Automated Provisioning of Cloud-Native Network Functions in Multi-Cloud Environments

Hirai, Shiku; Tojo, Takuya; Seto, Saburo; Yasukawa, Seisho

doi:10.1109/netsoft48620.2020.9165343

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…There are studies on monitoring systems that use Prometheus & the Elasticsearch, Logstash, and Kibana (ELK) stack to collect and utilize data generated in the cloud. There are many studies [13][14][15] wherein Prometheus and Grafana are used to collect and monitor metric data generated in the system [6,[10][11][12]20,21]; and Elasticsearch and Kibana are used to collect log data generated in the network. However, in most studies, these tools only monitored the data; there is only one research case [24] that directly collects and processes microservice data for AI/machine learning training.…”

Section: Related Work 21 Cloud Metric/log Data Applicationmentioning

confidence: 99%

Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice

2023

KSII TIIS

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In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.

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Section: Related Work 21 Cloud Metric/log Data Applicationmentioning

confidence: 99%

Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice

2023

KSII TIIS

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“…Algorithm 1: Algorithm for Distributed Implementation Input: ǫ, τ N i = τi 2 , ρ N i = ρi 2 ,θ i = θi Objectives and Constraints of P(N) and P(S) Output: f * e i , φ * c i ∀ i while True do @SDN: SDN solves P(N) to obtain f e i and optimal Lagrange multipliers λ N τ , λ N ρ , g N θi ; @Kubernetes: Kubernetes solves P(S) to obtain φ c i and optimal lagrange multipliers λ S τ , λ S ρ , g S θi ; Exchange Multipliers and sub-gradients; Update for Master Problem using (22); Stopping Criteria:…”

Section: B Distributed Robust Optimization With Stochastic Objectivementioning

confidence: 99%

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Roy¹,

Rao²,

Alpcan³

et al. 2022

Preprint

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Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.

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“…NFV and SDN have been proved essential in enabling network flexibility and programmability by decoupling control plane from user plane, and by allowing the detachment of network functions from the underlying hardware. In this respect, multiple works have study the usefulness of lightweight container solutions for deploying NFV environments and analysed how this choice determines network performance [16], [21], [22]. In [16] the authors address performance gaps associated with a VNF running on container and VM platforms (via KVM) in terms of CPU, memory consumption and deployment time.…”

Section: Related Workmentioning

confidence: 99%

Validation and Benchmarking of CNFs in OSM for pure Cloud Native applications in 5G and beyond

Adrián

Khodashenas

Hesselbach

et al. 2021

2021 International Conference on Computer Communications and Networks (ICCCN)

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Cloud Native (CN) in 5G systems has been identified as a pivotal candidate for operational and capital expenditure savings as well as for improvements in system agility and services role-out. CN telco is a step forward with respect to Network Function Virtualisation (NFV) aiming at embracing a microservice-based architecture. With this in mind, the European Telecommunications Standards Institute (ETSI) has evolved the ETSI NFV reference architecture to adapt to CN and fill the gap with the NFV framework, including containers and Zero-Touch, among other capabilities. Open-source Management & Orchestration (MANO) initiatives, such as Open Source MANO (OSM), are promoting this adoption giving support to CN solutions based on containers. However, at this early stage deployments are currently non-standalone and embedded in VNF-based solutions such as OpenStack. In this context, this paper presents a proof of concept of a full container technology deployment -via Kubernetes-in a NFV architecture. First, a full CN NFV environment is set with the help of OSM MANO, for which we describe the implementation to enable native kubernetesbased Container Network Functions (CNFs) and analyse their performance, limits, advantages and drawbacks. Finally, our solution for CNFs is benchmarked against a typical OSM-OpenStack setup where VNFs are deployed. The results obtained in this work can help to further encourage users and operators to use CNFs and get the most out of containerisation in NFV.

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Automated Provisioning of Cloud-Native Network Functions in Multi-Cloud Environments

Cited by 12 publications

References 2 publications

Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice

Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Validation and Benchmarking of CNFs in OSM for pure Cloud Native applications in 5G and beyond

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