Cloud Architecture Research Articles

An Enhanced Load Balancing Technique for Big-data Cloud Computing Environments

The need for cloud computing load balancing is a peculiar area of interest for researchers because it affects both the quality of service provided to users and resource utilisation on the part of cloud service providers. Due to the requirement to minimise processing costs, enhance throughput, improve resource efficiency, and optimise cloud node arrangement, existing cloud computing load balancing methods have been found to be restricted in their capacity to manage big-data cloud system load distribution. This research developed a novel Central-Regional Architecture Based Load Balancing Technique (CRLBT) different from the known central, distributive, and hierarchical cloud architectures. The proposed technique was developed by combining a formulated throughput maximisation algorithm with the algorithms; Throughput Maximised-Particle Swarm Optimisation (TM-PSO) and Throughput Maximised-Firefly optimisation (TM-Firefly). The developed technique was implemented using the MATLAB R2018 software package. The performance of the CRLBT in comparison to the already-in-use PSO and Firefly algorithms was evaluated using response time, throughput, job rejection ratio, and CPU utilisation rate. The significance of the improvement in load balancing brought about by the new approach was further assessed using a statistical T-Test. The results showed that the proposed CRLBT significantly outperformed the PSO and Firefly techniques regarding response time, throughput CPU utilisation rate, and task rejection ratio. Finally, significant improvements in response time, tax rejection ratio, CPU utilisation rate, and network throughput proved the ability of the proposed technique to handle task-resource distribution of big-data cloud centres superiorly.

Hybrid Edge Cloud: A Pragmatic Approach for Decentralized Cloud Computing

The massive growth of connected devices, including sensors and machines, is revolutionizing every aspect of human life. The socioeconomic impacts are significant and have already transformed many industries. In this article, we discuss some of the challenges of the explosion of devices and exponential growth in personal digital content and machine generated data. The centralized cloud architecture adopted in the early days of mobile Internet was designed primarily to allow access to data stored on the worldwide web. Today, use cases have evolved significantly. Humans and devices are now producing most of the data consumed on the Internet. As a result, the existing centralized cloud infrastructure is no longer efficient or sustainable. There is significant waste of network bandwidth to send terabytes of data to server farms that may be hundreds of kilometers away from the source and/or destination of data. The prevalent centralized cloud architecture does not adequately leverage massive amounts of computing resources on smart devices, which are idle most of the time. Moreover, despite all the efforts to reduce network latencies, communication to server farms is a major bottleneck for latency in many applications. We introduce a novel architectural approach to cloud decentralization called hybrid edge cloud (HEC) that minimizes network bandwidth usage, reduces communication latencies, and leverages resources on smart devices to reduce the burden on server farms and other centralized computing resources. HEC combines the benefits of new network technologies such as 5G and WiFi 6 in private and public clouds to leverage computing resources on smart devices to build a sustainable decentralized infrastructure for the hyper-connected world.

The Intelligent Detection Method of Electric Energy Meter Overload Operating and Collaborative Edge Computing for Social Internet of Things Systems

This study describes a line cloud architecture and IDM-based energy metering system to replace existing meter reading methods. They can regularly monitor meter readings without sending someone to each residence, and the bill is automatically sent to each user via IDM. If the consumer fails to pay the bill, the service provider can cut off the supply; this technology will prevent the illicit use of electricity, often known as power detection, and locate line faults rapidly and precisely without the need for human intervention. Because of the increased deployment of energy meters, much data on electric energy is used. Developing cloud architecture technologies could utilize this data better to prevent power detection. In this paper, an intelligent detection method of electric energy meter overload functioning state based on cloud architecture (IDM EEMOF-CA) is provided in detail and utilized to identify electric energy detection. There are currently no studies involving the use of IDM EEM-CA to detect power exposure to the authors' knowledge.

