A survey of network function placement

Xin, Li; Qian, Chen

doi:10.1109/ccnc.2016.7444915

Cited by 23 publications

(12 citation statements)

References 23 publications

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“…This has motivated a lot of work on network function placement, see e.g. the survey [27], with the aim of maximizing resource efficiency while providing mild service guarantees (e.g., CPU availability, inter-VNF delays). For instance, [28] presents a mixed integer linear programming to minimize the power consumption while guaranteeing traffic constraints which optimizes the VNF locations.…”

Section: Nfv and Reliabilitymentioning

confidence: 99%

Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements

Perez-Valero

Banchs

Serrano

et al. 2024

IEEE Trans. on Mobile Comput.

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Auto-scaling techniques aim to keep the right number of active servers for the current load: if this number is too small we risk service disruption, but if it is too large we waste resources. Despite the interest in the efficient operation of this type of systems, no prior work has addressed auto-scaling techniques for Network Function Virtualization (NFV) with stringent reliability requirements such as those envisioned in 5G (5 or 6 nines). To achieve such levels of reliability, we need to account for both the activation delay until servers become available (i.e., the wake-up or activation time) and the fallible nature of servers (which may fail with some probability). In this paper, we build on control theory to design an auto-scaling technique for a server farm for NFV that guarantees certain reliability while minimizing the number of active resources. We show that the use of well-established tools from control theory results in convergence times much shorter than those obtained with state-of-the-art reinforcement learning techniques. This shows that, despite the current trend to apply machine learning to all sorts of networking problems, there may be some cases where other techniques (such as control theory) can be more suitable.

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Section: Nfv and Reliabilitymentioning

confidence: 99%

Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements

Perez-Valero

Banchs

Serrano

et al. 2024

IEEE Trans. on Mobile Comput.

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“…Abdulaziz et al [12] provide a detailed review of VM placement approaches by classifying the solutions based on adopted algorithms or models (i.e., heuristics, meta-heuristics, matching, and Multi-Criteria Decision Making (MCDM)). Xin et al [13] discuss the network function placement and orchestration frameworks. Sedef et al [14] propose a taxonomy of VNF placement solutions where optimization methods are divided into four types: linear programming, non-linear programming, heuristic algorithms, and Machine Learning (ML) algorithms.…”

Section: E Related Workmentioning

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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“…This strategy results in a load-balanced system, but at the same time, increases communication costs. While service placement in terms of Virtual Machine placement in cloud datacenters [2,3] and Network Function Virtualization (vNF) placement [4,5] has been extensively studied, the placement of microservices in fog computing has received far less attention. Due to more stringent resource constraints, a higher degree of heterogeneity, the geographical distribution of fog nodes, and, above all, a decentralized system architecture, placement strategies devised for cloud platforms are not ideal for fog computing.…”

Section: Introductionmentioning

confidence: 99%

Autonomic IoT Application Placement in Edge/Fog Computing

2024

J Math Techniques Comput Math

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Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of microservices that are mapped to edge computing and communication devices (fog nodes). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

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A survey of network function placement

Cited by 23 publications

References 23 publications

Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements

Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements

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

Autonomic IoT Application Placement in Edge/Fog Computing

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