New Frontiers in Cloud Computing and Internet of Things

Over the last many decades, developments in computing and networking technologies have been attributed to the underlying Cloud computing and IoT technologies. Cloud and Edge/Fog computing enable IoT-based applications such as smart cities, emergency healthcare applications, and autonomous vehicles, as illustrated in Fig. 1.1. Therefore, a fundamental understanding of technologies associated with Cloud and IoT and the capabilities and limitations are crucial. This chapter explores state-of-the-art Cloud and IoT technologies. It provides a detailed discussion of different services, critical technologies, and open challenges that need to be addressed to make Cloud and IoT technologies robust and secure.

Nowadays cloud computing adoption as a form of hosted application and services is widespread due to decreasing costs of hardware, software, and maintenance. Cloud enables access to a shared pool of virtual resources hosted in large energy-hungry data centers for diverse information and communication services with dynamic workloads. The huge energy consumption of cloud data centers results in high electricity bills as well as emission of a large amount of carbon dioxide gas. Needless to say, efficient resource management in cloud environments has become one of the most important priorities of cloud providers and consequently has increased the interest of researchers to propose novel energy-saving solutions. This chapter presents a scientific and taxonomic survey of recent energy-efficient cloud resource management’ solutions in cloud environments. The main objective of this study is to propose a novel complete taxonomy for energy-efficient cloud resource management solutions, review recent research advancements in this area, classify the existing techniques based on our proposed taxonomy, and open up new research directions. Besides, it reviews and surveys the literature in the range of 2015 through 2021 in the subject of energy-efficient cloud resource management techniques and maps them to its proposed taxonomy, which unveils novel research directions and facilitates the conduction of future researches.

Large organizations and business centers use cloud services as a computing technology for their business purposes. Nevertheless, the use of cloud computing has resulted in creation of huge data centers. The major issues that occur in data centers are managing the infrastructural resources, maintaining the cost of applications (tasks), security, and high usage of energy. It represents cloud computing provides resources based on the principle of virtualization and pay-as-you-go model. The resources such as storage, CPU, network, and memory that are available in virtual machine need to be monitored frequently. This resources management has become a wide area of research. The optimization algorithm called Genetically Enhanced Shuffling Frog Leaping Algorithm (GESFLA) is implemented for the VM allocation and execution of tasks. The idea behind the proposed work is to address some of the issues such as minimizing the power consumption, costs of the running application, and to optimize the resource usage. Cloudsim toolkit is used to find the efficiency of this proposed algorithm with a Genetic Algorithm and Particle Swarm Optimization (GAPSO). Experiments are conducted using PlanetLab workload and Google Cluster Datasets which is very huge data. The experimental results indicate GESFLA’s superiority over GAPSO in terms of resource usage ratio, time to migrate the VMs, and total energy consumption.The proposed algorithm increases the performance of data center by maximizing resource utilization by 16% and migration time by 17%. Also, energy consumption is reduced in comparison with the existing algorithm GAPSO by 6%.

Scheduling streaming applications in Data Stream Management Systems (DSMS) have been investigated for years. As the deployment platform of DSMS migrates from on-premise clusters to elastic computing clouds, new requirements have emerged for the scheduling process to tackle workload fluctuations with heterogeneous cloud resources. Resource-efficient scheduling is to improve cost efficiency at runtime by dynamically matching the resource demands of streaming applications with the resource availability of computing nodes. In this chapter, we model the scheduling problem as a bin-packing variant and propose a heuristic-based algorithm to solve it with minimised inter-node communication. We also present a prototype scheduler named D-Storm, which extends the original Apache Storm framework into a self-adaptive MAPE-K (Monitoring, Analysis, Planning, Execution, Knowledge) architecture and validates the efficacy and efficiency of our scheduling algorithm. The evaluation carried out on real-world applications such as Twitter Sentiment Analysis proves that D-Storm outperforms the existing resource-aware scheduler and the default Storm scheduler in terms of reducing inter-node traffic and application latency, as well as yielding resource savings through task consolidation.

The exponential growth of Internet of Things (IoT) has given rise to a new wave of edge computing due to the need to process data on the edge, closer to where it is being produced and attempting to move away from a cloud-centric architecture. This provides its own opportunity to decrease latency and address data privacy concerns along with the ability to reduce public cloud costs. The serverless computing model provides a potential solution with its event-driven architecture to reduce the need for ever-running servers and convert the backend services to an as-used model. This model is an attractive prospect in edge computing environments with varying workloads and limited resources. Furthermore, its setup on the edge of the network promises reduced latency to the edge devices communicating with it and eliminates the need to manage the underlying infrastructure. In this book chapter, first, we introduce the novel concept of serverless edge computing, then, we analyze the performance of multiple serverless platforms, namely, OpenFaaS, AWS Greengrass, Apache OpenWhisk, when set up on the single-board computers (SBCs) on the edge and compare it with public cloud serverless offerings, namely, AWS Lambda and Azure Functions, to deduce the suitability of serverless architectures on the network edge. These serverless platforms are set up on a cluster of Raspberry Pis, and we evaluate their performance by simulating different types of edge workloads. The evaluation results show that OpenFaaS achieves the lowest response time on the SBC edge computing infrastructure, while serverless cloud offerings are the most reliable with the highest success rate.

