A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing

Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers

An Evolutionary-Based Algorithm for Smart-Living Applications Placement in Fog Networks

Fog computing is an emerging model, complementing the cloud computing platform, introduced to support the Internet of Things (IoT) processing requests at the edge of the network. Smart-living IoT scenarios require the execution of multiple processing tasks at the edge of the network and leveraging on the Fog Computing approach results to be a worthwhile solution. Genetic Algorithms (GA) are a heuristic search and optimization class of techniques inspired by natural evolution. We propose two GA-based approaches for optimizing the processing task placement in a fog computing edge infrastructure aiming to support the Smart-living IoT nodes requests. The numerical results obtained in Matlab show that both GA-based approaches allow to maximize the covered areas while minimizing the resource wastage through the minimization of the overlapping area

Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach

In the last years, Internet is evolving towards the cloud-computing paradigm complemented by fog-computing in order to distribute computing, storage, control, networking resources, and services close to end-user devices as much as possible, while sending heavy jobs to the remote cloud. When fog-computing nodes cannot be powered by the main electric grid, some environmental-friendly solutions, such as the use of solar- or wind-based generators could be adopted. Their relatively unpredictable power output makes it necessary to include an energy storage system in order to provide power, when a peak of work occurs during periods of low-power generation. An optimized management of such an energy storage system in a green fog-computing node is necessary in order to improve the system performance, allowing the system to cope with high job arrival peaks even during low-power generation periods. In this perspective, this paper adopts reinforcement learning to choose a server activation policy that ensures the minimum job loss probability. A case study is presented to show how the proposed system works, and an extensive performance analysis of a fog-computing node highlights the importance of optimizing battery management according to the size of the Renewable-Energy Generator system and the number of available servers

FOG-орієнтована інтелектуальна мережа для IoT-керованих розумних домівок

Темою магістерської дисертації є дослідження можливостей підвищення функціональності FOG-орієнтованої інтелектуальної мережі для IoT-керованих розумних домівок. Робота містить 75 сторінок, зокрема 11 ілюстрацій, 2 таблиці та 24 джерела інформації. Тема магістерської дисертації є актуальною, так як через зростаючі вимоги кінцевого користувача до якості передачі інформації провайдери телекомунікаційних послуг змушені приділяти велику увагу підвищенню надійності мереж. Мета магістерської роботи полягає в пошуку балансу в продуктивності пристроїв у локальній мережі та хмарі, зменшуючи при цьому передачу даних до хмари. Об’єктом дослідження є мережа змодельована як непрямий графік, що представляє FOG вузли у вигляді сітчастої мережі При виконанні роботи застосовувалося моделювання у пакеті програмного забезпечення Matlab 2018b для встановлення FOG вузлів з різними можливостями, отже, FOG вузли будуть відрізнятися за рівнем обслуговування. У дисертації була запропонована методика координації між окремими FOG вузлами для ефективного обходження підозрілих/скомпрометованих FOG вузлів в FOG-орієнтованій інтелектуальній мережі для IoT-керованих розумних домівок.The topic of the master's dissertation is the study of the possibilities of improving the functionality of FOG-oriented intelligent network for IoT-controlled smart homes. The work contains 75 pages, including 11 illustrations, 2 tables and 24 sources of information. The topic of the master's dissertation is relevant, as due to the growing demands of the end user to the quality of information transmission, telecommunications service providers are forced to pay much attention to improving the reliability of networks. The purpose of the master's thesis is to find a balance in the performance of devices in the local network and the cloud, while reducing data transmission to the cloud. The object of study is a network modeled as an indirect graph, representing FOG nodes in the form of a network The work used modeling in the Matlab 2018b software package to install FOG nodes with different capabilities, therefore, FOG nodes will differ in the level of service. In the dissertation, the technique of coordination between separate FOG nodes for effective handling of suspicious / compromised FOG nodes in FOG-oriented intelligent network for IoT-controlled smart homes was offered

