z-TORCH: An Automated NFV Orchestration and Monitoring Solution

Sciancalepore, Vincenzo; Yousaf, Faqir Zarrar; Costa‐Pérez, Xavier

doi:10.1109/tnsm.2018.2867827

Cited by 53 publications

(41 citation statements)

References 23 publications

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“…Every reference in the NFV domain in Table 1, makes use of a platform to automate VNF performance measurements. Generic architectures for profiling frameworks have been described earlier in [6,7,8,9,11,12], where also the relation to DevOps related workflows is highlighted. We extend this previous work with more insights for using a Service Oriented Architecture (SOA) and integration of both sampling and modeling methods.…”

Section: Related Workmentioning

confidence: 99%

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Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

et al. 2020

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Modern network services make increasing use of virtualized compute and network resources. This is enabled by the growing availability of softwarized network functions, which take on major roles in the total traffic flow (such as caching, routing or as firewall). To ensure reliable operation of its services, the service provider needs a good understanding of the performance of the deployed softwarized network functions. Ideally, the service performance should be predictable, given a certain input workload and a set of allocated (virtualized) resources (such as vCPUs and bandwidth). This helps to estimate more accurately how much resources are needed to operate the service within its performance specifications. To predict its performance, the network function should be profiled in the whole range of possible input workloads and resource configurations. However, this input can span a large space of multiple parameters and many combinations to test, resulting in an expensive and overextended measurement period. To mitigate this, we present a profiling framework and a sampling heuristic to help select both workload and resource configurations to test. Additionally, we compare several machine-learning based methods for the best prediction accuracy, in combination with the sampling heuristic. This work has been performed in the framework of the NG-PaaS and 5GTANGO project, funded by the European Commission under the Horizon 2020 and 5G-PPP Phase2 programmes, resp. under Grant Agreement No. 761 557 and 761 493 (http://ngpaas.eu) (https://www.5gtango.eu). This work is partly funded by UGent BOF/GOA project "Autonomic Networked Multimedia Systems".

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Section: Related Workmentioning

confidence: 99%

“…z-Torch [6] NFV-vital [7] NFV-inspector [8] NFV + Manually selected parameters and values Covers only part of the operational space.…”

Section: Sampling Heuristic Drawbacksmentioning

confidence: 99%

Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

et al. 2020

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“…The second adverse effect is represented by scale-up events, i.e., under-provisioned slices whose capabilities have to be swiftly extended to cope with unforeseen increases in traffic. As discussed in [14], such events have the potential to decrease the QoS/QoE of all services supported by the MNO. The quantity u expresses the number of time periods in which such events happen:…”

Section: A Performance Metricsmentioning

confidence: 99%

5G Traffic Forecasting: If Verticals and Mobile Operators Cooperate

Malandrino

Chiasserini

2019

2019 15th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

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In 5G research, it is traditionally assumed that vertical industries (a.k.a verticals) set the performance requirements for the services they want to offer to mobile users, and the mobile operators alone are in charge of orchestrating their resources so as to meet such requirements. Motivated by the observation that successful orchestration requires reliable traffic predictions, in this paper we investigate the effects of having the verticals, instead of the mobile operators, performing such predictions. Leveraging a real-world, large-scale, crowd-sourced trace, we find that involving the verticals in the prediction process reduces the prediction errors and improves the quality of the resulting orchestration decisions.

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“…The considered objective function is operational cost minimization of the service chain provider. In [16], a machine-learning-based method for jointly optimization NFV placement and monitoring processes. In [17] by using Deep Feedforward Neural Network or Multi-Layer Perceptron (MLP), a solution for proactive identification of SLA violations is presented.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Dynamic Reliability Aware NFV-Based Service Provisioning

Khezri

Moghadam

Farshbafan

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Network function virtualization (NFV) is referred to the technology in which softwarized network functions virtually run on commodity servers. Such functions are called virtual network functions (VNFs). A specific service is composed of a set of VNFs. This is a paradigm shift for service provisioning in telecom networks which introduces new design and implementation challenges. One of these challenges is to meet the reliability requirement of the requested services considering the reliability of the commodity servers. VNF placement which is the problem of assigning commodity servers to the VNFs becomes crucial under such circumstances. To address such an issue, in this paper, we employ Deep Q-Network (DQN) to model NFV placement problem considering the reliability requirement of the services. The output of the introduced model determines what placement will be optimal in each state. Numerical evaluations show that the introduced model can significantly improve the performance of the network operator.

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z-TORCH: An Automated NFV Orchestration and Monitoring Solution

Cited by 53 publications

References 23 publications

Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

5G Traffic Forecasting: If Verticals and Mobile Operators Cooperate

Deep Reinforcement Learning for Dynamic Reliability Aware NFV-Based Service Provisioning

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