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This book will help you to understand how the most successful tech start-ups launch and scale their services on AWS, and will teach you how you can do the same. It explains how to treat infrastructure as code, meaning you can bring resources online and offline as easily as you control your software. You will get insight into monitoring and alerting, so you can make sure your users have the best experience when using your service. You will learn how to implement automatic AWS instance provisioning using CloudFormation, deploy your application on a provisioned infrastructure with Ansible, manage infrastructure using Terraform, build and deploy a CI/CD pipeline with automated testing on AWS, understand the container journey for a CI/CD pipeline using AWS ECS and monitor and secure your AWS environment.

IoT technology applied to agriculture has produced a number of contributions in the recent years. Such solutions are, most of the time, fully tailored to a particular functional target and focus extensively on sensor-hardware development and customization. As a result, software-centered solutions for IoT system development are infrequent. This is not suitable, as the software is the bottleneck in modern computer systems, being the main source of performance loss, errors, and even cyber attacks. This paper takes a software-centric perspective to model and design IoT systems in a flexible manner. We contribute a software framework that supports the design of the IoT systems’ software based on software services in a client–server model with REST interactions; and it is exemplified on the domain of efficient irrigation in agriculture. We decompose the services’ design into the set of constituent functions and operations both at client and server sides. As a result, we provide a simple and novel view on the design of IoT systems in agriculture from a sofware perspective: we contribute simple design structure based on the identification of the front-end software services, their internal software functions and operations, and their interconnections as software services. We have implemented the software framework on an IoT irrigation use case that monitors the conditions of the field and processes the sampled data, detecting alarms when needed. We demonstrate that the temporal overhead of our solution is bounded and suitable for the target domain, reaching a response time of roughly 11 s for bursts of 3000 requests.

Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the perpetual software vendor adopts one of three different strategies: (1) an entry deterrence strategy, (2) a market segmentation strategy, or (3) a sequential dominance strategy. Surprisingly, we find that vendor competition does not always result in higher consumer surplus, and it might lead to a socially inefficient outcome under certain conditions. We further show insights into how the incumbent perpetual software vendor can defend its market position by providing incremental quality improvement through patching and/or by releasing consecutive versions with major quality upgrades. Finally, we extend our model to include the vendor’s quality improvement cost and users’ switching cost. These additional analyses help to identify the effect of different quality and cost factors on the market competitive equilibrium.

Efficient resource identification and discovery is the primary requirements for cloud computing services, as it assists in scheduling and managing of cloud applications. Cloud computing is a revolution of the economic model rather than technological. It takes advantage of several technologies that were tested and modified by replacing the local use of computers with centralized shared resources that are managed and stored by Cloud Service Providers (CSPs) in a transparent manner for Cloud Consumers (CCs). With this new use, various cloud services have appeared and it is mainly classified into three broad categories i.e., Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Each of these cloud services provides several benefits to the CCs through their respective Quality of Service (QoS) metric. Among the cloud service models, most of the QoS attribute and metric are identical and some are different. The vendors of cloud have focused their objectives on the development of scalability, resource consumption and performance, other characteristics of cloud have been ignored. While CSPs face challenging difficulties in publishing cloud services that displays their cloud resources, at the same time CCs do not have standard mechanism for cloud resource discovery, automated cloud services selection, and easy use of cloud services. In this frame, this paper puts forward a set of QoS metric for SaaS, IaaS, PaaS services and propose (i) An efficient algorithm for identifying the cloud services based on the QoS metric given by the cloud consumer using decision tree classification algorithm (ii) An efficient algorithm for Cloud service resource registry which aims to enable CSPs to register their services with its QoS attributes and (iii) A Cloud service resource discovery that search for the suitable cloud service and their attributes in the cloud service registry that meets the CCs application requirements using Split and Cache (SAC) algorithm. Our new approach makes the provisioning of cloud service possible by ease of resource identification, publication, discovery based on dynamic QoS attributes via web GUI interface backed by series of test that has validated and the proposed approach is feasible and sound. The recommended solution is important: instead of putting effort in locating, learning about the services and evaluating them, CCs can easily identify, discover the services, select and use the required cloud resources. The efficiency of our algorithms was assessed through experiments using CloudSim, which primarily decreases the response time, CPU utilization and memory consumption for identifying and searching the cloud services and increases the accuracy of the CSPs list retrieved along with their QoS attributes.

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software services’ performance requirements. In this work, we use a technical measurement of the scalability of cloud-based software services. Our technical scalability metrics are inspired by metrics of elasticity. We used two cloud-based systems to demonstrate the usefulness of our metrics and compare their scalability performance in two cloud platforms: Amazon EC2 and Microsoft Azure. Our experimental analysis considers three sets of comparisons: first we compare the same cloud-based software service hosted on two different public cloud platforms; second we compare two different cloud-based software services hosted on the same cloud platform; finally, we compare between the same cloud-based software service hosted on the same cloud platform with two different auto-scaling policies. We note that our technical scalability metrics can be integrated into a previously proposed utility oriented metric of scalability. We discuss the implications of our work.

Cloud computing provides the flexible architecture where data and resources are dispersed at various locations and are accessible from various industrial environments. Cloud computing has changed the using, storing, and sharing of resources such as data, services, and applications for industrial applications. During the last decade, industries have rapidly switched to cloud computing for having more comprehensive access, reduced cost, and increased performance. In addition, significant improvement has been observed in the internet of things (IoT) with the integration of cloud computing. However, this rapid transition into the cloud raised various security issues and concerns. Traditional security solutions are not directly applicable and sometimes ineffective for cloud-based systems. Cloud platforms’ challenges and security concerns have been addressed during the last three years, despite the successive use and proliferation of multifaceted cyber weapons. The rapid evolution of deep learning (DL) in the artificial intelligence (AI) domain has brought many benefits that can be utilized to address industrial security issues in the cloud. The findings of the proposed research include the following: we present a comprehensive survey of enabling cloud-based IoT architecture, services, configurations, and security models; the classification of cloud security concerns in IoT into four major categories (data, network and service, applications, and people-related security issues), which are discussed in detail; we identify and inspect the latest advancements in cloud-based IoT attacks; we identify, discuss, and analyze significant security issues in each category and present the limitations from a general, artificial intelligence and deep learning perspective; we provide the technological challenges identified in the literature and then identify significant research gaps in the IoT-based cloud infrastructure to highlight future research directions to blend cybersecurity in cloud.