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Terraform : up and running : writing infrastructure as code
\"Terraform has become a key player in the DevOps world for defining, launching, and managing infrastructure as code (IaC) across a variety of cloud and virtualization platforms, including AWS, Google Cloud, Azure, and more. This hands-on second edition, expanded and thoroughly updated for Terraform version 0.12 and beyond, shows you the fastest way to get up and running. Gruntwork cofounder Yevgeniy (Jim) Brikman walks you through dozens of code examples that demonstrate how to use Terraform's simple, declarative programming language for deploying and managing infrastructure with a few commands. Veteran sysadmins, DevOps engineers, and novice developers will quickly go from Terraform basics to running a full stack that can support a massive amount of traffic and a large team of developers. Explore changes from Terraform 0.9 through 0.12, including backends, workspaces, and first-class expressions. Learn how to write production-grade Terraform modules. Dive into manual and automated testing for Terraform code. Compare Terraform to Chef, Puppet, Ansible, CloudFormation, and Salt Stack. Deploy server clusters, load balancers, and databases. Use Terraform to manage the state of your infrastructure. Create reusable infrastructure with Terraform modules. Use advanced Terraform syntax to achieve zero-downtime deployment.\" - back cover.
Book
Towards a QoS-aware network virtual deployment for network-as-a-service
The digital environment is constantly evolving with a growing diversity of network access technologies, such as ADSL, WiFi, 5G, LiFi, Zigbee, and the deployment of innovative services such as mobility, location, and telemetric services, as well as new applications such as smart parking, smart cities, machine-to-machine communication, and pervasive gaming. A few years ago, the services offered were dependent on the type of network, such as voice for telecommunications networks, data for computer networks, and audio/video for broadcast networks. However, service providers now need to adapt and anticipate changing consumption patterns, such as user-centric services, in their offerings. The challenge lies in how to quickly and efficiently deploy new services in this rapidly evolving technological landscape. The primary aim of this paper is to examine the impact of virtualization on the network deployment process in this new landscape. We concentrate on the integration of virtualization into the Network Deployment Process (called Virtual Network Virtual Deployment-VNVD). VNVD considers the properties of flexibility, adaptability, and dynamicity, which are crucial for Network-as-a-Service. In this context, Software-Defined Networking and Network Function Virtualization play a significant role in the design of new network architectures.
Journal Article
Q-learning based distributed denial of service detection
Distributed denial of service (DDoS) attacks the target service providers by sending a huge amount of traffic to prevent legitimate users from getting the service. These attacks become more challenging in the software-defined network paradigm, due to the separation of the control plane from the data plane. Centralized software defined networks are more vulnerable to DDoS attacks that may cause the failure of all networks. In this work, a new approach is proposed based on q-learning to enhance the detection of DDoS attacks and reduce false positives and false negatives. The results of this work are compared with entropy detection in terms of the number of received packets to detect the attack and also the continuity of service for legitimate users. Moreover, these results indicate that the proposed system detects the DDoS attack from flash crowds and redirects the traffic to the edge of the data center. A second controller is used to redirect traffic to a honeypot server that works as a mirror server. This guarantees the continuity of service for both normal and suspected traffic until further analysis is done. The results indicate an increase of up to 50% in the throughput compared to other approaches.
Journal Article
Power Consumption Analysis of Operating Systems for Wireless Sensor Networks
In this paper four wireless sensor network operating systems are compared in terms of power consumption. The analysis takes into account the most common operating systems—TinyOS v1.0, TinyOS v2.0, Mantis and Contiki—running on Tmote Sky and MICAz devices. With the objective of ensuring a fair evaluation, a benchmark composed of four applications has been developed, covering the most typical tasks that a Wireless Sensor Network performs. The results show the instant and average current consumption of the devices during the execution of these applications. The experimental measurements provide a good insight into the power mode in which the device components are running at every moment, and they can be used to compare the performance of different operating systems executing the same tasks.
Journal Article
Orchestrating Smart Business Network dynamics for innovation
This paper proposes the concept of
Orchestrating
Smart Business Networks (SBN) as a managerial function that shapes structural dynamics for innovation. On the basis of commitment, as one way to exert power, and dynamic capacities, the suggested managerial function can develop a path of efficiency towards innovation by managing the SBN's structural dynamics, the network boundaries and digital platforms. The network's centripetal and centrifugal forces are used as the units of analysis, and this managerial function is then tested with empirical results obtained from a case study.
