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

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

Software Defined Networking (SDN) is a paradigm that moves out the network switch’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 SDN’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

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands. In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant

Towards virtualized network functions as a service

Network Function Virtualization (NFV) is a promising technology that proposes to move packet processing from dedicated hardware middle-boxes to software running on commodity servers. As such, NFV brings the possibility of outsourcing enterprise Network Function (NFs) processing to the cloud. However, for a Cloud Service Provider (CSP) to offer such services, several research problems still need to be addressed. When an enterprise outsources its NFs to a CSP, the CSP is responsible for deciding: (1) where initial Virtual NFs (VNFs) should be instantiated, and (2) what, when and where additional VNFs should be instantiated to satisfy the traffic changes (scaling), (3) how to update the network configurations with minimum impact on network performances, etc. This brings the requirement of a cloud management framework for VNFs and the cloud infrastructure related operations: provisioning, configuring, maintaining and scaling of the VNFs, as well as configuring and updating of the cloud network. In this thesis we explore three aspects of a cloud management framework for VNF: (1) dynamic resource allocation, (2) VNFs scaling methods and (3) dynamic load balancing. In the context of dynamic resource allocation for VNFs, we explore two resource allocation algorithms for: (1) the initial placement of VNFs, and (2) the scaling of VNFs to support traffic changes. We propose two approximation approaches (heuristic based): (1) Iterated Local Search (ILS) and (2) Genetic Programming (GP) to implement the resource allocation algorithms. We compare these heuristic based approaches with a traditional resource allocation approach: Integer Linear Programming (ILP). In the context of VNFs scaling methods, we explored three different scaling approaches: (1) vertical scaling, (2) migration and (3) horizontal. We analyse the three scaling methods in-terms of their practical implementation aspects as well as the optimization aspects with respect to the management. In the context of dynamic load balancing, we explore load balancing approaches that maintain affinity and handle states and sessions of the traffic, so that the requirement of state migration is avoided. We propose a session-aware load balancing algorithm based on consistent hashing.La virtualización de funciones de redes (NFV) es una tecnología prometedora que propone mover el procesamiento de paquetes de cajas intermedias de hardware dedicadas al procesamiento especializado de paquetes a módulos de software que se ejecuta en servidores no especializados. Como tal, NFV crea la posibilidad de externalizar de las redes empresariales el procesamiento hecho por funciones de redes (NFs) a la nube. Sin embargo, para que un Proveedor de Servicios en la Nube (CSP) ofrezca tales servicios, todavía hay que resolver varios problemas. Cuando una empresa subcontrata sus NF a un CSP, el CSP es responsable de decidir: (1) dónde deben instanciarse las NF virtuales iniciales (VNF), y (2) qué tipo, cuándo y dónde deben instanciarse VNF adicionales para satisfacer los cambios de tráfico (Escalamiento), (3) cómo actualizar las configuraciones de la red con el mínimo impacto en los rendimientos de la misma, etc. Esto requiere de un marco de gestión de la nube para VNFs y las operaciones relacionadas con la infraestructura de nube: provisiona miento, mantenimiento y escalado de les VNS. Así como la configuración y actualización de la red en la nube. En esta tesis exploramos tres aspectos de un marco de gestión de la nube para VNF: (1) asignación dinámica de recursos, (2) métodos de escalado para VNFs y (3) balanceo de carga dinámico. En el contexto de la asignación dinámica de recursos para VNFs, exploramos dos algoritmos de asignación de recursos para: (1) la ubicación inicial de VNFs, y (2) la escala de VNFs para apoyar los cambios de tráfico. Proponemos dos métodos de aproximación (basadas en heurísticas): (1) Búsqueda Local Iterada (ILS) y (2) Programación Genética (GP) para implementar los algoritmos de asignación de recursos. Comparamos estos enfoques heurísticos con en un enfoque tradicional de asignación de recursos: Programación Lineal Entera (ILP). En el contexto de los métodos de escalde VNFs, hemos explorado tres enfoques de escala diferentes: (1) escalamiento vertical, (2) la migración y (3) horizontal. Analizamos los tres métodos de escalado en términos de sus aspectos de implementación práctica, así como los aspectos de optimización con respecto a la gestión. En el contexto del balanceo de carga dinámico, exploramos enfoques de equilibrio de carga que mantienen la afinidad y manejan estados y sesiones del tráfico, de manera que se evita el la necesidad de migración del estado. Proponemos un algoritmo de equilibrio de carga que considera sesiones basado en funciones de hash consistente

Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms

With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.This research was sponsored by U.S. Army Research Laboratory and U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. Jorge Lobo was partially supported by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya. Also this work was supported by the Maria de Maeztu Units of Excellence Programme and the Spanish Ministry of Economy and Competitiveness under the María de Maezto Units of Excellence Program (MDM-2015-0502)

Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms

With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.This research was sponsored by U.S. Army Research Laboratory and U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. Jorge Lobo was partially supported by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya. Also this work was supported by the Maria de Maeztu Units of Excellence Programme and the Spanish Ministry of Economy and Competitiveness under the María de Maezto Units of Excellence Program (MDM-2015-0502)

Data for NFVSDN experiments

##Project Structure: 1. GeneratePolicies. 2. DistributeTrafficOverPolicies. 3. PoliciesToChange. 4. TopologyCreator. 5. ExampleDataSet. ##Guidelines to use the data and programs in the repository. There are two ways that this repository can be useful for anyone that needs data about VNFs and their traffic on the cloud. 1.Directly use the already generated data set. 2.Generate your own data set using the given programs. ##How to use the already generated data set: ExampleDataSet. We have generated data for: 1.Possible policy requests with initial traffic passing through them defined. 2.Scaling requirements for each 15 minutes for 2 days. 3.Topology data (nodes, links, paths) for K-Fat Tree, BCube and VL2 architectures with 64 servers. You can use these data directly as inputs for your experiments. ##How to use the programs and generate the required data sets. If you want to generate your own data sets according to your requirements, you can use the given programs. 1) First step is to generate the policy requests data set using the policy requests generation program: GeneratePolicies. - Inputs to the program: number of large scaled enterprise networks. - Output of the program: a set of policies for each enterprise with 100 NFs. 2) After we have created the policy requets data set, the seconds step is to create the traffic data set for the policies using the initial traffic distribution program: DistributeTrafficOverPolicies. - Inputs to the program: the set of policies, initial traffic load. - Output of the program: distribution of the traffic load over policies. 3) The third step is to create the scaling requirements data set to reflect the traffic changes over the time using the scaling requirements over the time program: PoliciesToChange. 4) The last step is to generate the required topology data for different network architectures (K-Fat tree, BCube, VL2) using the topology generation program: TopologyCreator. - Inputs to the program: network architecture and number of servers. - Output of the program: the topology: nodes, links and paths.Network Function Virtualization (NFV) proposes to move packet processing from dedicated hardware middle-boxes to software running on commodity servers: virtualized Network Function (NFs) (i.e, Firewall, Proxy, Intrusion Detection System etc.). We have been developing an experimental platform called Network Function Center (NFC) to study issues related to NFV and NFs, assuming that the NFC will deliver virtualized NFs as a service to clients on a subscription basis. Our studies specially focus on dynamic resource allocation for NFs and we have proposed two new resource allocation algorithms based on Genetic Programming (GP) [1] and currently working on another algorithm based on Iterative Local Search. For a more realistic evaluation of these algorithms, testing data is a fundamental component, but unfortunately, public traffic data specifically referring to virtualized NFs chains is not readily available. Therefore, we developed a model to generate the specific data we needed, based on the available general traffic data [2]. This repository contains all the details about how we modelled general data into the specific data we wanted, with along the software we used and the assumptions we made during the data modelling process. Using this data and programs, the evaluation results presented in our publications can be easily reproduced. [1] W. Rankothge, J. Ma, F. Le, A. Russo, and J. Lobo, [“Towards making network function virtualization a cloud computing service,”] (http://repositori.upf.edu/handle/10230/26035) in IM 2015. [2] W. Rankothge, F. Le, A. Russo, and J. Lobo, [“Experimental results on the use of genetic algorithms for scaling virtualized network functions,”] (http://repositori.upf.edu/handle/10230/26036) in IEEE SDN/NFV 2015

Experimental results on the use of genetic algorithms for scaling virtualized network functions

Network Function Virtualization (NFV) is bringing closer the possibility to truly migrate enterprise data centers into the cloud. However, for a Cloud Service Provider to offer such services, important questions include how and when to scale out/in resources to satisfy dynamic traffic/application demands. In previous work [1], we have proposed a platform called Network Function Center (NFC) to study research issues related to NFV and Network Functions (NFs). In a NFC, we assume NFs to be implemented on virtual machines that can be deployed in any server in the network. In this paper we present further experiments on the use of Genetic Algorithms (GAs) for scaling out/in NFs when the traffic changes dynamically. We combined data from previous empirical analyses [2], [3] to generate NF chains and for getting traffic patterns of a day and run simulations of resource allocation decision making. We have implemented different fitness functions with GA and compared their performance when scaling out/in over time.This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Jorge Lobo was also partially supported by the Secretaria dUniversitats i Recerca de la Generalitat de Catalunya