Pub Date : 2020-11-10DOI: 10.1109/NFV-SDN50289.2020.9289837
Amir Mohamad, H. Hassanein
Edge computing resources deployed at the access network are distributed and limited compared to the abundant core cloud resources. With the increasing demand on edge resources by emerging delay-sensitive use-cases, efficient resource utilization is to play an indispensable role. Taking advantage of operation dynamics and common functions in network/enterprise services, we propose PSVShare a priority-based, least-cost and resource-efficient service placement algorithm. The algorithm considers practical settings and conditions such as service categories (premium and best-effort), service arrival and completion times, and service relocation/migration due to changing traffic loads. PSVShare satisfies more services, achieves lower rejection rates, and is agnostic to arrival order and ratio of premium to best-effort services.
{"title":"PSVShare: A Priority-based SFC placement with VNF Sharing","authors":"Amir Mohamad, H. Hassanein","doi":"10.1109/NFV-SDN50289.2020.9289837","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289837","url":null,"abstract":"Edge computing resources deployed at the access network are distributed and limited compared to the abundant core cloud resources. With the increasing demand on edge resources by emerging delay-sensitive use-cases, efficient resource utilization is to play an indispensable role. Taking advantage of operation dynamics and common functions in network/enterprise services, we propose PSVShare a priority-based, least-cost and resource-efficient service placement algorithm. The algorithm considers practical settings and conditions such as service categories (premium and best-effort), service arrival and completion times, and service relocation/migration due to changing traffic loads. PSVShare satisfies more services, achieves lower rejection rates, and is agnostic to arrival order and ratio of premium to best-effort services.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"66 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":"115212170","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-SDN50289.2020.9289916
Christian Wernecke, Helge Parzyjegla, Gero Mühl, P. Danielis, E. Schweissguth, D. Timmermann
Publish/subscribe is a flexible communication pattern for loosely coupled distributed applications. The content-based variant matches each published notification against active subscriptions to determine a set of interested subscribers to which the notification is to be delivered. Since the recipient set can be different for each notification, it is challenging to find and install profitable forwarding rules on the network switches. In this paper, we present novel notification forwarding schemes implemented in P4 that use virtual trees (VTs) installed on switches and additional forwarding information encoded in notification packets that is used to connect VTs, to extend VT branches, or to cut off VT subtrees. For deriving beneficial VTs, we consider (i) topological properties of the physical network, (ii) publisher/subscriber relationships, and (iii) notification statistics. We present a generic algorithm for encoding distribution trees and evaluate our forwarding schemes in a data center network. The results show that our schemes perform well and save network bandwidth by reducing the notification header length.
{"title":"Stitching Notification Distribution Trees for Content-based Publish/Subscribe with P4","authors":"Christian Wernecke, Helge Parzyjegla, Gero Mühl, P. Danielis, E. Schweissguth, D. Timmermann","doi":"10.1109/NFV-SDN50289.2020.9289916","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289916","url":null,"abstract":"Publish/subscribe is a flexible communication pattern for loosely coupled distributed applications. The content-based variant matches each published notification against active subscriptions to determine a set of interested subscribers to which the notification is to be delivered. Since the recipient set can be different for each notification, it is challenging to find and install profitable forwarding rules on the network switches. In this paper, we present novel notification forwarding schemes implemented in P4 that use virtual trees (VTs) installed on switches and additional forwarding information encoded in notification packets that is used to connect VTs, to extend VT branches, or to cut off VT subtrees. For deriving beneficial VTs, we consider (i) topological properties of the physical network, (ii) publisher/subscriber relationships, and (iii) notification statistics. We present a generic algorithm for encoding distribution trees and evaluate our forwarding schemes in a data center network. The results show that our schemes perform well and save network bandwidth by reducing the notification header length.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"38 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120813450","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-SDN50289.2020.9289872
Danny Lachos Perez, Christian Esteve Rothenberg
The evolving Internet application landscape is envisioned to adopt technologies such as SDN, NFV, and MEC to provide softwarized services, requiring resource orchestration across multiple networks managed by different technological and administrative domains. In such multi-domain settings, the collaboration between networks and applications provides opportunities to both applications to improve their performances and network service providers to increase their business offerings. Although many systems are proposed to support such collaborations, they are point or incremental solutions. In this work, we propose the exploration of a more integrated architecture with huge possibilities taking a network-application integration (NAI) approach. Specifically, we explore the NAI possibilities in two concrete aspects: application-aware networking and network-aware applications. We review recent progress in these two aspects, and identify the key barriers in systematically realizing such a deep integration. To address these barriers, we propose a generic multi-domain NAI exposure and discovery framework, called MUDED. Through different systematic analysis and demonstrated prototypes, the MUDED's key components showcase: the maturing of the IETF ALTO protocol on the road to becoming a generic NAI possibilities discovery and exposure mechanism; and the optimized network view generation to simplify the network service placement and management.