Journal Article
Percolation in real interdependent networks
The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Whereas theoretical methods of direct applicability to real isolated networks exist, the frameworks developed so far in percolation theory for interdependent network layers are of little help in practical contexts, as they are suited only for special models in the limit of infinite size. Here, we introduce a set of heuristic equations that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network. We demonstrate that percolation transitions in interdependent networks can be understood by decomposing these systems into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a smooth percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in small networks. We provide examples of real systems that have developed interdependent networks sharing cores of ‘high quality’ edges to prevent catastrophic failures.
Our understanding of how catastrophe propagates in multi-layered networks relies on theories that apply only to infinite systems. Reducing the interconnected networks to a set of decoupled graphs provides a route to probing finite sizes.
Journal Article
Performance Evaluation Using RYU SDN Controller in Software-Defined Networking Environment
Software-defined networking (SDN) is a new approach that overcomes the obstacles which are faced by conventional networking architecture. The core idea of SDN is to separate the control plane from the data plane. This idea improves the network in many ways, such as efficient utilization of resources, better management of the network, reduced cost, innovation with new evolution, and many others. To manage all these changes, there is a great need for an efficient controller to improve the utilization of resources for the better performance of the network. The controller is also responsible for the analysis and monitoring of real-time data traffic. There is a great need for a high-performance controller in networking industries, data centres, academia, and research due to the tremendous growth of distributed processing-based real time applications. Therefore, it is crucial to investigate the performance of an open-source controller to provide efficient traffic routing, leading to improved utilization of resources for the enhanced performance metrics of the network. The paper presents an implementation of SDN architecture using an open-source RYU SDN controller for the network traffic analysis. The proposed work evaluates the performance of SDN architecture based custom network topology for a node to node performance parameters such as bandwidth, throughput and roundtrip time, etc. The simulation results exhibit an improved performance of the proposed work in comparison to the existing default network topology for SDN.
Journal Article
An operating system for executing applications on quantum network nodes
The goal of future quantum networks is to enable new internet applications that are impossible to achieve using only classical communication
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and functionalities
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on quantum processors have been performed in ad hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here we report on the design and implementation of an architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the capability of the architecture to execute applications in high-level software by implementing it as a quantum network operating system—QNodeOS—and executing test programs, including a delegated computation from a client to a server
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on two quantum network nodes based on nitrogen-vacancy (NV) centres in diamond
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. We show how our architecture allows us to maximize the use of quantum network hardware by multitasking different applications. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an extra driver for QNodeOS for a trapped-ion quantum network node based on a single
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. Our architecture lays the groundwork for computer science research in quantum network programming and paves the way for the development of software that can bring quantum network technology to society.
A new quantum operating system architecture is described that is capable of executing applications on quantum networks in high-level software, which is a step towards bringing quantum network technology to society.
Journal Article
An intelligent fault detection approach based on reinforcement learning system in wireless sensor network
The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network’s weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network’s existence. Consequently, it extends the network’s lifetime, overcomes the data transmission’s energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.
Journal Article
SNORT based early DDoS detection system using Opendaylight and open networking operating system in software defined networking
Software-defined networking (SDN) is an approach in the network that provides many advantages with the help of separating the intelligence of the network (controller) with the underlying network infrastructure (data plane). But this isolation also gives birth to many security concerns; therefore, the need to protect the network from various attacks is becoming mandatory. Distributed Denial of Service (DDoS) in SDN is one such attack that is becoming a hurdle to its growth. Before the mitigation of DDoS attacks, the primary step is to detect them. In this paper, an early DDoS detection tool is created by using SNORT IDS (Intrusion Detection System). This tool is integrated with popularly used SDN controllers (Opendaylight and Open Networking Operating System). For the experimental setup, five different network scenarios are considered. In each scenario number of hosts, switches and data packets vary. For the creation of different hosts, switches the Mininet emulation tool is used whereas for generating the data packets four different penetration tools such as Hping3, Nping, Xerxes, Tor Hammer, LOIC are used. The generated data packets are ranging from (50,000 per second–2,50,000 per second) and the number of hosts/switches are ranging from (50–250) in every scenario respectively. The data traffic is bombarded towards the controllers and the evaluation of these packets is achieved by making use of Wireshark. The analysis of our DDoS detection system is performed on the basis of various parameters such as time to detect the DDoS attack, Round Trip Time (RTT), percentage of packet loss and type of DDoS attack. It is found that ODL takes minimum time to detect the successful DDoS attack and more time to go down than ONOS. Our tool ensures the timely detection of fast DDoS attacks which delivers the better performance of the SDN controller and not compromising the overall functionality of the entire network.
Journal Article