On Monolithic and Microservice Deployment of Network Functions

Sharma, Sachin; Uniyal, Navdeep; Tola, Besmir; Jiang, Yuming

doi:10.1109/netsoft.2019.8806705

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…In the context of microservices-based NFV, the studies in [5], [6], [26] and [27] investigated the deployment of decomposed VNF-Forwarding Graphs (VNF-FGs). Based on the traffic demands, the authors in [6] suggested a MILP model to determine the best decomposition for each SFC from the pool of already decomposed SFCs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This will further load the network and push for serving a variety of new services and applications instantaneously in addition to the existing ones. To support this evolution towards 6G, the NFV architecture, which was initiated in 2012 [3], is also evolving towards a microservices-based architecture [4], [5], [6], [7]. We assume that readers have basic knowledge of NFV; however, for more details on the NFV framework, interested readers can refer to [8], [9], and [10].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Decomposition of Service Function Chain Using a Deep Reinforcement Learning Approach

et al. 2022

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The Internet of Things (IoT) universe will continue to expand with the advent of the sixth generation of mobile networks (6G), which is expected to support applications and services with higher data rates, ultra-reliability, and lower latency compared to the fifth generation of mobile networks (5G). These new demanding 6G applications will introduce heavy load and strict performance requirements on the network. Network Function Virtualization (NFV) is a promising approach to handling these challenging requirements, but it also poses significant Resource Allocation (RA) challenges. Especially since 6G network services will be highly complicated and comparatively short-lived, network operators will be compelled to deploy these services in a flexible, on-demand, and agile manner. To address the aforementioned issues, microservice approaches are being investigated, in which the services are decomposed and loosely coupled, resulting in increased deployment flexibility and modularity. This study investigates a new RA approach for microservices-based NFV for efficient deployment and decomposition of Virtual Network Function (VNF) onto substrate networks. The decomposition of VNFs involves additional overheads, which have a detrimental impact on network resources; hence, finding the right balance of when and how much decomposition to allow is critical. Thus, we develop a criterion for determining the potential/candidate VNFs for decomposition and also the granularity of such decomposition. The joint problem of decomposition and efficient embedding of microservices is challenging to model and solve using exact mathematical models. Therefore, we implemented a Reinforcement Learning (RL) model using Double Deep Q-Learning, which revealed an almost 50% more normalized Service Acceptance Rate (SAR) for the microservice approach over the monolithic deployment of VNFs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Decomposition of Service Function Chain Using a Deep Reinforcement Learning Approach

et al. 2022

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“…The analysis in [3] has been deepened in [4], where Docker is compared with other container engines. In [5], Docker-based monolith and microservice approaches to design an NFV-based system are compared and a methodology is proposed to estimate the number of instances required to maximize performance.…”

Section: The Docker Container Enginementioning

confidence: 99%

Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios

et al. 2021

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We introduce an open-source, scalable, and distributed architecture, called Megalos, that supports the implementation of virtual network scenarios consisting of virtual devices (VDs) where each VD may have several Layer 2 interfaces assigned to virtual LANs. We rely on Docker containers to realize vendor-independent VDs and we leverage Kubernetes for the management of the nodes of a distributed cluster. Our architecture does not require platform-specific configurations and supports a seamless interconnection between the virtual environment and the physical one. Also, it guarantees the segregation of each virtual LAN traffic from the traffic of other LANs, from the cluster traffic, and from Internet traffic. Further, a packet is only sent to the cluster node containing the recipient VD. We produce several example applications where we emulate large network scenarios, with thousands of VDs and LANs. Finally, we experimentally show the scalability potential of Megalos by measuring the overhead of the distributed environment and of its signaling protocols.

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“…The several services de-aggregated from a VNF (or a VNF service chain) have complex mesh-like inter-dependencies among them [3,5] i.e., they may require to call each other several times to set up a network service. Hence, catering to these inter-dependencies, which are typically governed by strict network requirements in terms of traffic volume, latency, quality of service, etc., makes the services management unprecedentedly complex.…”

Section: Introductionmentioning

confidence: 99%

Towards explainable artificial intelligence for network function virtualization

Sharma

Nag

Cordeiro

et al. 2020

Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies

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Network Function Virtualization (NFV) refers to the process of running network functions in virtualized IT infrastructures as softwarized Virtual Network Functions (VNFs). Several telecom service providers are currently benefiting from this concept, as it enables a faster introduction of new network services, thereby meeting changing requirements. Following a trend initially adopted by cloud service providers, telecom service providers are also adopting deaggregation of the VNFs into microservices (services). However, a service-based architecture that can manage a large set of diverse and sensitive network functions requires new Artificial Intelligence (AI)-based methodologies to cope with the complexity of the service-based NFV paradigm. This paper focuses on the use of explainable AI (XAI) for gradually migrating towards a servicesbased architecture in NFV. The paper first establishes the need for XAI to transform the NFV architecture to a µservice-based architecture and then describes some of our research objectives. Afterwards, our preliminary approach and long-term visions are provided. CCS CONCEPTS • CCS->Networks->Network architectures->Network design principles ;

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On Monolithic and Microservice Deployment of Network Functions

Cited by 7 publications

References 21 publications

Dynamic Decomposition of Service Function Chain Using a Deep Reinforcement Learning Approach

Dynamic Decomposition of Service Function Chain Using a Deep Reinforcement Learning Approach

Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios

Towards explainable artificial intelligence for network function virtualization

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