{"title":"MUDED: Integrating Networks with Applications through Multi-Domain Exposure and Discovery Mechanisms","authors":"Danny Lachos Perez, Christian Esteve Rothenberg","doi":"10.1109/NFV-SDN50289.2020.9289872","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289872","url":null,"abstract":"The evolving Internet application landscape is envisioned to adopt technologies such as SDN, NFV, and MEC to provide softwarized services, requiring resource orchestration across multiple networks managed by different technological and administrative domains. In such multi-domain settings, the collaboration between networks and applications provides opportunities to both applications to improve their performances and network service providers to increase their business offerings. Although many systems are proposed to support such collaborations, they are point or incremental solutions. In this work, we propose the exploration of a more integrated architecture with huge possibilities taking a network-application integration (NAI) approach. Specifically, we explore the NAI possibilities in two concrete aspects: application-aware networking and network-aware applications. We review recent progress in these two aspects, and identify the key barriers in systematically realizing such a deep integration. To address these barriers, we propose a generic multi-domain NAI exposure and discovery framework, called MUDED. Through different systematic analysis and demonstrated prototypes, the MUDED's key components showcase: the maturing of the IETF ALTO protocol on the road to becoming a generic NAI possibilities discovery and exposure mechanism; and the optimized network view generation to simplify the network service placement and management.","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":"129178286","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-SDN50289.2020.9289908
Samer Y. Khamaiseh, I. Alsmadi, Abdullah Al-Alaj
Recently, different machine learning-based detection systems are proposed to detect DDoS saturation attacks in Software-defined Networking (SDN). Meanwhile, different research studies highlight the vulnerabilities of adapting such systems in SDN. For instance, an adversary can fool the machine learning classifiers of these systems by crafting specific adversarial attack samples, preventing the detection of DoS saturation attacks. To better understand the security properties of these classifiers in adversarial settings, this paper investigates the robustness of the supervised and unsupervised machine learning classifiers against adversarial attacks. First, we propose an adversarial testing tool that can generate adversarial attacks that avoid the detection of four saturation attacks (i.e., SYN, UDP, ICMP, and TCP-SARFU), by perturbing different traffic features. Second, we propose a machine learning-based saturation attack detection system that utilizes different supervised and unsupervised machine learning classifiers as a testing platform. The experimental results demonstrate that the generated adversarial attacks can reduce the detection performance of the proposed detection system dramatically. Specifically, the detection performance of the four saturation attacks was decreased by more than 90% across several machine learning classifiers. This indicates that the proposed adversarial testing tool can effectively compromise the machine learning-based saturation attack detection systems.
