Pub Date : 2020-11-10DOI: 10.1109/nfv-sdn50289.2020.9289889
Timothy Culver, Krishna Kadiyala, F. Ozog, Roland Picard, Leo Popokh
Defined Networks: A Comprehensive Approach”
定义网络:综合方法”
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The advantages of Network Function Virtualization (NFV) have attracted many use cases ranging from virtual Customer Premises Equipment (vCPE) to virtual Radio Access Network (vRAN) and virtual Evolved Packet Core (vEPC). Fast packet processing libraries such as Data Plane Development Kit (DPDK) are necessary to enable NFV. Currently, DPDK provides a framework for Quality of Service (QoS) which is used for queue management, traffic shaping and policing, but it lacks a general purpose queue management framework. In this paper, we propose DPDK-FQM, a framework to implement queue management algorithms in DPDK, run them and collect the desired statistics. Subsequently, we implement Proportional Integral controller Enhanced (PIE) and Controlled Delay (CoDel) queue management algorithms by using the proposed framework. We develop a new DPDK application to demonstrate the usage of APIs in DPDK-FQM, and verify the correctness of the framework and implementations of PIE and CoDel. Our experiments on a high speed network testbed show that PIE and CoDel exhibit their key characteristics by controlling the queue delay at a desired target, while fully utilizing the bottleneck bandwidth.
网络功能虚拟化(NFV)的优势吸引了许多用例，从虚拟客户端设备(vCPE)到虚拟无线接入网(vRAN)和虚拟演进分组核心(vEPC)。数据平面开发工具包(Data Plane Development Kit, DPDK)等快速数据包处理库是实现NFV的必要条件。目前，DPDK提供了用于队列管理、流量整形和监管的服务质量(QoS)框架，但缺乏通用的队列管理框架。在本文中，我们提出了DPDK- fqm框架来实现DPDK中的队列管理算法，并运行它们并收集所需的统计信息。随后，我们利用所提出的框架实现了比例积分控制器增强(PIE)和控制延迟(CoDel)队列管理算法。我们开发了一个新的DPDK应用程序来演示DPDK- fqm中api的使用，并验证了PIE和CoDel的框架和实现的正确性。我们在高速网络试验台上的实验表明，PIE和CoDel在充分利用瓶颈带宽的同时，能够在期望的目标上控制队列延迟，从而显示出它们的关键特性。
{"title":"DPDK-FQM: Framework for Queue Management Algorithms in DPDK","authors":"Archit Pandey, Gokul Bargaje, Avinash, Sanjana Krishnam, Tarun Anand, Leslie Monis, M. Tahiliani","doi":"10.1109/NFV-SDN50289.2020.9289914","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289914","url":null,"abstract":"The advantages of Network Function Virtualization (NFV) have attracted many use cases ranging from virtual Customer Premises Equipment (vCPE) to virtual Radio Access Network (vRAN) and virtual Evolved Packet Core (vEPC). Fast packet processing libraries such as Data Plane Development Kit (DPDK) are necessary to enable NFV. Currently, DPDK provides a framework for Quality of Service (QoS) which is used for queue management, traffic shaping and policing, but it lacks a general purpose queue management framework. In this paper, we propose DPDK-FQM, a framework to implement queue management algorithms in DPDK, run them and collect the desired statistics. Subsequently, we implement Proportional Integral controller Enhanced (PIE) and Controlled Delay (CoDel) queue management algorithms by using the proposed framework. We develop a new DPDK application to demonstrate the usage of APIs in DPDK-FQM, and verify the correctness of the framework and implementations of PIE and CoDel. Our experiments on a high speed network testbed show that PIE and CoDel exhibit their key characteristics by controlling the queue delay at a desired target, while fully utilizing the bottleneck bandwidth.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121429448","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289896
Mishal Shah, Mehnaz Yunus, Pavan Vachhani, Leslie Monis, M. Tahiliani, B. Talawar
Data Plane Development Kit (DPDK) provides a set of libraries for fast packet processing that allow applications in the user space to directly interact with the NIC. Currently, DPDK provides a power management library that enables the applications to save power. However, it lacks features to effectively measure the power consumption of the system. In this paper we propose PowerDPDK, a software-based real-time library to measure the power consumption of DPDK applications. PowerDPDK leverages the Running Average Power Limit (RAPL) feature available on modern Intel processors to provide the power consumed by the CPU package and DRAM. We discuss the architecture of PowerDPDK and describe the process to incorporate it into DPDK applications. Subsequently, we use PowerDPDK to measure the power consumption of a few sample DPDK applications and a chain of Virtual Network Functions (VNFs) in OpenNetVM, a high-performance container-based platform for Network Function Virtualization (NFV). We show that a major share of the power consumed by DPDK is due to the use of Poll Mode Drivers (PMD), and hence, even a simple Layer 2 forwarding application consumes a large amount of power.
