NFV service dynamicity with a DevOps approach: Insights from a use-case realization

Rossem, Steven Van; Cai, Xiao; Cerrato, Ivano; Danielsson, Per; Németh, Felicián; Pechenot, Bertrand; Pelle, István; Risso, Fulvio; Sharma, Sachin; Skoldstrom, Pontus; John, Wolfgang

doi:10.23919/inm.2017.7987357

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…This study considers scaling data-plane resources as a function of traffic load; but also in this case, a reactive approach that does not benefit from historic data is used. Moreover, compared to [16]- [18], our study explores the benefits of autoscaling also from the leasers point of view.…”

Section: Background and Related Workmentioning

confidence: 99%

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Rahman

Ahmed

Huynh

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on autoscaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., startup time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report indepth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers. 1

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Section: Background and Related Workmentioning

confidence: 99%

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Rahman

Ahmed

Huynh

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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“…Distinct open-source tools implement a specific part of the envisioned SDK environment, such as monitor data analysis [12,13]. Adapters for light-weight or specialized environments [8][9][10] can deploy chained virtual machines or containers for testing or emulate the execution of service control functions on large scale data center topologies.…”

Section: Limitations In Existing Sdk Environmentsmentioning

confidence: 99%

Introducing Development Features for Virtualized Network Services

et al. 2018

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Network virtualization and softwarizing network functions, are trends aiming at higher network efficiency, cost reduction and agility. They are driven by the evolutions in Software Defined Networking (SDN) and Network Function Virtualization (NFV). This shows that software will play an increasingly important role within telecommunication services, which were previously dominated by hardware appliances. Service providers can benefit from this, as it enables a faster introduction of new telecom services, combined with an agile set of possibilities to optimize and fine-tune their operations. However, the provided telecom services can only evolve if the adequate software tools are available. In this article, we explain how the development, deployment and maintenance of such an SDN/NFV-based telecom service puts specific requirements on the platform providing it. A Software Development Kit (SDK) is introduced, allowing service providers to adequately design, test and evaluate services before they are deployed in production and also update them during their lifetime. This continuous cycle between development and operations, a concept known as DevOps, is a well-known strategy in software development. To extend its context further to SDN/NFV-based services, the functionalities provided by traditional cloud platforms are not yet sufficient. By giving an overview of the currently available tools and their limitations, the gaps in DevOps for SDN/NFV services are highlighted. The benefit of such an SDK is illustrated by a secure content delivery network service (enhanced with deep packet inspection and elastic routing capabilities). With this use-case, the dynamics between developing and deploying a service are further illustrated.

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“…The ideas reflected in these examples could be applied to other monitoring metrics, where the Common Rules and other parts of the scripts we presented could be reused. For example, in Van Rossem and others, we have used the proposed query engine to measure the imbalance of CPU load and congestion risk for troubleshooting purpose. Query scripts would typically be provided as a query library, so that users may use a familiar command line interface–like syntax to query these monitoring metrics.…”

Section: Example Usage Of the Languagementioning

confidence: 99%

Automatic data aggregation for recursively modeled NFV services

2017

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Summary Network function virtualization (NFV) allows to model network services as graphs interconnecting virtual network functions (VNFs), which may include nested VNFs, modeled as subgraphs of VNFs or end points. To query the performance data of network services modeled as abstract and high‐level graphs is challenging because of the recursivity of the NFV architecture and the elasticity and dynamicity provided by the NFV infrastructure. We propose to use Datalog, a declarative logic programming language, to build a framework that supports efficient data aggregation for performance metrics of recursively modeled network services. We present our recursive query language for automatic and flexible decomposition and aggregation of NFV performance metrics and describe example use cases for both compute and network metrics. We also describe the design and implementation of a proof‐of‐concept query engine using the language. Our performance evaluation shows that the total query latency is dominated by retrieval times of performance metrics from infrastructure databases, and the time for execution and automatic decomposition of high‐level queries by the query engine itself increases linearly with the size of the service graph up to 1000 nodes. Hence, our evaluations show that the query engine scales well, bounded mainly by the limited execution capabilities of our test environment. Furthermore, it can handle multiple concurrent queries up to the concurrency limits of the backend database in use. The proposed query language and engine are thus effective in recursively retrieving performance metrics of NFV environments supporting large‐scale service graphs and large numbers of query requests.

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NFV service dynamicity with a DevOps approach: Insights from a use-case realization

Cited by 8 publications

References 13 publications

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Introducing Development Features for Virtualized Network Services

Automatic data aggregation for recursively modeled NFV services

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