security monitoring approach are demonstrated by considering the Content Poisoning Attack (CPA), one of the most critical attacks in NDN, through numerous experiment data collected from a real NDN deployment.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83720092","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}
System Protection Communication Network (SPCN) is a new type of high-speed, real-time, secure and reliable communication network proposed in China supporting services such as AC/DC control, pumped storage control etc. In order to reduce the impact of SPCN failure on electric power system, this paper proposes a risk modeling and optimization approach. Firstly, we build a risk model to analyze the dynamic link and service risk from aspects of failure probability and its impact value. Then, we construct a risk optimization problem aiming at minimizing the link risk balance degree with service quality and risk constraints, and propose improved genetic algorithm to solve it. Based on part of network topology from a Chinese province, simulation results show that the proposed approach can make SPCN more reliable comparing to other methods when link failure occurs.
{"title":"Risk modeling and optimization approach for system protection communication networks","authors":"Xinting Hu, Wenjing Li, Peng Yu, Fangzheng Chen, Fangzheng Chen, Yuan Tian","doi":"10.1109/NOMS.2018.8406320","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406320","url":null,"abstract":"System Protection Communication Network (SPCN) is a new type of high-speed, real-time, secure and reliable communication network proposed in China supporting services such as AC/DC control, pumped storage control etc. In order to reduce the impact of SPCN failure on electric power system, this paper proposes a risk modeling and optimization approach. Firstly, we build a risk model to analyze the dynamic link and service risk from aspects of failure probability and its impact value. Then, we construct a risk optimization problem aiming at minimizing the link risk balance degree with service quality and risk constraints, and propose improved genetic algorithm to solve it. Based on part of network topology from a Chinese province, simulation results show that the proposed approach can make SPCN more reliable comparing to other methods when link failure occurs.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76297365","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406200
S. Madanapalli, Minzhao Lyu, Himal Kumar, H. Gharakheili, V. Sivaraman
Operators of enterprise and carrier networks in-creasingly require real-time visibility into traffic patterns in their network, so they can do better resource management (congestion detection, dynamic routing, capacity scheduling) and security protection (detection of intrusions and volumetric attacks). Of particular interest are elephant flows that transfer large volumes, since they demand most resources and can inflict most damage. Today's techniques for detecting and monitoring elephant flows are based on software-based packet analysis or hardware-based inspection, which are either unscalable or expensive. In this paper we design, implement, and evaluate an SDN-based solution that is scalable (to tens of Gigabits-per-second) and inexpensive (built using commodity OpenFlow switches). We first develop a system architecture that judiciously combines software packet inspection with hardware flow-table counters to identify and monitor heavy flows. We then use real traffic traces taken from a campus network to tune our algorithm parameters for desired trade-off between software load and hardware table size. Finally, we prototype our solution on a commodity OpenFlow hardware switch together with open-source controller and packet inspection software, and demonstrate operation at 10Gbps in a real campus network.
{"title":"Real-time detection, isolation and monitoring of elephant flows using commodity SDN system","authors":"S. Madanapalli, Minzhao Lyu, Himal Kumar, H. Gharakheili, V. Sivaraman","doi":"10.1109/NOMS.2018.8406200","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406200","url":null,"abstract":"Operators of enterprise and carrier networks in-creasingly require real-time visibility into traffic patterns in their network, so they can do better resource management (congestion detection, dynamic routing, capacity scheduling) and security protection (detection of intrusions and volumetric attacks). Of particular interest are elephant flows that transfer large volumes, since they demand most resources and can inflict most damage. Today's techniques for detecting and monitoring elephant flows are based on software-based packet analysis or hardware-based inspection, which are either unscalable or expensive. In this paper we design, implement, and evaluate an SDN-based solution that is scalable (to tens of Gigabits-per-second) and inexpensive (built using commodity OpenFlow switches). We first develop a system architecture that judiciously combines software packet inspection with hardware flow-table counters to identify and monitor heavy flows. We then use real traffic traces taken from a campus network to tune our algorithm parameters for desired trade-off between software load and hardware table size. Finally, we prototype our solution on a commodity OpenFlow hardware switch together with open-source controller and packet inspection software, and demonstrate operation at 10Gbps in a real campus network.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73989214","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406259
Ahmed Mohammed Mikaeil, Weisheng Hu
Time division multiplexing passive optical networks (TDM-PONs) technologies are viewed as an attractive solution for flexible and cost-effective mobile front-haul for dense deployment of small cells in cloud radio access network (C-RAN) architecture. Due to the high latency of upstream transmission in TDM-PON because of using a dynamic bandwidth allocation (DBA) mechanism to share the upstream bandwidth, it is a challenge for TDM-PON to meet the strict latency requirement of C-RAN mobile front-haul. Several DBA mechanisms have been proposed in the literature to address this issue for IEEE Ethernet passive optical network (i.e. 10G-EPON) based mobile front-haul. However, ITU TDM-PON such as XG-PON have not yet even been explored in the context of mobile front-haul. In this paper, we present an optimized XG-PON-compliant DBA mechanism called Optimized-Round Robin (Optimized-RR) to support front-haul traffic transport over XG-PON in virtualized small-cell C-RAN architecture. We evaluate its performance in terms of delay, jitter and packet loss over a dynamic data rate mobile front-haul traffic by comparing it with two other recently proposed XG-PON- compliant DBAs namely, Group Assured GIANT (g GIANT) and simple Round-Robin (RR-DBA) DBAs. The performance evaluation results not only show its significant improvement in terms of upstream delay and utilization, but also show a lower packet loss and jitter for aggregated small cells front-haul traffic when comparing it to gGIANT and RR-DBA.
