ECCN: An Elastic Customized Cloud Network Platform

Currently, a wide demand for the implementation and use of various cloud solutions is a modern trend and the driving force behind the development of network technologies. The growth of cloud application services delivered through data centers with varying network traffic needs demonstrates the limitations of traditional routing and load balancing methods. The combination of the advantages of software defined networks (SDN) technology and artificial intelligence (AI) methods ensures efficient management and operation of computer network resources. The paper proposes an approach to neural network multipath routing in SDN based on a genetic algorithm. The architecture and model of an artificial neural network has been developed to solve the problem of multipath routing in the SDN, which is able to predict the shortest paths based on the metrics of communication links. To optimize the hyperparameters of the neural network model, it is proposed to use a modified genetic algorithm. A visual software system SDNLoadBalancer has been developed and an experimental SDN topology has been designed, which makes it possible to study in detail the processes of neural network multipath routing in SDN based on the proposed approach. The obtained results show that the proposed neural network model has the ability to predict routes with high accuracy in real time, which makes it possible to implement various load balancing schemes in order to increase performance of SDN.

The rigidity of static configurations in the network devices, given by a list of commands or static scripts, is limiting current networks. They do not provide automatic resource provisioning and affects directly to the decrease of the efficiency. Furthermore, data centers have different traffic patterns because of the implementation of network virtualization and cloud computing technologies. Now the applications are executed in many virtual machines that must communicate, thus increasing the communication between servers. Software Defined Networks (SDNs) technology has appeared to allow higher programmable, automatic and flexible networks. They are able to divide the control plane from the data plane. In SDNs, the code is programmed centrally instead of programming node by node. The implementation can be done independently of the trademark or the model as long as it supports the communication protocol. SDN allows including new functions in the network devices. Moreover, it can easily include certain intelligent functions, in order to provide intelligence to the network. These features allow the system to self-adapt with the goal of improving it performance. The speech is focused on detailing and discussing some published Intelligent Systems for Multimedia delivery in SDN. Moreover, we will revise the most well-known artificial intelligence techniques that can be used in network data and communication networks. We will also revise the main network parameters and how they affect to the multimedia delivery. First we will detail the network architecture and the communication protocol design and development. We will also show how the information taken from the data frames, the users and servers behavior, and the traffic patterns (traffic changes, quality of service parameters, state of the frames, etc.) can be used to improve the multimedia delivery performance. It will self-adapt according to the state of the network. Network parameters and patterns are used by an intelligent network algorithm to estimate the behavior using the empirical data. Intelligent Systems for Multimedia delivery can be implemented in a wide range of multimedia applications.

Cloud computing is an emerging area where all the network services are provided in the form of pay per use. Utilizing the available resources efficiently is one of the challenge in the data center network. Cloud resources are given as virtual machine to the end users. Sometimes, Virtual Machines (VMs) are overloaded or underloaded. The overloaded VMs degrade the performance of applications and underloaded VMs lead to inefficient resource utilization. To effectively utilize the available resources, we have proposed Dynamic Agent-Based’Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN). This load balancing algorithm uses the global visibility of SDN to efficiently migrate virtual machines in the data center network. The results show that our SDN load balancing approach improves the overall network efficiency and works well on data migration.The proposed algorithm helps to increase the processing speed and predict the loaded VM in heavy load conditions and optimizes the resource utilization. The migration process results are compared with Multi-Path TCP (MPTCP) and Heuristic algorithm (HA) and it is seen that our proposed algorithm takes less migration time compared to MPTCP and HA.

The fundamental role of the software defined networks (SDNs) is to decouple the data plane from the control plane, thus providing a logically centralized visibility of the entire network to the controller. This enables the applications to innovate through network programmability. To establish a centralized visibility, a controller is required to discover a network topology of the entire SDN infrastructure. However, discovering a network topology is challenging due to: 1) the frequent migration of the virtual machines in the data centers; 2) lack of authentication mechanisms; 3) scarcity of the SDN standards; and 4) integration of security mechanisms for the topology discovery. To this end, in this paper, we present a comprehensive survey of the topology discovery and the associated security implications in SDNs. This survey provides discussions related to the possible threats relevant to each layer of the SDN architecture, highlights the role of the topology discovery in the traditional network and SDN, presents a thematic taxonomy of topology discovery in SDN, and provides insights into the potential threats to the topology discovery along with its state-of-the-art solutions in SDN. Finally, this survey also presents future challenges and research directions in the field of SDN topology discovery.

The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user's requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.

Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact. From the network's perspective, the migration time is determined by the amount of data to be migrated and the available bandwidth used for such transfer. In this paper, we examine the problem of how to schedule the migrations and how to allocate network resources for migration when multiple VMs need to be migrated at the same time. We consider the problem in the Software-defined Network (SDN) context since it provides flexible control on routing. More specifically, we propose a method that computes the optimal migration sequence and network bandwidth used for each migration. We formulate this problem as a mixed integer programming, which is NP-hard. To make it computationally feasible for large scale data centers, we propose an approximation scheme via linear approximation plus fully polynomial time approximation, and obtain its theoretical performance bound. Through extensive simulations, we demonstrate that our fully polynomial time approximation (FPTA) algorithm has a good performance compared with the optimal solution and two state-of-the-art algorithms. That is, our proposed FPTA algorithm approaches to the optimal solution with less than 10% variation and much less computation time. Meanwhile, it reduces the total migration time and the service downtime by up to 40% and 20% compared with the state-of-the-art algorithms, respectively.

Routing and load balancing are two important factors that guarantee Quality of Service (QoS) in the Internet. Meanwhile Software Defined Networking (SDN) has introduced a new networking paradigm that make routing and load balancing policies implemented more quickly and flexibly in the system. In the SDN architecture, the "intelligence" of the network is centralized in the application layer and control layer instead of distributing in the network device in traditional network. Current studies using SDN-based routing and load balancing still stop at the scope of finding appropriate network resource for one single data flow but not considering network resource for all flows coming from different sources as the whole. In this paper, we propose a global load-balanced routing scheme, which can take advantages of global view of the SDN controller to make a global policy for routing and load balancing. The solution has been shown to outperform traditional algorithms and recent SDN-based artificial intelligence algorithms in terms of delay and network utilization.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

Due to the need to agilely cope with more complex and virtualized applications, integrating Software-Defined Networking (SDN) switches and OpenStack software platform into the data center has becomes an important enabling technology. However, the SDN controller and OpenStack have their own management interface, such as Horizon, which may not be able to meet the user's requirement on managing both platforms in one data center. For example, although the deployment of switches of a data center usually follows a layering hierarchy, the Graphical User Interface (GUI) of management system of the SDN controller, such as Ryu, shows the topology of switches in a random and messy manner. Furthermore, tightly integration of SDN and OpenStack is required for agile and real-time management. Thus, in this paper, we design and implement a management system which tightly integrates SDN and OpenStack. In the proposed management system, the topology displays SDN switches, Open vSwitches (OVS), as well as virtual machines in hierarchy of multi-rooted trees. In order to display millions of virtual machines and thousands of switches, the GUI of the proposed management system provides a scalable view for users to zoom in or zoom out the topology. The proposed management system adopts modular design and provides web-based user interface and RESTful API for interoperability. The prototype of the proposed management system is shown to demonstrate the usability and scalability of the proposed system.

On the use of the genetic programming for balanced load distribution in software-defined networks

Network functions virtualization (NFV) technology is an emerging networking technology in the telecom industry which provides flexible deployment of network services by reducing the Operating Expenses and Capital Expenses. NFV is an innovative step to virtualize network services that usually run on fixed hardware. With the concept of NFV, services like routing, load balancing, deep packet inspectors and firewalls are packaged as virtual network functions (VNFs) on commodity hardware. NFV helps the service provider to flexibly add that service in the form of a virtual machine (VMs) without upgrading or buying a new hardware on the customer end. This allows a faster deployment of the all the network services with new innovations and helps in increasing great opportunities in the field of telecom-networking. This paper provides an information about NFV technology with its characteristics, enabling technologies, benefits, use cases and challenges. NFV architecture and its comparison with Software Defined Networking (SDN) technology is also presented in this survey paper.

A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture

CBase: Fast Virtual Machine storage data migration with a new data center structure

Artificial intelligence based load balancing in SDN: A comprehensive survey

With ubiquitous presence and perpetual growth of software applications and computing devices connected to the Internet, organizations and businesses are faced with increasing challenges in operating and maintaining technologies that support their IT infrastructure. Among the goals of technology management are cost-effectiveness, energy efficiency, and secure management of information technologies, which are expected to produce maximum utility value. Virtualization has been proposed as a promising solution to the challenges facing today's IT managers. From virtualized servers to virtualized networks to virtual data centers, various virtualized systems have been explored as means of easing IT operation challenges. One of the major benefits of virtualization is the possibility of building a network of virtual machines and storage infrastructure between the virtual data center and external physical networks. A data center involves a large number of networking devices such as switches, routers, and firewalls. With traditional networking, managing networks in data centers is a complex task due to the closed architecture and vertical integration among the networking devices. Software-defined networking (SDN) is a new architectural paradigm, enabling flexible and dynamic management of networks and associated services. This paper presents the benefits of virtualization in general, the advantages of adopting virtualization in data centers, a practical implementation of virtual data centers on OpenStack, and implementing SDN to manage networks in the virtual data center.