A Data Distribution Service in a hierarchical SDN architecture: implementation and evaluation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Software-defined networks (SDNs) have caused a paradigm shift in communication networks as they enable network programmability using either centralized or distributed controllers. With the development of the industry and society, new verticals have emerged, such as Industry 4.0, cooperative sensing and augmented reality. These verticals require network robustness and availability, which forces the use of distributed domains to improve network scalability and resilience. To this aim, this paper proposes a new solution to distribute SDN domains by using Data Distribution Services (DDS). The DDS allows the exchange of network information, synchronization among controllers and auto-discovery. Moreover, it increases the control plane robustness, an important characteristic in 5G networks (e.g., if a controller fails, its resources and devices can be managed by other controllers in a short amount of time as they already know this information). To verify the effectiveness of the DDS, we design a testbed by integrating the DDS in SDN controllers and deploying these controllers in different regions of Spain. The communication among the controllers was evaluated in terms of latency and overhead.Postprint (author's final draft

Optimal placement of User Plane Functions in 5G networks

Because of developments in society and technology, new services and use cases have emerged, such as vehicle-to-everything communication and smart manufacturing. Some of these services have stringent requirements in terms of reliability, bandwidth, and network response time and to meet them, deploying network functions (NFs) closer to users is necessary. Doing so will lead to an increase in costs and the number of NFs. Under such circumstances, the use of optimization strategies for the placement of NFs is crucial to offer Quality of Service (QoS) in a cost-effective manner. In this vein, this paper addresses the User Plane Functions Placement (UPFP) problem in 5G networks. The UPFP is modeled as a Mixed-Integer Linear Programming (MILP) problem aimed at determining the optimal number and location of User Plane Functions (UPFs). Two optimization models are proposed that considered various parameters, such as latency, reliability and user mobility. To evaluate their performance, two services under the Ultra-Reliable an Low-Latency Communication (URLLC) category were selected. The acquired results showcase the effectiveness of our solutions.Postprint (author's final draft

The resources placement problem in a 5G hierarchical SDN control plane

In this paper, we address the SDN Controllers and Virtual Network Functions (VNFs) placement problem in 5G networks. To this aim, we propose an architecture for the 5G Control Plane and a method to determine the optimal placement of controllers and VNFs. The placement is determined according not only to latency and capacity requirements but also to type of Network Function (NF).Peer ReviewedPostprint (author's final draft

DQN-based intelligent controller for multiple edge domains

Advanced technologies like network function virtualization (NFV) and multi-access edge computing (MEC) have been used to build flexible, highly programmable, and autonomously manageable infrastructures close to the end-users, at the edge of the network. In this vein, the use of single-board computers (SBCs) in commodity clusters has gained attention to deploy virtual network functions (VNFs) due to their low cost, low energy consumption, and easy programmability. This paper deals with the problem of deploying VNFs in a multi-cluster system formed by this kind of node which is characterized by limited computational and battery capacities. Additionally, existing platforms to orchestrate and manage VNFs do not consider energy levels during their placement decisions, and therefore, they are not optimized for energy-constrained environments. In this regard, this study proposes an intelligent controller as a global allocation mechanism based on deep reinforcement learning (DRL), specifically on deep Q-network (DQN). The conceived mechanism optimizes energy consumption in SBCs by selecting the most suitable nodes across several clusters to deploy event requests in terms of nodes’ resources and events’ demands. A comparison with available allocation algorithms revealed that our solution required 28% fewer resource costs and reduced 35% the energy consumption in the clusters’ computing nodes while maintaining high levels of acceptance ratio.This work has been supported in part (50%) by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under projects PID2019-108713RB-C51 & PID2019-108713RB-C52 MCIN/ AEI/10.13039/501100011033; and in part (50%) by AI@EDGE H2020-ICT-52-2020 under grant agreement No. 10101592

An SDN-based solution for horizontal auto-scaling and load balancing of transparent VNF clusters

© 2021 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.Postprint (published version

An energy-friendly scheduler for edge computing systems

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster

An architecture for the 5G control plane based on SDN and data distribution service

The tremendous growth of services and costumers’ demands have rendered traditional networks inefficient. Telecommunication operators need a more flexible, scalable, faster and programmable architecture to offer users these new services. Software Defined Networking (SDN) has emerged as a natural solution to this situation as it enables network programmability. This article provides a review of the SDN architectures applied to fifth generation (5G) networks. In this work, the prime focus is a proposal of control plane for a 5G architecture with a hybrid hierarchical set of controllers. The architecture is based on a federation of multiple sub-network controllers, each managing only a section of the network, conveniently coordinated by a hierarchically-superior controller. The use of Data Distribution Service (DDS) as a standard of the Object Management Group (OMG) is explored to improve the performance of the proposed architecture. DDS is used taking into account empirical results which have demonstrated a significant improvement in the performance compared to other existing solutions that do not use DDS. We illustrate the flexibility of our approach by presenting some use cases describing how the different elements of this architecture works.Postprint (author's final draft

Software defined networks and data distribution service as key features for the 5G control plane

The latency and flexible requirements of the 5G network are challenging telecommunication operators to have a more flexible, scalable, faster and programmable architecture. To solve this problem, this paper proposes a hybrid hierarchical set of Software Defined Networks (SDN) controllers as the control plane for 5G networks. The architecture is based on a federation of hierarchically-superior controllers which use Data Distribution Service (DDS) to communicate among each other and coordinate multiple sub-network controllers.Peer ReviewedPostprint (author's final draft

A framework for placement and optimization of network functions in 5G

In this paper, we propose an architecture for the 5G core network using Software Defined Networks (SDN) and Network Functions Virtualization (NFV) technologies. Moreover, based on this architecture, we design a framework for determining the Network Functions (NFs) placement and optimizing the NFVI resources and network response time during emergency situations. This framework is based on a two-stage method. In the first stage of the method, the placement of the SDN Controllers and Virtual Network Functions (VNFs) is determined considering not only latency requirements and load levels but also users mobility and network functions type. Meanwhile, in the second stage, the VNF Infrastructure (VNFI) resources are dynamically optimized according to variations on network conditions

A framework for placement and optimization of network functions in 5G

In this paper, we propose an architecture for the 5G core network using Software Defined Networks (SDN) and Network Functions Virtualization (NFV) technologies. Moreover, based on this architecture, we design a framework for determining the Network Functions (NFs) placement and optimizing the NFVI resources and network response time during emergency situations. This framework is based on a two-stage method. In the first stage of the method, the placement of the SDN Controllers and Virtual Network Functions (VNFs) is determined considering not only latency requirements and load levels but also users mobility and network functions type. Meanwhile, in the second stage, the VNF Infrastructure (VNFI) resources are dynamically optimized according to variations on network conditions.Postprint (author's final draft