Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165321
M. Müller, D. Behnke, Patrick-Benjamin Bök, Stefan Schneider, Manuel Peuster, H. Karl
Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration. Our focus is on demonstrating the automatic network reconfiguration that is triggered by the IDS.
{"title":"Cloud-Native Threat Detection and Containment for Smart Manufacturing","authors":"M. Müller, D. Behnke, Patrick-Benjamin Bök, Stefan Schneider, Manuel Peuster, H. Karl","doi":"10.1109/NetSoft48620.2020.9165321","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165321","url":null,"abstract":"Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration. Our focus is on demonstrating the automatic network reconfiguration that is triggered by the IDS.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134494861","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 : 2020-06-01DOI: 10.1109/netsoft48620.2020.9165316
Antonio Suriano, D. Striccoli, G. Piro, Raffele Bolla, G. Boggia
The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.
{"title":"Attestation of Trusted and Reliable Service Function Chains in the ETSI-NFV Framework","authors":"Antonio Suriano, D. Striccoli, G. Piro, Raffele Bolla, G. Boggia","doi":"10.1109/netsoft48620.2020.9165316","DOIUrl":"https://doi.org/10.1109/netsoft48620.2020.9165316","url":null,"abstract":"The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134621064","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165475
S. V. Rossem, Thomas Soenen, W. Tavernier, D. Colle, M. Pickavet, P. Demeester
Many media services undergo a varying workload, showing periodic usage patterns or unexpected traffic surges. As cloud and NFV services are increasingly softwarized, they enable a fully dynamic deployment and scaling behaviour. At the same time, there is an increasing need for fast and efficient mechanisms to allocate sufficient resources with the same elasticity, only when they are needed. This requires adequate performance models of the involved services, as well as awareness of those models in the involved orchestration machinery. In this paper we present how a scalable content delivery service can be deployed in a resource- and time-efficient manner, using adaptive machine learning models for performance profiling. We include orchestration mechanisms which are able to act upon the profiled knowledge in a dynamic manner. Using an offline profiled performance model of the service, we are able to optimize the online service orchestration, requiring fewer scaling iterations.
{"title":"Adaptive & Learning-aware Orchestration of Content Delivery Services","authors":"S. V. Rossem, Thomas Soenen, W. Tavernier, D. Colle, M. Pickavet, P. Demeester","doi":"10.1109/NetSoft48620.2020.9165475","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165475","url":null,"abstract":"Many media services undergo a varying workload, showing periodic usage patterns or unexpected traffic surges. As cloud and NFV services are increasingly softwarized, they enable a fully dynamic deployment and scaling behaviour. At the same time, there is an increasing need for fast and efficient mechanisms to allocate sufficient resources with the same elasticity, only when they are needed. This requires adequate performance models of the involved services, as well as awareness of those models in the involved orchestration machinery. In this paper we present how a scalable content delivery service can be deployed in a resource- and time-efficient manner, using adaptive machine learning models for performance profiling. We include orchestration mechanisms which are able to act upon the profiled knowledge in a dynamic manner. Using an offline profiled performance model of the service, we are able to optimize the online service orchestration, requiring fewer scaling iterations.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"27 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133488201","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165336
Kurt Friday, Elie F. Kfoury, E. Bou-Harb, J. Crichigno
Distributed Denial of Service (DDoS) attacks have terrorized our networks for decades, and with attacks now reaching 1.7 Tbps, even the slightest latency in detection and subsequent remediation is enough to bring an entire network down. Though strides have been made to address such maliciousness within the context of Software Defined Networking (SDN), they have ultimately proven ineffective. Fortunately, P4 has recently emerged as a platform-agnostic language for programming the data plane and in turn allowing for customized protocols and packet processing. To this end, we propose a first-of-a-kind P4-based detection and mitigation scheme that will not only function as intended regardless of the size of the attack, but will also overcome the vulnerabilities of SDN that have characteristically been exploited by DDoS. Moreover, it successfully defends against the broad spectrum of currently relevant attacks while concurrently emphasizing the Quality of Service (QoS) of legitimate end-users and overall SDN functionality. We demonstrate the effectiveness of the proposed scheme using a software programmable P4-switch, namely, the Behavorial Model version 2 (BMv2), showing its ability to withstand a variety of DDoS attacks in real-time via three use cases that can be generalized to most contemporary attack vectors. Specifically, the results substantiate that the mechanism herein is orders of magnitude faster than traditional polling techniques (e.g., NetFlow or sFlow) while minimizing the impact on benign traffic. We concur that the approach's design particularities facilitate seamless and scalable deployments in high-speed networks requiring line-rate functionality, in addition to being generic enough to be integrated into viable network topologies.