{"title":"Deceiving Machine Learning-Based Saturation Attack Detection Systems in SDN","authors":"Samer Y. Khamaiseh, I. Alsmadi, Abdullah Al-Alaj","doi":"10.1109/NFV-SDN50289.2020.9289908","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289908","url":null,"abstract":"Recently, different machine learning-based detection systems are proposed to detect DDoS saturation attacks in Software-defined Networking (SDN). Meanwhile, different research studies highlight the vulnerabilities of adapting such systems in SDN. For instance, an adversary can fool the machine learning classifiers of these systems by crafting specific adversarial attack samples, preventing the detection of DoS saturation attacks. To better understand the security properties of these classifiers in adversarial settings, this paper investigates the robustness of the supervised and unsupervised machine learning classifiers against adversarial attacks. First, we propose an adversarial testing tool that can generate adversarial attacks that avoid the detection of four saturation attacks (i.e., SYN, UDP, ICMP, and TCP-SARFU), by perturbing different traffic features. Second, we propose a machine learning-based saturation attack detection system that utilizes different supervised and unsupervised machine learning classifiers as a testing platform. The experimental results demonstrate that the generated adversarial attacks can reduce the detection performance of the proposed detection system dramatically. Specifically, the detection performance of the four saturation attacks was decreased by more than 90% across several machine learning classifiers. This indicates that the proposed adversarial testing tool can effectively compromise the machine learning-based saturation attack detection systems.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"88 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":"129181220","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-SDN50289.2020.9289852
Asma Islam Swapna, R. V. Rosa, Christian Esteve Rothenberg, R. Pasquini, J. Baliosian
The concept of network slicing plays a thriving role as 5G rolls out business models vouched by different stakeholders. The dynamic and variable characterization of end-to-end cloud-network slices encompasses the composition of different slice parts laying at different administrative domains. Following a profit-maximizing Slice-as-a-Service (SaaS) model, such a multi-domain facet offers promising business opportunities in support of diverse vertical industries, rendering to network slicing marketplace members the roles of Infrastructure Provider, Slice Provider, and Tenants. The effective realization of SaaS approaches introduces a dynamic resource allocation problem, manifested as challenging run-time decisions upon on-demand slice part requests. The Orchestrator is hence responsible to perform an optimized decision on-the-fly on which elasticity requests to address based on an orchestration policy defined within the context of Network Slice architecture for the followed revenue model. This paper presents a slice management strategy for such an orchestrator can follow, based on reinforcement learning, able to efficiently orchestrate slice elasticity requests to comprehend the maximum revenue for the stakeholders of end-to-end network slice lifecycle. The proposed strategy orients a Slice Orchestrator to learn which slice requests to address as per availability of the required resources at the different participating Infrastructure Providers. The experimental results show the Reinforcement Learning based Orchestrator outperforms several benchmark heuristics focused on revenue maximization.
{"title":"Policy Controlled Multi-domain cloud-network Slice Orchestration Strategy based on Reinforcement Learning","authors":"Asma Islam Swapna, R. V. Rosa, Christian Esteve Rothenberg, R. Pasquini, J. Baliosian","doi":"10.1109/NFV-SDN50289.2020.9289852","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289852","url":null,"abstract":"The concept of network slicing plays a thriving role as 5G rolls out business models vouched by different stakeholders. The dynamic and variable characterization of end-to-end cloud-network slices encompasses the composition of different slice parts laying at different administrative domains. Following a profit-maximizing Slice-as-a-Service (SaaS) model, such a multi-domain facet offers promising business opportunities in support of diverse vertical industries, rendering to network slicing marketplace members the roles of Infrastructure Provider, Slice Provider, and Tenants. The effective realization of SaaS approaches introduces a dynamic resource allocation problem, manifested as challenging run-time decisions upon on-demand slice part requests. The Orchestrator is hence responsible to perform an optimized decision on-the-fly on which elasticity requests to address based on an orchestration policy defined within the context of Network Slice architecture for the followed revenue model. This paper presents a slice management strategy for such an orchestrator can follow, based on reinforcement learning, able to efficiently orchestrate slice elasticity requests to comprehend the maximum revenue for the stakeholders of end-to-end network slice lifecycle. The proposed strategy orients a Slice Orchestrator to learn which slice requests to address as per availability of the required resources at the different participating Infrastructure Providers. The experimental results show the Reinforcement Learning based Orchestrator outperforms several benchmark heuristics focused on revenue maximization.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"24 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":"116493549","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-SDN50289.2020.9289849
Alexandre Huff, Giovanni Venâncio, Vinicius Fulber Garcia, E. P. Duarte
Service Function Chains (SFCs) are compositions of Virtual Network Functions (VNFs) designed to provide complex network services. In this work, we propose a strategy to build an SFC across multiple domains and multiple clouds using multiple NFV platforms, which we call a Multi-SFC. To the best of our knowledge, this is the first solution to allow an SFC to be built across multiple different orchestrators - although there are other solutions for multiple domains and clouds. The basic building block of the proposed strategy is the SFC segment, in which all VNFs are connected within a single cloud/domain/platform. A pair of different segments is interconnected through a VNF tunnel that consists of a pair of VNFs, each interfacing one of the connected segments. A tunnel can be implemented with different technologies such as a VPN or VXLAN. The main advantage of the Multi-SFC strategy is that it is a holistic approach that allows operators to deploy SFCs on multiple clouds/domains/platforms without having to deal with a myriad of minute details required to configure and interconnect the different underlying technologies. A prototype was implemented as a proof of concept and experimental results are presented.
{"title":"Building Multi-domain Service Function Chains Based on Multiple NFV Orchestrators","authors":"Alexandre Huff, Giovanni Venâncio, Vinicius Fulber Garcia, E. P. Duarte","doi":"10.1109/NFV-SDN50289.2020.9289849","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289849","url":null,"abstract":"Service Function Chains (SFCs) are compositions of Virtual Network Functions (VNFs) designed to provide complex network services. In this work, we propose a strategy to build an SFC across multiple domains and multiple clouds using multiple NFV platforms, which we call a Multi-SFC. To the best of our knowledge, this is the first solution to allow an SFC to be built across multiple different orchestrators - although there are other solutions for multiple domains and clouds. The basic building block of the proposed strategy is the SFC segment, in which all VNFs are connected within a single cloud/domain/platform. A pair of different segments is interconnected through a VNF tunnel that consists of a pair of VNFs, each interfacing one of the connected segments. A tunnel can be implemented with different technologies such as a VPN or VXLAN. The main advantage of the Multi-SFC strategy is that it is a holistic approach that allows operators to deploy SFCs on multiple clouds/domains/platforms without having to deal with a myriad of minute details required to configure and interconnect the different underlying technologies. A prototype was implemented as a proof of concept and experimental results are presented.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"10 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":"124733384","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-SDN50289.2020.9289863
J. Baranda, J. Mangues‐Bafalluy, E. Zeydan, L. Vettori, R. Martínez, Xi Li, Andres Garcia-Saavedra, C. Chiasserini, C. Casetti, Konstantin Tomakh, O. Kolodiazhnyi, C. Bernardos
The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1–2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).
{"title":"On the Integration of AI/ML-based scaling operations in the 5Growth platform","authors":"J. Baranda, J. Mangues‐Bafalluy, E. Zeydan, L. Vettori, R. Martínez, Xi Li, Andres Garcia-Saavedra, C. Chiasserini, C. Casetti, Konstantin Tomakh, O. Kolodiazhnyi, C. Bernardos","doi":"10.1109/NFV-SDN50289.2020.9289863","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289863","url":null,"abstract":"The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1–2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"1 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":"129383965","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-sdn50289.2020.9289905
FoFoSDN
There are several contemporary examples of network failures, particularly in the domain of SDNs, that led to substantial loss for businesses. Formal methods are promised to address performance, reliability, and security issues of SDNs using the rigour provided by their underlying mathematics. The focus of this workshop is on formal methods aimed to enable the formal specification and verification of SDNs and NFVs. We aim at bringing together leading researchers and practitioners from the fields of formal methods, networking, programming languages, and security, to investigate the task of developing formal foundations for networks. The topics of interest include, but are not restricted to: foundations (including formal semantics of) programming languages, formal specification languages, formal verification techniques, synthesis and correct-by-construction methods, and testing, debugging, monitoring and other forms of non-exhaustive validation and verification.
{"title":"Formal Foundations of Software Defined Networks (FoFoSDN 2020)","authors":"FoFoSDN","doi":"10.1109/nfv-sdn50289.2020.9289905","DOIUrl":"https://doi.org/10.1109/nfv-sdn50289.2020.9289905","url":null,"abstract":"There are several contemporary examples of network failures, particularly in the domain of SDNs, that led to substantial loss for businesses. Formal methods are promised to address performance, reliability, and security issues of SDNs using the rigour provided by their underlying mathematics. The focus of this workshop is on formal methods aimed to enable the formal specification and verification of SDNs and NFVs. We aim at bringing together leading researchers and practitioners from the fields of formal methods, networking, programming languages, and security, to investigate the task of developing formal foundations for networks. The topics of interest include, but are not restricted to: foundations (including formal semantics of) programming languages, formal specification languages, formal verification techniques, synthesis and correct-by-construction methods, and testing, debugging, monitoring and other forms of non-exhaustive validation and verification.","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":"131044356","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-SDN50289.2020.9289883
Andrés Cárdenas, D. Fernández
The use of Network Function Virtualization (NFV) and Software-defined Networks (SDN) technologies in 5G networks provide Mobile Network Operators (MNOs) with new capacities to deploy and orchestrate network functions in a virtualized and programmable way, allowing them in turn to better provide and support new connectivity requirements demanded by users and vertical industries. The Network Slicing function provides the base functionality to virtualize the network and efficiently share it among customers. Besides, the extensive use of virtualization technologies in 5G provides a good support for the creation of virtual Mobile Network Operators (vMNO), that offer network services to its clients using the network resources provided by an MNO. However, the 3GPP architecture defined for the management of network slices fails to provide a good support for the vMNOs to have their own network slice management system. The main goal of the thesis work outlined in this paper is to address the integration of current technologies used by 5G networks into multi-domain and multi-client cloud computing scenarios, for the provision of vMNOs following a Network Slice as a Service (NSaaS) approach. The ongoing work focuses on defining mechanisms for the deployment, orchestration and management of diverse vMNOs available in the network slice ecosystem, proposing an extension to the network slice management capabilities proposed by 3GPP.