With the advances in monitoring techniques and storage capability in the cloud, a high volume of valuable monitoring data is available. The collected data can be used for profiling applications behavior and detecting anomalous events that identify unexpected problems in the normal functioning of the system. However, the fast-changing environment of the cloud brings a need for fast and efficient analytic solutions to monitor the cloud system for its correct operational behavior. The isolation-based method is an effective approach for detecting anomalies. This method randomly samples the data and builds several isolation-trees (iTrees) data structures to find anomalous records. However, a common challenge of iTrees as well as other anomaly detection algorithms is dealing with high-dimensional data that can impact the accuracy and execution time of the process. This is an important issue for cloud-hosted applications where a variety of problems are constantly changing the normal pattern of features from low-level network data to high-level application performance. Therefore, refining the feature space for the removal of irrelevant attributes is a critical issue.

In this chapter, we introduce an iterative iTree based Learning (ITL) algorithm to handle high-dimensional data. ITL takes the advantage of iTree structure to learn relevant features for detecting anomalies. Initially, it builds iTrees making use of all features of the data. Then, in the iterative steps, it refines the set of the features to find the most relevant ones by selecting highly ranked anomalies discovered in the previous iteration. Experiments are conducted to validate the performance of our proposed ITL method on several benchmark datasets. The results show that ITL can achieve significant speedups with the appropriate choice of the number of iTrees while achieving or exceeding the area under the curve (AUC) values of other state-of-the-art isolation-based anomaly detection methods.

Data centres in contemporary times are essential as the supply of data increases. Data centres are areas where computing systems are concentrated for facilitating data processing, transfer and storage. At present, traditional data centres have moved more towards the cloud model—thereby making the processing, storage and harnessing of data more manageable and more accessible via the utility and subscription-based model of computing services. From the administrative point of view, cloud data centres are complex systems and hard to grasp and require large amounts of time to analyse different aspects of the cloud data centre such as maintenance and resource management. For a cloud data centre admin, this could be a challenging problem and a highly time-consuming task. Accordingly, there is a need to improve the useability of cloud data centre monitoring and management tools, and the digital twin could fulfil this need. This book chapter’s primary objective is to construct a digital twin—a 3D visualisation and monitoring tool—of a cloud data centre managed by OpenStack, the well-known open-source cloud computing infrastructure software. To evaluate our proposed tool, we garner feedback on the digital twin’s useability compared to the OpenStack dashboard. The input will be received from cloud data centre experts as they test the digital twin and answer various questions in an interview. The study results show that our proposed digital twin will help data centre admins better monitor and manage their data centres. It will also facilitate further research and implementation of the digital twin of data centres to improve usability.

With the advent of technology, the development of IoT system has increased over the years, especially in IIoT. This chapter focuses on the use of an IoT framework and its basic components for various purposes. This chapter describes the IoT protocols, the IoT architecture, its elements, and the different models to address industrial issues when developing Internet of Things (IoT) systems. The standard protocols and their component designs have been standardized to enable an IoT system, including various parameters such as frequency range, bandwidth, power, data transmission rate, and maximum radiated power are examined here. With the implemented designs, it becomes simple to understand and create a framework or optimize parameters with a proprietary one that enables the experimental problems to be reliable and scalable.

The Internet of Things (IoT) comprises a set of complementary technologies which offer unprecedented opportunities for interacting with the physical environment. Faced with multiple pressures on our physical wellbeing such as climate change, habitat and species loss, increasing urbanisation and global pandemics, the IoT paradigm appears to present some timely solutions. This chapter discusses the fit between the characteristics of IoT and the needs of sustainable development. It explores the potential for unintended consequences and concludes with some suggestions for the role of IoT in the global transition to sustainability.

The utility of Internet of Things (IoT) and cloud computing has become indispensable in various industrial sectors. This chapter provides a brief insight into the application of IoT and cloud computing in education, entertainment, transportation, manufacturing, healthcare and agriculture. Besides, it also discusses the future opportunities for such emerging technologies in these sectors. Further, there have been massive developments in the field of cloud computing which has played a crucial role in ensuring a proper learning environment and resources.