{"title":"Towards a Unified In-Network DDoS Detection and Mitigation Strategy","authors":"Kurt Friday, Elie F. Kfoury, E. Bou-Harb, J. Crichigno","doi":"10.1109/NetSoft48620.2020.9165336","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165336","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks have terrorized our networks for decades, and with attacks now reaching 1.7 Tbps, even the slightest latency in detection and subsequent remediation is enough to bring an entire network down. Though strides have been made to address such maliciousness within the context of Software Defined Networking (SDN), they have ultimately proven ineffective. Fortunately, P4 has recently emerged as a platform-agnostic language for programming the data plane and in turn allowing for customized protocols and packet processing. To this end, we propose a first-of-a-kind P4-based detection and mitigation scheme that will not only function as intended regardless of the size of the attack, but will also overcome the vulnerabilities of SDN that have characteristically been exploited by DDoS. Moreover, it successfully defends against the broad spectrum of currently relevant attacks while concurrently emphasizing the Quality of Service (QoS) of legitimate end-users and overall SDN functionality. We demonstrate the effectiveness of the proposed scheme using a software programmable P4-switch, namely, the Behavorial Model version 2 (BMv2), showing its ability to withstand a variety of DDoS attacks in real-time via three use cases that can be generalized to most contemporary attack vectors. Specifically, the results substantiate that the mechanism herein is orders of magnitude faster than traditional polling techniques (e.g., NetFlow or sFlow) while minimizing the impact on benign traffic. We concur that the approach's design particularities facilitate seamless and scalable deployments in high-speed networks requiring line-rate functionality, in addition to being generic enough to be integrated into viable network topologies.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133104596","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165383
M. Zolotukhin, Sanjay Kumar, T. Hämäläinen
With the recent progress in the development of low-budget sensors and machine-to-machine communication, the Internet-of-Things has attracted considerable attention. Unfortunately, many of today's smart devices are rushed to market with little consideration for basic security and privacy protection making them easy targets for various attacks. Unfortunately, organizations and network providers use mostly manual workflows to address malware-related incidents and therefore they are able to prevent neither attack damage nor potential attacks in the future. Thus, there is a need for a defense system that would not only detect an intrusion on time, but also would make the most optimal real-time crisis-action decision on how the network security policy should be modified in order to mitigate the threat. In this study, we are aiming to reach this goal relying on advanced technologies that have recently emerged in the area of cloud computing and network virtualization. We are proposing an intelligent defense system implemented as a reinforcement machine learning agent that processes current network state and takes a set of necessary actions in form of software-defined networking flows to redirect certain network traffic to virtual appliances. We also implement a proof-of-concept of the system and evaluate a couple of state-of-art reinforcement learning algorithms for mitigating three basic network attacks against a small realistic network environment.
