Dynamic Resource Allocation and Placement of Cloud Native Network Services

Arora, Sagar; Ksentini, Adlen

doi:10.1109/icc42927.2021.9500276

Cited by 16 publications

(5 citation statements)

References 4 publications

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“…Sagar et al [122] propose a dynamic resource allocation and placement algorithm (DRAP) aiming to design and place a simple cloud-native network service. Their approach can help service providers to reduce their infrastructure costs.…”

Section: )mentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network's performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include microservices, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

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Section: )mentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…We then imitate the inherent randomness of mobile device behaviors, by adding a coloured noise with a standard deviation equal to 35% of the mean throughput. This power-law noise has a power spectral density per unit of bandwidth proportional to 1 𝑓 𝜙 [23], where 𝜙 = 0 implies white noise, 𝜙 = 1 represents pink noise, and Brownian noise corresponds to 𝜙 = 2. We select 𝜙 = 1.08 to strike a balance between trend and noise.…”

Section: Synthetic Datasetmentioning

confidence: 99%

“…Cloud-native NSaaS management. Cloud-native network architectures offer a natural support to network slicing operations [29]: they allow assigning dedicated resources (e.g., spectrum, transport capacity, compute or memory resources, depending on the target network domain) to each slice [1], configuring dynamically the Virtual Network Functions (VNF) according to the SLA of each slice [24], and monitoring the fulfillment of such SLA [9]. As a result, the cloudification of networks implicitly paves the way to the realization of Network Slicing as a Service (NSaaS) models.…”

Section: Introductionmentioning

confidence: 99%

kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices

Alcalá-Marín,

Bazco-Nogueras,

Banchs

et al. 2023

Proceedings of the Twenty-Fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Net

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Cloud-native mobile networks pave the road for Network Slicing as a Service (NSaaS), where slice overbooking is a promising management strategy to maximize the revenues from admitted slices by exploiting the fact they are unlikely to fully utilize their reserved resources concurrently. While seminal works have shown the potential of overbooking for NSaaS in simplistic cases, its realization is challenging in practical scenarios with realistic slice demands, where its actual performance remains to be tested. In this paper, we propose kaNSaaS, a complete solution for NSaaS management with slice overbooking that combines deep learning and classical optimization to jointly solve the key tasks of admission control and resource allocation. Experiments with large-scale measurement data of actual tenant demands show that kaNSaaS increases the network operator profits by 300% with respect to NSaaS management strategies that do not employ overbooking, while outperforming by more than 20% state-of-the-art overbooking-based approaches.

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“…With the rise of containerization in recent years, there are some works focusing on CNFs. Arora and Ksentini [6], propose a dynamic resource allocation algorithm considering the availability requirements of end users.…”

Section: Related Workmentioning

confidence: 99%