The entertainment industry has also seen changes similar to the ones in the education industry. The transportation and logistics industry was impacted heavily during the COVID-19 pandemic. With technologies breaking benchmarks time and again, technology will soon make a huge difference with respect to transportation. The need and ability to control various aspects of a manufacturing pipeline remotely and wirelessly has been a domain of interest for many researchers and scholars and the need is increasing day by day. The healthcare industry envisions transforming a hospital-centric approach into a complete healthcare experience at the comfort of your home using the latest technologies including IoT and cloud computing. This crisis due to the pandemic has forced farmers to look towards technology; therefore, IoT-based agricultural solutions have become popular all across the world. Finally, this chapter also discusses the future opportunities provided by these technologies in handling the day-to-day livelihood during such pandemic disasters.

Data turn out to be an imperative production factor that could be equivalent to material possessions and human capital. The rapid growth of cloud computing and IoT triggers the sharp growth of data. Most existing clouds provide a better-coordinated analytics workflow in a rationalized and automatic mode but then again is deficient of prognostic or real-time proficiencies. Therefore, Big Data Analytics on IoT-centered educational scheme is projected. The IoT-centered education monitoring system encompasses of “Internet of Education sensor things.” It produces enormous volumes of data that could not be managed by educators. The educator’s imperative apprehension is that they require to make serious verdicts about their student’s progress from these enormous capacities of educational information. The educators have to separate out the information about one precise student from the overflow of educational information arriving from the immense number of students. A framework is proposed in this chapter that will be utilized to organize the educational information of students into the Cloud. This chapter makes use of RFID technology to capture the frequency of students at a strategic location on the campus such as the library, lecture theatre, hostel, and cafeteria and send their data to the cloud for analysis. The proposed architecture provides an overview of the student’s progress toward their academic achievement by monitoring their activities on campus based on the location visited.

Protection of equipment and the feeder when a large amount of electric power energy is generated in the distribution network becomes more complex which requires more attention for the safety of personnel and equipment. The focus of this chapter is on the protection system of a grid designed to be integrated into the smart environment-based Internet-of-Things technologies. The purpose of this chapter is to monitor the effect of faults on the overcurrent protection scheme of the distribution network and prevent the network by isolating the affected part of the network via the Internet for the safety of equipment and personnel. The impact of faults at the different buses of the distribution network, the zoning of faults, and the coordination of the protection relay are well observed by carrying out load flow analysis, and faults are injected at different buses of the system. The analysis of the result revealed at the end of this chapter how the network responds to the monitoring of faults through the Internet. This work helps to attain the United Nation’s Sustainability Development Goal (SDG) – 7 and affordable and clean energy in developing countries, especially in sub-Saharan Africa.

The enormous volumes of IoT-based datasets in the cloud system produced by medical industries are too multifaceted and in bulky capacity to be handled and scrutinized by conventional analytics procedures. There is a necessity to establish a powerful system for scrutinizing and extracting vital information from this multifaceted data. This chapter presents a technique of utilizing Naïve Bayes classifier for predicting heart disease. The purpose of the investigation is to implement an analytics method for analyzing IoT-based medical datasets in the cloud system. The Naïve Bayes classifier model was used as the classification model, and it was implemented in WEKA 3.8. The system accuracy was evaluated using a k-fold (k = 10) cross-validation method having 90% of the data as trained datasets and 10% of the data as the tested datasets. The system obtained an accuracy of 76%. A final analysis of the entire dataset using 100% of the instances as trained datasets was conducted and gave an accuracy of 77%. Confusion matrix was utilized for the system assessment, and it was deduced that the Naïve Bayes classifier correctly classified 232 instances out of 299 instances and incorrectly classified 67 instances.

The emerging technology and conventional usefulness practice of medicine in healthcare are augmented with the IoT framework. IoT provides the characteristics for easy doing of businesses and helps doctors and medical staff to offer the quality of services for the patients. The different form of information self-processed from the large set of real-time cases increases in both ways such as precision and medical data volume. Moreover, the quality of services related to medical care has been enhanced by the intelligent use of the IoT devices in healthcare framework. Presently, the IoT plays a significant role in the healthcare industry, where doctors and medical staff get empowered to provide efficient and best care. Many facilities incorporated in the healthcare industry makes it possible to monitor in real time with the aid of the intelligent medical IoT devices. It makes it imaginable to sync the patient’s data through mobile application and can be accessed from anywhere, especially for the patients residing in the remote locations. Despite of the current challenges, it has been providing constant vigilance about the role of IoT, artificial intelligence, machine learning, data analysis, etc., in the healthcare sector.