{"title":"Reinforcement Learning for Attack Mitigation in SDN-enabled Networks","authors":"M. Zolotukhin, Sanjay Kumar, T. Hämäläinen","doi":"10.1109/NetSoft48620.2020.9165383","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165383","url":null,"abstract":"With the recent progress in the development of low-budget sensors and machine-to-machine communication, the Internet-of-Things has attracted considerable attention. Unfortunately, many of today's smart devices are rushed to market with little consideration for basic security and privacy protection making them easy targets for various attacks. Unfortunately, organizations and network providers use mostly manual workflows to address malware-related incidents and therefore they are able to prevent neither attack damage nor potential attacks in the future. Thus, there is a need for a defense system that would not only detect an intrusion on time, but also would make the most optimal real-time crisis-action decision on how the network security policy should be modified in order to mitigate the threat. In this study, we are aiming to reach this goal relying on advanced technologies that have recently emerged in the area of cloud computing and network virtualization. We are proposing an intelligent defense system implemented as a reinforcement machine learning agent that processes current network state and takes a set of necessary actions in form of software-defined networking flows to redirect certain network traffic to virtual appliances. We also implement a proof-of-concept of the system and evaluate a couple of state-of-art reinforcement learning algorithms for mitigating three basic network attacks against a small realistic network environment.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109844","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165442
Ioakeim Fotoglou, George Papathanail, Angelos Pentelas, Panagiotis Papadimitriou, V. Theodorou, Dimitrios Dechouniotis, S. Papavassiliou
The increasing resource demand and diversity of network services is taken under serious consideration by the various stakeholders, driving the architecture design of 5G (and beyond) networks. Network slicing, as a prominent aspect of next-generation network architectures, aims at satisfying the diverse service requirements in terms of throughput, latency, reliability, and/or security. However, the prevailing way of slice provisioning, i.e., in the form of isolated bundles of computing, storage, and network resources, makes cross-slice communication inefficient, especially at the network edge. This inevitably hinders opportunities for Business-to-Business (B2B) synergies at the event of service co-location. In this paper, we study this novel aspect of network slicing, i.e., cross-slice communication (CSC). We particularly promote a form of optimized CSC, at which two co-located slices can establish peering in a secure and controlled manner, by confining peering traffic within the boundaries of the datacenter, while still preserving the important aspect of resource isolation. Such optimized CSC can foster synergies between service providers without additional latency or traffic in the backhaul/transport network. In this context, we investigate various ways to establish optimized CSC at edge computing infrastructures, based on functionalities offered by state-of-the-art management and orchestration (MANO) frameworks, such as OpenSourceMANO.
{"title":"Towards Cross-Slice Communication for Enhanced Service Delivery at the Network Edge","authors":"Ioakeim Fotoglou, George Papathanail, Angelos Pentelas, Panagiotis Papadimitriou, V. Theodorou, Dimitrios Dechouniotis, S. Papavassiliou","doi":"10.1109/NetSoft48620.2020.9165442","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165442","url":null,"abstract":"The increasing resource demand and diversity of network services is taken under serious consideration by the various stakeholders, driving the architecture design of 5G (and beyond) networks. Network slicing, as a prominent aspect of next-generation network architectures, aims at satisfying the diverse service requirements in terms of throughput, latency, reliability, and/or security. However, the prevailing way of slice provisioning, i.e., in the form of isolated bundles of computing, storage, and network resources, makes cross-slice communication inefficient, especially at the network edge. This inevitably hinders opportunities for Business-to-Business (B2B) synergies at the event of service co-location. In this paper, we study this novel aspect of network slicing, i.e., cross-slice communication (CSC). We particularly promote a form of optimized CSC, at which two co-located slices can establish peering in a secure and controlled manner, by confining peering traffic within the boundaries of the datacenter, while still preserving the important aspect of resource isolation. Such optimized CSC can foster synergies between service providers without additional latency or traffic in the backhaul/transport network. In this context, we investigate various ways to establish optimized CSC at edge computing infrastructures, based on functionalities offered by state-of-the-art management and orchestration (MANO) frameworks, such as OpenSourceMANO.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120932091","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 : 2020-06-01DOI: 10.1109/netsoft48620.2020.9165482
Michel Gokan Khan, J. Taheri, M. Khoshkholghi, A. Kassler, Carolyn Cartwright, M. Darula, Shuiguang Deng
Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2–5 times more computing resources than the PTE.