{"title":"Network Slice Lifecycle Management Model for NFV-based 5G Virtual Mobile Network Operators","authors":"Andrés Cárdenas, D. Fernández","doi":"10.1109/NFV-SDN50289.2020.9289883","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289883","url":null,"abstract":"The use of Network Function Virtualization (NFV) and Software-defined Networks (SDN) technologies in 5G networks provide Mobile Network Operators (MNOs) with new capacities to deploy and orchestrate network functions in a virtualized and programmable way, allowing them in turn to better provide and support new connectivity requirements demanded by users and vertical industries. The Network Slicing function provides the base functionality to virtualize the network and efficiently share it among customers. Besides, the extensive use of virtualization technologies in 5G provides a good support for the creation of virtual Mobile Network Operators (vMNO), that offer network services to its clients using the network resources provided by an MNO. However, the 3GPP architecture defined for the management of network slices fails to provide a good support for the vMNOs to have their own network slice management system. The main goal of the thesis work outlined in this paper is to address the integration of current technologies used by 5G networks into multi-domain and multi-client cloud computing scenarios, for the provision of vMNOs following a Network Slice as a Service (NSaaS) approach. The ongoing work focuses on defining mechanisms for the deployment, orchestration and management of diverse vMNOs available in the network slice ecosystem, proposing an extension to the network slice management capabilities proposed by 3GPP.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"111 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":"132991038","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-SDN50289.2020.9289870
Aman Khalid, Flavio Esposito
Membership testing has many networking applications like distributed caching, peer to peer networks, or resource routing, to name a few. Several studies have reported the advantages of using membership testing in Software Defined Networking, and Bloom Filters have been widely adopted for that purpose. Cuckoo Filters is a recently proposed alternative to Bloom that outperforms them in terms of speed and memory efficiency, with some drawbacks. In this paper, we propose an Optimized Cuckoo Filter (OCF) design that limits some of the Cuckoo Filter drawbacks and gives a better-amortized search time, with less false positives. We then present an implementation of Optimized Cuckoo Filter in distributed SDN and NFV applications, with customizable parameters that enable the data structure to adapt to different workloads. We discuss the use cases of this data structure in SDN and show the performance gain when using our solution with proper configuration. We also show the benefits of this data structure in different SDN and NFV applications by simulating real-world scenarios: content-centric caching and Virtual Firewall as a Network Function and invoke dialog for the widespread adoption of this data structure outside academia through open-source collaboration.
{"title":"Optimized Cuckoo Filters for Efficient Distributed SDN and NFV Applications","authors":"Aman Khalid, Flavio Esposito","doi":"10.1109/NFV-SDN50289.2020.9289870","DOIUrl":"https://doi.org/10.1109/NFV-SDN50289.2020.9289870","url":null,"abstract":"Membership testing has many networking applications like distributed caching, peer to peer networks, or resource routing, to name a few. Several studies have reported the advantages of using membership testing in Software Defined Networking, and Bloom Filters have been widely adopted for that purpose. Cuckoo Filters is a recently proposed alternative to Bloom that outperforms them in terms of speed and memory efficiency, with some drawbacks. In this paper, we propose an Optimized Cuckoo Filter (OCF) design that limits some of the Cuckoo Filter drawbacks and gives a better-amortized search time, with less false positives. We then present an implementation of Optimized Cuckoo Filter in distributed SDN and NFV applications, with customizable parameters that enable the data structure to adapt to different workloads. We discuss the use cases of this data structure in SDN and show the performance gain when using our solution with proper configuration. We also show the benefits of this data structure in different SDN and NFV applications by simulating real-world scenarios: content-centric caching and Virtual Firewall as a Network Function and invoke dialog for the widespread adoption of this data structure outside academia through open-source collaboration.","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"69 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":"121149878","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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