数据平面开发工具包(Data Plane Development Kit, DPDK)提供了一组用于快速数据包处理的库，允许用户空间中的应用程序直接与NIC交互。目前，DPDK提供了一个电源管理库，使应用程序能够节省电力。但缺乏有效测量系统功耗的特性。在本文中，我们提出了一个基于软件的实时库PowerDPDK来测量DPDK应用程序的功耗。PowerDPDK利用现代英特尔处理器上可用的运行平均功率限制(RAPL)功能来提供CPU封装和DRAM所消耗的功率。我们讨论了PowerDPDK的体系结构，并描述了将其合并到DPDK应用程序中的过程。随后，我们使用PowerDPDK来测量OpenNetVM中几个示例DPDK应用程序和虚拟网络功能链(VNFs)的功耗，OpenNetVM是一个基于高性能容器的网络功能虚拟化(NFV)平台。我们表明，DPDK消耗的大部分功率是由于使用了轮询模式驱动程序(PMD)，因此，即使是简单的第二层转发应用程序也会消耗大量功率。
{"title":"PowerDPDK: Software-Based Real-Time Power Measurement for DPDK Applications","authors":"Mishal Shah, Mehnaz Yunus, Pavan Vachhani, Leslie Monis, M. Tahiliani, B. Talawar","doi":"10.1109/NFV-SDN50289.2020.9289896","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289896","url":null,"abstract":"Data Plane Development Kit (DPDK) provides a set of libraries for fast packet processing that allow applications in the user space to directly interact with the NIC. Currently, DPDK provides a power management library that enables the applications to save power. However, it lacks features to effectively measure the power consumption of the system. In this paper we propose PowerDPDK, a software-based real-time library to measure the power consumption of DPDK applications. PowerDPDK leverages the Running Average Power Limit (RAPL) feature available on modern Intel processors to provide the power consumed by the CPU package and DRAM. We discuss the architecture of PowerDPDK and describe the process to incorporate it into DPDK applications. Subsequently, we use PowerDPDK to measure the power consumption of a few sample DPDK applications and a chain of Virtual Network Functions (VNFs) in OpenNetVM, a high-performance container-based platform for Network Function Virtualization (NFV). We show that a major share of the power consumed by DPDK is due to the use of Poll Mode Drivers (PMD), and hence, even a simple Layer 2 forwarding application consumes a large amount of power.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129269424","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289880
M. Kourtis, Themis Anagnostopoulos, S. Kukliński, M. Wierzbicki, Andreas Oikonomakis, G. Xilouris, I. Chochliouros, N. Yi, A. Kostopoulos, Lechosław Tomaszewski, Thanos Sarlas, H. Koumaras
Network slicing already plays an important role as a critical enabler in the current 5G technology domain. 5G aims to disrupt and accelerate innovation in various vertical fields, among those is the vehicular industry. In the detailed scope of the 5G-DRIVE research project promoting cooperation between the EU and China, a set of trials is to be undertaken towards promoting 5G growth. In this paper, initially, we identify a variety of challenges arising from the 5G convergence to the automotive industry. Then we describe the specific innovative framework of the 5G-DRIVE research, together with a novel network slicing mechanism deployed at the edge. Then an analysis on the corresponding architectures is presented, and how they operate in a set of trials for new 5G services. The services described are a virtualized caching network function (vCache), and a deep packet inspection one (vDPI), which are deployed at the edge facilitating an edge 5G service. For each case, the services are deployed and evaluated in the 5G Drive platform using the Katana slicing framework. Additional analysis of the OSM slicing platform is presented. The results demonstrate the performance of a network slicing mechanism for 5G service deployments in an edge enabled platform.
{"title":"5G Network Slicing Enabling Edge Services","authors":"M. Kourtis, Themis Anagnostopoulos, S. Kukliński, M. Wierzbicki, Andreas Oikonomakis, G. Xilouris, I. Chochliouros, N. Yi, A. Kostopoulos, Lechosław Tomaszewski, Thanos Sarlas, H. Koumaras","doi":"10.1109/NFV-SDN50289.2020.9289880","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289880","url":null,"abstract":"Network slicing already plays an important role as a critical enabler in the current 5G technology domain. 5G aims to disrupt and accelerate innovation in various vertical fields, among those is the vehicular industry. In the detailed scope of the 5G-DRIVE research project promoting cooperation between the EU and China, a set of trials is to be undertaken towards promoting 5G growth. In this paper, initially, we identify a variety of challenges arising from the 5G convergence to the automotive industry. Then we describe the specific innovative framework of the 5G-DRIVE research, together with a novel network slicing mechanism deployed at the edge. Then an analysis on the corresponding architectures is presented, and how they operate in a set of trials for new 5G services. The services described are a virtualized caching network function (vCache), and a deep packet inspection one (vDPI), which are deployed at the edge facilitating an edge 5G service. For each case, the services are deployed and evaluated in the 5G Drive platform using the Katana slicing framework. Additional analysis of the OSM slicing platform is presented. The results demonstrate the performance of a network slicing mechanism for 5G service deployments in an edge enabled platform.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128653624","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289898
Vitor A. Cunha, Daniel Corujo, J. Barraca, R. Aguiar
Slice-based Network Control allows the delivery of different SLAs to heterogeneous services and the isolation of network flows, all within the same shared infrastructure. Industry 4.0 and the IoT are prime use-cases for Network Slicing and expose a large number of embedded systems that cannot run advanced anti-malware routines - this raises significant security concerns. An approach to defending against these issues is honeynets, isolated sandbox networks with decoy functions (honeypots) mimicking the real endpoints. However, steering an active TCP connection (i.e., the attack) to a different endpoint (i.e., the decoy) is still a significant challenge. This article proposes using the SDN controller to bootstrap a smooth handover of the active TCP session across endpoints. Our proposal's core is a purpose-built proxy function that will resume a live attack session with the decoy using the Linux Kernel's TCP-REPAIR features. Because we are effectively recreating the socket as if the connection was initially established with that new endpoint, all of the TCP state machine and control sequence inner-workings are still done seamlessly by the kernel's built-in routines and the higher-level abstractions that use them. The results show that our approach has a similar performance to a regular socket (latency and throughput), while the new management interfaces integrate nicely into the existing Network Slicing operations.
{"title":"Using Linux TCP connection repair for mid-session endpoint handover: a security enhancement use-case","authors":"Vitor A. Cunha, Daniel Corujo, J. Barraca, R. Aguiar","doi":"10.1109/NFV-SDN50289.2020.9289898","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289898","url":null,"abstract":"Slice-based Network Control allows the delivery of different SLAs to heterogeneous services and the isolation of network flows, all within the same shared infrastructure. Industry 4.0 and the IoT are prime use-cases for Network Slicing and expose a large number of embedded systems that cannot run advanced anti-malware routines - this raises significant security concerns. An approach to defending against these issues is honeynets, isolated sandbox networks with decoy functions (honeypots) mimicking the real endpoints. However, steering an active TCP connection (i.e., the attack) to a different endpoint (i.e., the decoy) is still a significant challenge. This article proposes using the SDN controller to bootstrap a smooth handover of the active TCP session across endpoints. Our proposal's core is a purpose-built proxy function that will resume a live attack session with the decoy using the Linux Kernel's TCP-REPAIR features. Because we are effectively recreating the socket as if the connection was initially established with that new endpoint, all of the TCP state machine and control sequence inner-workings are still done seamlessly by the kernel's built-in routines and the higher-level abstractions that use them. The results show that our approach has a similar performance to a regular socket (latency and throughput), while the new management interfaces integrate nicely into the existing Network Slicing operations.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587563","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289894
Beny Nugraha, Rathan Narasimha Murthy
Software-Defined Networking (SDN) is a promising networking paradigm that provides outstanding manageability, scalability, controllability, and flexibility. Despite having such promising features, SDN is not intrinsically secure. For instance, it still suffers from Denial of Service (DDoS) attacks, which is one of the major threats that compromise the availability of the network. One type of DDoS attacks, that is considered as one of the most challenging to be detected, are slow DDoS attacks. In recent years, deep learning algorithms have been applied for reliable and highly accurate traffic anomaly detection. Therefore, in this paper, we propose the use of a hybrid Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) model to detect slow DDoS attacks in SDN-based networks. The performance of this method is evaluated based on custom datasets. The obtained results are quite impressive - all considered performance metrics are above 99%. Our hybrid CNN-LSTM model also outperforms other deep learning models like MultiLayer Perceptron (MLP) and standard machine learning models like l-Class Support Vector Machines (l-Class SVM).