{"title":"A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions","authors":"Michel Gokan Khan, J. Taheri, M. Khoshkholghi, A. Kassler, Carolyn Cartwright, M. Darula, Shuiguang Deng","doi":"10.1109/netsoft48620.2020.9165482","DOIUrl":"https://doi.org/10.1109/netsoft48620.2020.9165482","url":null,"abstract":"Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2–5 times more computing resources than the PTE.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935273","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 : 2020-06-01DOI: 10.1109/netsoft48620.2020.9165447
Giannis Giakoumakis, Eva Papadogiannaki, G. Vasiliadis, S. Ioannidis
Modern commodity computing systems are composed of a number of heterogeneous processing units, each one with its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only one device (either the CPU or an accelerator), leaving the remaining computational resources underutilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications that exploits any heterogeneous architecture that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse, concurrently executed network packet processing applications, eliminating the interference effects introduced at run-time. The evaluation results show that our scheduler is able to tackle any interference in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.
{"title":"Pythia: Scheduling of Concurrent Network Packet Processing Applications on Heterogeneous Devices","authors":"Giannis Giakoumakis, Eva Papadogiannaki, G. Vasiliadis, S. Ioannidis","doi":"10.1109/netsoft48620.2020.9165447","DOIUrl":"https://doi.org/10.1109/netsoft48620.2020.9165447","url":null,"abstract":"Modern commodity computing systems are composed of a number of heterogeneous processing units, each one with its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only one device (either the CPU or an accelerator), leaving the remaining computational resources underutilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications that exploits any heterogeneous architecture that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse, concurrently executed network packet processing applications, eliminating the interference effects introduced at run-time. The evaluation results show that our scheduler is able to tackle any interference in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123740760","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165444
Estefanía Coronado, G. Cebrián-Márquez, R. Riggio
Connected and automated vehicles currently rely on on-board resources to implement autonomous functions, leaving the mobile network for non-mission-critical applications. At the same time, the ultra-low latency, the increased bandwidth, and the softwarization and virtualization technologies of 5G systems are opening the door to multiple applications in the context of connected and automated vehicles. The deployment of applications at the edge of the mobile network under the Multi-access Edge Computing (MEC) paradigm becomes an excellent option for meeting the latency requirements imposed by connected mobility. In this context, this demonstration showcases how remote and autonomous driving applications, such as lane tracking and object detection, can be offloaded to a MEC-enabled 5G network without impairing their effectiveness, and the change in the latency perceived by end-users with respect to a cloud deployment.
{"title":"Enabling Autonomous and Connected Vehicles at the 5G Network Edge","authors":"Estefanía Coronado, G. Cebrián-Márquez, R. Riggio","doi":"10.1109/NetSoft48620.2020.9165444","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165444","url":null,"abstract":"Connected and automated vehicles currently rely on on-board resources to implement autonomous functions, leaving the mobile network for non-mission-critical applications. At the same time, the ultra-low latency, the increased bandwidth, and the softwarization and virtualization technologies of 5G systems are opening the door to multiple applications in the context of connected and automated vehicles. The deployment of applications at the edge of the mobile network under the Multi-access Edge Computing (MEC) paradigm becomes an excellent option for meeting the latency requirements imposed by connected mobility. In this context, this demonstration showcases how remote and autonomous driving applications, such as lane tracking and object detection, can be offloaded to a MEC-enabled 5G network without impairing their effectiveness, and the change in the latency perceived by end-users with respect to a cloud deployment.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131734838","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 : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165361
Arij Elmajed, A. Aghasaryan, É. Fabre
Virtualization technologies become pervasive in networking, as a way to better exploit hardware capabilities and to quickly deploy tailored networking solutions for customers. But these new programmability abilities of networks also come with new management challenges: it is critical to quickly detect performance degradation, before they impact Quality of Service (QoS) or produce outages and alarms, as this takes part in the closed loop that adapts resources to services. This paper addresses the early detection, localization and identification of faults, before alarms are produced. We rely on the abundance of metrics available on virtualized networks, and explore various data preprocessing and classification techniques. As all Machine Learning approaches must be fed with large datasets, we turn to our advantage the softwarization of networks: one can easily deploy in a cloud the very same software that is used in production, and analyze its behaviour under stress, by fault injection.
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