A Fog Computing Approach for Cognitive, Reliable and Trusted Distributed Systems

In the Internet of Things era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes an impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. It worth noting that the long distance in this context refers to the physical path and time interval of when data is generated and when it get processed. To explain more, by the time the data reaches a far data centre, the importance of the data can be depreciated. Therefore, the network topologies have evolved to permit data processing and storage at the edge of the network, introducing what so-called fog Computing. The later will obviously lead to improvements in quality of service via processing and responding quickly and efficiently to varieties of data processing requests. Although fog computing is recognized as a promising computing paradigm, it suffers from challenging issues that involve: i) concrete adoption and management of fogs for decentralized data processing. ii) resources allocation in both cloud and fog layers. iii) having a sustainable performance since fog have a limited capacity in comparison with cloud. iv) having a secure and trusted networking environment for fogs to share resources and exchange data securely and efficiently. Hence, the thesis focus is on having a stable performance for fog nodes by enhancing resources management and allocation, along with safety procedures, to aid the IoT-services delivery and cloud computing in the ever growing industry of smart things. The main aspects related to the performance stability of fog computing involves the development of cognitive fog nodes that aim at provide fast and reliable services, efficient resources managements, and trusted networking, and hence ensure the best Quality of Experience, Quality of Service and Quality of Protection to end-users. Therefore the contribution of this thesis in brief is a novel Fog Resource manAgeMEnt Scheme (FRAMES) which has been proposed to crystallise fog distribution and resource management with an appropriate service's loads distribution and allocation based on the Fog-2-Fog coordination. Also, a novel COMputIng Trust manageMENT (COMITMENT) which is a software-based approach that is responsible for providing a secure and trusted environment for fog nodes to share their resources and exchange data packets. Both FRAMES and COMITMENT are encapsulated in the proposed Cognitive Fog (CF) computing which aims at making fog able to not only act on the data but also interpret the gathered data in a way that mimics the process of cognition in the human mind. Hence, FRAMES provide CF with elastic resource managements for load balancing and resolving congestion, while the COMITMENT employ trust and recommendations models to avoid malicious fog nodes in the Fog-2-Fog coordination environment. The proposed algorithms for FRAMES and COMITMENT have outperformed the competitive benchmark algorithms, namely Random Walks Offloading (RWO) and Nearest Fog Offloading (NFO) in the experiments to verify the validity and performance. The experiments were conducted on the performance (in terms of latency), load balancing among fog nodes and fogs trustworthiness along with detecting malicious events and attacks in the Fog-2-Fog environment. The performance of the proposed FRAMES's offloading algorithms has the lowest run-time (i.e., latency) against the benchmark algorithms (RWO and NFO) for processing equal-number of packets. Also, COMITMENT's algorithms were able to detect the collaboration requests whether they are secure, malicious or anonymous. The proposed work shows potential in achieving a sustainable fog networking paradigm and highlights significant benefits of fog computing in the computing ecosystem

Enhanced IoT-Based Electrocardiogram Monitoring System with Deep Learning

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the ECG classification models in the cloud. Although deep learning technologies have shown their great use in extracting ECG signal features and recognizing useful patterns for diagnosis, the existing methods are found to have unacceptable levels of performance (with an accuracy capped at only 60%) in identifying certain abnormal rhythms that can cause life-threating cardiac events. In combating this deficiency, we have developed three methods that help produce, preserve, and sharpen the abnormality-relevant features needed to improve the detection of the abnormalities. These three methods are then integrated into a DNN framework for the detection of the ECG rhythms of interest. The experiment results on a publicly available data set demonstrate the effectiveness of the proposed method with the best accuracy result ever published. On the edge end of the IoT-based ECG monitoring system, both extremely noisy and almost noise-free ECGs could be locked in by the device worn by a mobile patient. However, transmitting an indiscriminate collection of noisy and noise-free ECG cycles to the cloud for the categorization of cardiac abnormalities typically leads to significant false alarm rates. Alternatively, merely relying on a single denoising or quality assessing process on the edge to cope with all the recorded ECG signals can also be problematic, as the former can catastrophically distort those noise-free sections of the ECG signal, while the latter tends to cause notable loss of meaningful clinical information by discarding the signal sections that stand a good chance to be recovered by a denoising process. In this dissertation, we present a series of machine learning based models in support of edge-level stratification and preprocessing, for selecting the ECG signals that either have clear morphologies or retain their morphologies after necessary denoising to upload to the cloud. On the other hand, signals that are useless for diagnosis will be deleted early in the signal chain to lessen the load that would otherwise be imposed on the communication network and the cloud. In specific, the severity of the noise presence in the collected ECG signals is first evaluated right on the edge, after which the ECG signals get stratified into three levels and processed accordingly: (1) Signals that are assessed to have clear morphologies are admitted to the cloud for classification; (2) Signals with significantly corrupted morphologies—caused by baseline wandering, electrode motion, and muscle artifacts—are judged to be useless for classification on the cloud and are therefore dropped right away on the edge; (3) Signals that fall between the previous two extremes with partially corrupted morphologies are warranted to go through a denoising process. This very last type of signals after denoising will be assessed again by a dedicated quality assurance algorithm, and only the denoised signal that carries recognizable diagnostic information will be sent to the cloud for classification. The performances of the proposed method are evaluated using five publicly available datasets, and the results have confirmed a saving of the network traffic and a noticeable load reduction at the cloud, which is critical to an edge-cloud computing environment. Since selective denoising, indicated above, becomes an integral part of ECG processing flow of the proposed (IoT)-based cardiac monitoring system, we have developed a set of machine-learning based denoising models that take into account of the limited power capabilities of the edge. Instead of resting on sophisticated and power-hungry denoising methods to indiscriminately cleanse ECG signals across the whole spectrum of noise conditions, our focus is placed on denoising ECG signals that have moderate noise levels and thus being able to recover useful ECG morphologies for ECG signal stratification purposes described above. Specially, we propose a series of machine-learning based denoising models that allows us (1) to select signals’ spectrums that are most relevant to diagnostically useful morphologies in the frequency domain, and subsequently, (2) recover recognizable diagnostic information from them. The experiment results on five publicly available datasets confirm the effectiveness of the proposed method