{"title":"Deep Learning-based Slow DDoS Attack Detection in SDN-based Networks","authors":"Beny Nugraha, Rathan Narasimha Murthy","doi":"10.1109/NFV-SDN50289.2020.9289894","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289894","url":null,"abstract":"Software-Defined Networking (SDN) is a promising networking paradigm that provides outstanding manageability, scalability, controllability, and flexibility. Despite having such promising features, SDN is not intrinsically secure. For instance, it still suffers from Denial of Service (DDoS) attacks, which is one of the major threats that compromise the availability of the network. One type of DDoS attacks, that is considered as one of the most challenging to be detected, are slow DDoS attacks. In recent years, deep learning algorithms have been applied for reliable and highly accurate traffic anomaly detection. Therefore, in this paper, we propose the use of a hybrid Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) model to detect slow DDoS attacks in SDN-based networks. The performance of this method is evaluated based on custom datasets. The obtained results are quite impressive - all considered performance metrics are above 99%. Our hybrid CNN-LSTM model also outperforms other deep learning models like MultiLayer Perceptron (MLP) and standard machine learning models like l-Class Support Vector Machines (l-Class SVM).","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133326479","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289875
José Takeru Infiesta, Carlos Guimarães, L. Contreras, A. D. Oliva
5G and Edge computing are two technologies set to impose a paradigm shift from today's traditional networking solutions. In particular, transport networks, which connect distinct computing infrastructures, must guarantee a wide range of performance requirements from coexisting network services. 5G network slicing enables such capability by providing the flexibility to support multiple and isolated virtual networks over the same and shared infrastructure. This paper introduces the GST And Network Slice Operator (GANSO) framework for automating the creation of network slices over SDN architectures, focusing on transport networks interconnecting Edge data centers. To characterise the type of network slice to be deployed, it uses Generic network Slice Templates (GSTs). Initially, five GST attributes are implemented in a proof-of-concept prototype, namely through configurable User Data Access and Rate Limit parameters. It is then validated in a scenario considering the instantiation of network slices over the transport network for different virtual applications hosted across the edge-to-cloud continuum.
{"title":"GANSO: Automate Network Slicing at the Transport Network Interconnecting the Edge","authors":"José Takeru Infiesta, Carlos Guimarães, L. Contreras, A. D. Oliva","doi":"10.1109/NFV-SDN50289.2020.9289875","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289875","url":null,"abstract":"5G and Edge computing are two technologies set to impose a paradigm shift from today's traditional networking solutions. In particular, transport networks, which connect distinct computing infrastructures, must guarantee a wide range of performance requirements from coexisting network services. 5G network slicing enables such capability by providing the flexibility to support multiple and isolated virtual networks over the same and shared infrastructure. This paper introduces the GST And Network Slice Operator (GANSO) framework for automating the creation of network slices over SDN architectures, focusing on transport networks interconnecting Edge data centers. To characterise the type of network slice to be deployed, it uses Generic network Slice Templates (GSTs). Initially, five GST attributes are implemented in a proof-of-concept prototype, namely through configurable User Data Access and Rate Limit parameters. It is then validated in a scenario considering the instantiation of network slices over the transport network for different virtual applications hosted across the edge-to-cloud continuum.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132281536","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289888
Yang Zhang, Zhi-Li Zhang
In an era of ubiquitous connectivity, various new applications, network protocols, and online services (e.g., cloud services, distributed machine learning, cryptocurrency) have been constantly creating, underpinning many of our daily activities. Emerging demands for networks have led to growing traffic volume and complexity of modern networks, which heavily rely on a wide spectrum of specialized network functions (e.g., Firewall, Load Balancer) for diverse purposes. Although these (virtual) network functions (VNFs) are widely deployed, they are instantiated in an uncoordinated manner failing to meet growing demands of evolving networks. In this dissertation, we argue that networks equipped with VNFs can be designed in a fashion similar to how computer software is programmed today. By following the blueprint of modularization, networks can be made more efficient, secure, and manageable.