{"title":"Optimized XG-PON DBA mechanism for front-haul upstream traffic in virtualized small cell cloud-RAN architecture","authors":"Ahmed Mohammed Mikaeil, Weisheng Hu","doi":"10.1109/NOMS.2018.8406259","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406259","url":null,"abstract":"Time division multiplexing passive optical networks (TDM-PONs) technologies are viewed as an attractive solution for flexible and cost-effective mobile front-haul for dense deployment of small cells in cloud radio access network (C-RAN) architecture. Due to the high latency of upstream transmission in TDM-PON because of using a dynamic bandwidth allocation (DBA) mechanism to share the upstream bandwidth, it is a challenge for TDM-PON to meet the strict latency requirement of C-RAN mobile front-haul. Several DBA mechanisms have been proposed in the literature to address this issue for IEEE Ethernet passive optical network (i.e. 10G-EPON) based mobile front-haul. However, ITU TDM-PON such as XG-PON have not yet even been explored in the context of mobile front-haul. In this paper, we present an optimized XG-PON-compliant DBA mechanism called Optimized-Round Robin (Optimized-RR) to support front-haul traffic transport over XG-PON in virtualized small-cell C-RAN architecture. We evaluate its performance in terms of delay, jitter and packet loss over a dynamic data rate mobile front-haul traffic by comparing it with two other recently proposed XG-PON- compliant DBAs namely, Group Assured GIANT (g GIANT) and simple Round-Robin (RR-DBA) DBAs. The performance evaluation results not only show its significant improvement in terms of upstream delay and utilization, but also show a lower packet loss and jitter for aggregated small cells front-haul traffic when comparing it to gGIANT and RR-DBA.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81621501","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 : 2018-04-23DOI: 10.1109/NOMS.2018.8406230
Felipe A. Lopes, Pablo G. S. Tiburcio, R. Bauer, S. Fernandes, M. Zitterbart
Software-Defined Networking (SDN) allows fine granular control by applications running in the network control plane, facilitating the management, orchestration, and deployment of network services. However, the diversity of application, protocols, and switches makes the task of developing applications for such networks very complex. Besides, such heterogeneity makes it harder to support the manifold requirements that may arise from different control plane applications and to verify if the underlying infrastructure satisfies the requirements from these applications. In this paper, we propose a two-phase solution for this problem, extending the Model-Driven Networking (MDN) framework for: i) enabling it to model infrastructure capabilities, so that we can verify if these capabilities could satisfy applications requirements; and ii) applying a flow delegation technique to leverage the set of network capabilities in order to support applications requirements. Our experiments demonstrate that our flow delegation mechanism not only improves the network compatibility but also achieves better bandwidth usage and jitter ratios ^22% lower when considering QoS requirements.
SDN (Software-Defined Networking)，即软件定义网络，通过运行在网络控制平面的应用程序进行精细粒度的控制，方便网络服务的管理、编排和部署。然而，应用程序、协议和交换机的多样性使得为此类网络开发应用程序的任务非常复杂。此外，这种异构性使得支持可能来自不同控制平面应用程序的多种需求以及验证底层基础设施是否满足这些应用程序的需求变得更加困难。在本文中，我们针对这个问题提出了一个两阶段的解决方案，扩展模型驱动网络(MDN)框架，以便:i)使其能够对基础设施功能建模，以便我们可以验证这些功能是否可以满足应用程序的需求;ii)应用流委托技术来利用一组网络功能以支持应用程序需求。实验表明，我们的流量委托机制不仅提高了网络兼容性，而且在考虑QoS要求的情况下，实现了更好的带宽利用率和更低22%的抖动率。
{"title":"Model-based flow delegation for improving SDN infrastructure compatibility","authors":"Felipe A. Lopes, Pablo G. S. Tiburcio, R. Bauer, S. Fernandes, M. Zitterbart","doi":"10.1109/NOMS.2018.8406230","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406230","url":null,"abstract":"Software-Defined Networking (SDN) allows fine granular control by applications running in the network control plane, facilitating the management, orchestration, and deployment of network services. However, the diversity of application, protocols, and switches makes the task of developing applications for such networks very complex. Besides, such heterogeneity makes it harder to support the manifold requirements that may arise from different control plane applications and to verify if the underlying infrastructure satisfies the requirements from these applications. In this paper, we propose a two-phase solution for this problem, extending the Model-Driven Networking (MDN) framework for: i) enabling it to model infrastructure capabilities, so that we can verify if these capabilities could satisfy applications requirements; and ii) applying a flow delegation technique to leverage the set of network capabilities in order to support applications requirements. Our experiments demonstrate that our flow delegation mechanism not only improves the network compatibility but also achieves better bandwidth usage and jitter ratios ^22% lower when considering QoS requirements.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77113503","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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