An empirical case for container-driven fine-grained VNF resource flexing

Sheoran, Amit; Bu, Xiangyu; Cao, Lianjie; Sharma, Puneet; Fahmy, Sonia

doi:10.1109/nfv-sdn.2016.7919486

Cited by 22 publications

(6 citation statements)

References 4 publications

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“…The Orchestrator provides deployment of VNFs using hypervisor technology over bare-metal servers. Alternatively, container-based approach such as Docker could be adequate once it employs better CPU and memory consumption by considering traditional virtualised environments (Sheoran et al, 2016). However, owing the network performance metrics traditional hypervisor technology such as full-fledged guest operating system bring better network throughput which is a primary requirement to NaaS approach then container-based approach (Kozhirbayev and Sinnott, 2017).…”

Section: Figure 5 % Cpu Consumption Overtimementioning

confidence: 99%

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

Moreira

Silva

Rosa

et al. 2020

IJGUC

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Rich-media applications deployed on cloud lead the use of the internet by people and organisations around the world. The advent of Software-Defined Networking (SDN), Networking Function Virtualisation (NFV), and Machine Learning (ML) techniques brings new possibilities to address carrier environment challenges, especially QoS. The literature does not show a smart and flexible solution that brings scalability with holistic management of network, compute, and application resources taking into account different approaches to enhance QoS. This paper presents the Orchestrator that manages agnostic network, compute, and applications resources hosted in the cloud. Through ML algorithms, our solution enhances QoS towards network resilience, bandwidth allocation based on real-time traffic, and end-to-end QoS mechanism to the event-driven scenario. The Orchestrator operation spans across different domains providing applications, virtual functions, and cloud resources management elastically. Our experimental evaluation in a large-scale testbed shows Orchestrator's capability to provide a smart jitter decrease using AI techniques.

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Section: Figure 5 % Cpu Consumption Overtimementioning

confidence: 99%

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

Moreira

Silva

Rosa

et al. 2020

IJGUC

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“…However, this architecture mainly suffers from flexible auto scaling in terms of handling monolithic MME implementation and complex state management. In [8], the authors proposed container-driven fine-grained resource flexing. They benchmarked the performance of the MME, Suricata, and Snort VNFs on bare metal, containers, and VMs under varying workloads.…”

Section: Motivation and Related Workmentioning

confidence: 99%

A cloud native solution for dynamic auto scaling of MME in LTE

Amogh

Veeramachaneni

Rangisetti

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%.

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“…The current trend is to decompose a complete VNF in the form of micro-services [2], [10], [3], [11], [12] interacting between each other, each micro-service executing a set of elementary tasks. Once a VNF is decomposed into micro-services, the subsequent task is to instantiate them onto a virtualized architecture.…”

Section: A Decomposition Of a Vnf Into Micro-servicesmentioning

confidence: 99%

Verifying the configuration of virtualized network functions in software defined networks

Pelay¹,

Guillemin

Barais

2017

2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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Abstract-The deployment of modular virtual network functions (VNFs) in software defined infrastructures (SDI) enables cloud and network providers to deploy integrated network services across different resource domains. It leads to a large interleaving between network configuration through software defined network controllers and VNF deployment within this network. Most of the configuration management tools and network orchestrators used to deploy VNF lack of an abstraction to express Assume-Guarantee contracts between the VNF and the SDN configuration. Consequently, VNF deployment can be inconsistent with network configurations. To tackle this challenge, in this paper, we present an approach to check the consistency between the VNF description described from a set of structural models and flow-chart models and a proposed deployment on a real SDN infrastructure with its own configuration manager. We illustrate our approach on virtualized Evolved Packet Core function.

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An empirical case for container-driven fine-grained VNF resource flexing

Cited by 22 publications

References 4 publications

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

A cloud native solution for dynamic auto scaling of MME in LTE

Verifying the configuration of virtualized network functions in software defined networks

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