{"title":"Enhancing Performance, Security, and Management in Network Function Virtualization","authors":"Yang Zhang, Zhi-Li Zhang","doi":"10.1109/NFV-SDN50289.2020.9289888","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289888","url":null,"abstract":"In an era of ubiquitous connectivity, various new applications, network protocols, and online services (e.g., cloud services, distributed machine learning, cryptocurrency) have been constantly creating, underpinning many of our daily activities. Emerging demands for networks have led to growing traffic volume and complexity of modern networks, which heavily rely on a wide spectrum of specialized network functions (e.g., Firewall, Load Balancer) for diverse purposes. Although these (virtual) network functions (VNFs) are widely deployed, they are instantiated in an uncoordinated manner failing to meet growing demands of evolving networks. In this dissertation, we argue that networks equipped with VNFs can be designed in a fashion similar to how computer software is programmed today. By following the blueprint of modularization, networks can be made more efficient, secure, and manageable.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114580517","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 : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289859
Ali Kelkawi, Ameer Mohammed, Anwar Alyatama
The recent introduction of Software Defined Networks (SDN) into the traditional networking paradigm to create hybrid SDN networks brings with it several economical, technical and organizational challenges which must be addressed. In deploying hybrid SDN networks, the maintenance of numerous factors is taken into consideration such as throughput, network traffic, load balancing and fast failure recovery. One strategy that has been suggested is the incremental deployment of SDN controllers alongside legacy networking systems to reap the benefits of both paradigms while minimizing disruptions to networking services and maintaining network performance from the perspective of traffic engineering. In this paper, we seek to explore an optimal incremental deployment sequence of legacy networking devices to programmable SDN switches based on traffic engineering measures, namely minimizing maximum link utilization, thus determining the most suitable devices to migrate. A combination of two metaheuristics algorithms (Particle Swarm Optimization and Ant Colony Optimization) are implemented to identify this optimal sequence in terms of the locations of routers to be migrated along with the optimal weight setting and flow split ratios at each stage of migration. The deployment sequence is simulated and compared with static migration algorithms for evaluation.
{"title":"Incremental Deployment of Hybrid IP/SDN Network with Optimized Traffic Engineering","authors":"Ali Kelkawi, Ameer Mohammed, Anwar Alyatama","doi":"10.1109/NFV-SDN50289.2020.9289859","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289859","url":null,"abstract":"The recent introduction of Software Defined Networks (SDN) into the traditional networking paradigm to create hybrid SDN networks brings with it several economical, technical and organizational challenges which must be addressed. In deploying hybrid SDN networks, the maintenance of numerous factors is taken into consideration such as throughput, network traffic, load balancing and fast failure recovery. One strategy that has been suggested is the incremental deployment of SDN controllers alongside legacy networking systems to reap the benefits of both paradigms while minimizing disruptions to networking services and maintaining network performance from the perspective of traffic engineering. In this paper, we seek to explore an optimal incremental deployment sequence of legacy networking devices to programmable SDN switches based on traffic engineering measures, namely minimizing maximum link utilization, thus determining the most suitable devices to migrate. A combination of two metaheuristics algorithms (Particle Swarm Optimization and Ant Colony Optimization) are implemented to identify this optimal sequence in terms of the locations of routers to be migrated along with the optimal weight setting and flow split ratios at each stage of migration. The deployment sequence is simulated and compared with static migration algorithms for evaluation.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718512","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 : 2020-11-10DOI: 10.1109/nfv-sdn50289.2020.9289853
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