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

A multi-criteria decision making approach for scaling and placement of virtual network functions

This paper investigates the joint scaling and placement problem of network services made up of virtual network functions (VNFs) that can be provided inside a cluster managing multiple points of presence (PoPs). Aiming at increasing the VNF service satisfaction rates and minimizing the deployment cost, we use both transport and cloud-aware VNF scaling as well as multi-attribute decision making (MADM) algorithms for VNF placement inside the cluster. The original joint scaling and placement problem is known to be NP-hard and hence the problem is solved by separating scaling and placement problems and solving them individually. The experiments are done using a dataset containing the information of a deployed digital-twin network service. These experiments show that considering transport and cloud parameters during scaling and placement algorithms perform more efficiently than the only cloud based or transport based scaling followed by placement algorithms. One of the MADM algorithms, Total Order Preference by Similarity to the Ideal Solution (TOPSIS), has shown to yield the lowest deployment cost and highest VNF request satisfaction rates compared to only transport or cloud scaling and other investigated MADM algorithms. Our simulation results indicate that considering both transport and cloud parameters in various availability scenarios of cloud and transport resources has significant potential to provide increased request satisfaction rates when VNF scaling and placement using the TOPSIS scheme is performed.This work was partially funded by EC H2020 5GPPP 5Growth Project (Grant 856709), Spanish MINECO Grant TEC2017-88373-R (5G-REFINE), Generalitat de Catalunya Grant 2017 SGR 1195 and the National Program on Equipment and Scientifc and Technical Infrastructure, EQC2018-005257-P under the European Regional Development Fund (FEDER). We would also like to thank Milan Groshev, Carlos Guimarães for providing dataset for scaling of robot manipulator based digital twin service

Dynamic service chain composition in virtualised environment

Network Function Virtualisation (NFV) has contributed to improving the flexibility of network service provisioning and reducing the time to market of new services. NFV leverages the virtualisation technology to decouple the software implementation of network appliances from the physical devices on which they run. However, with the emergence of this paradigm, providing data centre applications with an adequate network performance becomes challenging. For instance, virtualised environments cause network congestion, decrease the throughput and hurt the end user experience. Moreover, applications usually communicate through multiple sequences of virtual network functions (VNFs), aka service chains, for policy enforcement and performance and security enhancement, which increases the management complexity at to the network level. To address this problematic situation, existing studies have proposed high-level approaches of VNFs chaining and placement that improve service chain performance. They consider the VNFs as homogenous entities regardless of their specific characteristics. They have overlooked their distinct behaviour toward the traffic load and how their underpinning implementation can intervene in defining resource usage. Our research aims at filling this gap by finding out particular patterns on production and widely used VNFs. And proposing a categorisation that helps in reducing network latency at the chains. Based on experimental evaluation, we have classified firewalls, NAT, IDS/IPS, Flow monitors into I/O- and CPU-bound functions. The former category is mainly sensitive to the throughput, in packets per second, while the performance of the latter is primarily affected by the network bandwidth, in bits per second. By doing so, we correlate the VNF category with the traversing traffic characteristics and this will dictate how the service chains would be composed. We propose a heuristic called Natif, for a VNF-Aware VNF insTantIation and traFfic distribution scheme, to reconcile the discrepancy in VNF requirements based on the category they belong to and to eventually reduce network latency. We have deployed Natif in an OpenStack-based environment and have compared it to a network-aware VNF composition approach. Our results show a decrease in latency by around 188% on average without sacrificing the throughput

Past Before Future: A Comprehensive Review on Software Defined Networks Road Map

Software Defined Networking (SDN) is a paradigm that moves out the network switch2019;s control plane (routing protocols) from the switch and leaves only the data plane (user traffic) inside the switch. Since the control plane has been decoupled from hardware and given to a logically centralized software application called a controller; network devices become simple packet forwarding devices that can be programmed via open interfaces. The SDN2019;s concepts: decoupled control logic and programmable networks provide a range of benefits for management process and has gained significant attention from both academia and industry. Since the SDN field is growing very fast, it is an active research area. This review paper discusses the state of art in SDN, with a historic perspective of the field by describing the SDN paradigm, architecture and deployments in detail

Network Security Automation

L'abstract è presente nell'allegato / the abstract is in the attachmen

Life-cycle management and placement of service function chains in MEC-enabled 5G networks

Recent advancements in mobile communication technology have led to the fifth generation of mobile cellular networks (5G), driven by the proliferation in data traffic demand, stringent latency requirements, and the desire for a fully connected world. This transformation calls for novel technology solutions such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) to satisfy service requirements while providing dynamic and instant service deployment. MEC and NFV are two principal and complementary enablers for 5G networks whose co-existence can lead to numerous benefits. Despite the numerous advantages MEC offers, physical resources at the edge are extremely scarce and require efficient utilization. In this doctoral dissertation, we first attempt to optimize resource utilization at the network edge for the scenario of live video streaming. We specifically utilize the real-time Radio Access Network (RAN) information available at the MEC servers to develop a machine learning-based prediction solution and anticipate user requests. Consequently, Integer Linear Programming (ILP) models are used to prefetch/cache video contents from a centralized video server. Regarding the advantages of NFV technology for the deployment of NFs, the second problem that this dissertation address is the proper association of the users to the gNBs along with efficient placement of SFCs on the substrate network. Our primary purpose is to find a proper embedding of the SFC in a hierarchical 5G network. The problem is formulated as a Mixed Integer Linear Programming (MILP) model, having the objective to minimize service provisioning cost, link utilization, and the effect of VNF migration on users' perceived quality of experience. After rigorously analyzing the proposed SFC placement and considering mobile networks' dynamicity, our next goal is to develop an ILP-based model that minimizes the resource provisioning cost by dynamically embed and scale SFCs so that provisioning cost is minimized while user requirements are met

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities