Orchestration Architecture for Automatic Deployment of 5G Services from Bare Metal in Mobile Edge Computing Infrastructure

Chirivella‐Perez, Enrique; Calero, José M. Alcaraz; Wang, Qi; Aguado, J.

doi:10.1155/2018/5786936

Cited by 16 publications

(14 citation statements)

References 42 publications

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“…Chaining of heterogeneous functions is important to both NFV and IoT platforms, although it is still an open research challenge in 2019 according to Vaquero et al [16]. The Juju cloud modelling language, for example, is being used to orchestrate 5G Virtual Network Function (VNF) services [17]. These relationships are also useful for more data flow-based workloads [18] [19].…”

Section: Relationships In Modelsmentioning

confidence: 99%

Service Relationship Orchestration: Lessons Learned From Running Large Scale Smart City Platforms on Kubernetes

Sebrechts¹,

Borny

Wauters

et al. 2021

IEEE Access

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Smart cities aim to make urban life more enjoyable and sustainable but their highly heterogeneous and distributed context creates unique operational challenges. In such an environment, multiple companies work together with government on applications and data streams spanning several management domains. Deploying these applications, each of which consists of several connected services, and maintaining an overview of application topologies remains difficult. Even though cloud modelling languages have been proposed to solve similar issues, they are not well fit for such a heterogeneous environment because they often require an "all or nothing" approach. Moreover, cloud modelling languages add an additional abstraction layer which rarely supports all features of the underlying platform and make it harder to reuse existing knowledge and tools. This research defines service relationships as the key element to modelling applications as topologies of services. We use this definition to pinpoint what is lacking in the state of the art Kubernetes orchestration tools and provide a blueprint for how relationship support can be added to any orchestrator. We present "orcon", a proof of concept orchestrator which extends the Kubernetes API to allow managing relationships between services by adding metadata to service definitions. Our evaluation shows this orchestrator enables lifecycle synchronization and configuration change propagation with an overhead of only 0.44 seconds per service.

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Section: Relationships In Modelsmentioning

confidence: 99%

Service Relationship Orchestration: Lessons Learned From Running Large Scale Smart City Platforms on Kubernetes

Sebrechts¹,

Borny

Wauters

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Also, CloudNFV/Cloudify projects leverage the feature of SDN and Cloud NFV to enable configuration, monitoring, automated provisioning on the network infrastructure. An example of practice tools is Juju, 70 this ecosystem provides a combination of the preferable definition of practice service in which the automation is created by the so-called charms.…”

Section: Summary and Lesson Learnedmentioning

confidence: 99%

5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues

Debbabi

Jmal

Fourati

2021

Concurrency and Computation

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Network slicing (NS) presents the key enabler of cellular network improvements. It allows enhancing the performance of diverse requirements supported for verticals industries. The concept of NS was carefully studied over the previous few years, and the primary operational principles were developed. However, there is an important need for more investigations on studying NS to enable further development. This article offers a deep study related to the NS principle, the recent standardization process for Third Generation Partnership Project and Fifth Generation Public Private Partnership, the diverse broad use cases, NS key concepts, NS architectures, and NS management and orchestration. Besides, it discusses radio access network slicing and sharing, the algorithms, the projects, and the NS practical experience and practices. Finally, this article proposes and highlights a possible solution to several open research issues.

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“…We examined three applications, namely PRADS, Iptables [89], and Zeek [88] (formerly Bro), which are deployed in bare metal fashion [95], [96] on the MEC servers. The major benefit of these applications is to improve the network security.…”

Section: Fast Experiments On a Real Testbedmentioning

confidence: 99%

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

et al. 2021

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Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.INDEX TERMS Application state transfer, multi-access edge computing (MEC), network function virtualization (NFV), software-defined networking (SDN).

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Orchestration Architecture for Automatic Deployment of 5G Services from Bare Metal in Mobile Edge Computing Infrastructure

Cited by 16 publications

References 42 publications

Service Relationship Orchestration: Lessons Learned From Running Large Scale Smart City Platforms on Kubernetes

Service Relationship Orchestration: Lessons Learned From Running Large Scale Smart City Platforms on Kubernetes

5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

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