Large-Scale Multimedia Communication on A Cloud Computing Platform

Abstract: This research examines the creation of a multimedia communication platform to expand the size of multimedia communication based on cloud computing (CC). The research focuses on high-quality material, multimedia profit avenues, and copyright awareness. Big data diversion under cloud computing optimizes data grouping and pattern identification. Cloud computing's typical large data shunting technology uses open-source embedded system architecture. The connecting coil ties the shunting system's switch to the cloud computing platform's switch, resulting in data loss and corruption. Using this method, it has been shown that CC cannot reach the ideal resource utilization, SLA violation rate, and energy use. Experiments reveal a divergence from this research. The major goal of media development is now the growth and self-awareness of media. Largescale multimedia communication needs an architecture for cloud computing with dynamic growth, efficient processing, and flexible resource scheduling

Educational 5G Edge Computing: Framework and Experimental Study

Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency.

Cost-effective stochastic resource placement in edge clouds with horizontal and vertical sharing

To support distributed upper-level services such as those in edge or fog computing, a hierarchical and distributed multicloud architecture is introduced to replace the traditional centralized cloud architecture. The intra-time slot demand fluctuation with corresponding multidimensional resource sharing are neglected for efficiency reasons in the existing algorithms. By considering them, the service provider has the potential to obtain better solution, but also makes the scheduling problem a highly complicated stochastic optimization one. To effectively and efficiently solve the problem containing a nonlinear objective and constraints with heterogeneous nonlinear pricing functions, the general stochastic scheduling problem is formulated and its structure is exploited to obtain the base solution quickly, which is further applied with solution closure for fast convergence in the following configurable metaheuristic searching phrase. The experiments with many different problem settings show its superiority over existing algorithms with more than 30% revenue improvement. Therefore, the proposed algorithm can be used as a good supplement to the existing methods.

A Novel CNN-TLSTM Approach for Dengue Disease Identification and Prevention using IoT-Fog Cloud Architecture.

One of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease expansion is mainly due to the different factors such as climate change, socioeconomic factors, viral evolution, globalization, etc. The unavailability of certain antiviral therapy and specific vaccine increases the risk of the dengue disease spreading even further. This arises the need for a novel technique that overcomes the complexities associated with dengue disease prediction such as low reporting level, misclassification, and incompatible disease monitoring framework. This paper mainly overcomes the above-mentioned problems by integrating the Internet of Things (IoT), fog-cloud, and deep learning techniques for efficient dengue monitoring. A compatible disease monitoring framework is formed via the IoT devices and the reports are effectively created and transferred to the healthcare facilities via the fog-cloud model. The misdiagnosis error is overcome in this paper using the novel Hybrid Convolutional Neural Network (CNN) with Tanh Long and Short Term Memory (TLSTM) based Adaptive Teaching Learning Based Optimization (ATLBO) algorithm. The ATLBO optimized CNN-TLSTM architecture mainly analyzes the dengue-related parameters such as Soft Bleeding, Muscle Pain, Joint Pain, Skin rash, Fever, Water Site, Carbon Dioxide, Water Site Humidity, Water Site Temperature, etc. for an efficient clinical decision making and timely disease diagnosis. The experimental results are conducted using a real-time dataset and its performance is validated using various performance metrics. When compared in terms of different statistical parameters such as accuracy, f-measure, mean square error, and reliability, the proposed method offers superior results in the case of dengue disease detection than other existing methods. The ATLBO optimized hybrid CNN-TLSTM shows an accuracy of 96.9%, a precision of 95.7%, recall of 96.8%, and F-measure of 96.2% which is relatively high when compared to the existing techniques. The proposed model is capable of identifying the patients in a certain geographical region and preventing the disease emergency via immediate disease diagnosis and alerting the healthcare officials to offer the stipulated services.

FedSup: A communication-efficient federated learning fatigue driving behaviors supervision approach

With the proliferation of smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. Besides, due to the limitation of communication and computing resources, it is difficult to carry out training and data transmission on vehicles. To tackle these challenges, we propose FedSup, a communication-efficient federated learning method for fatigue driving behaviors supervision. Inspired by the resources allocation mechanism in edge intelligence, FedSup dynamically optimizes the sharing model with tailored client–edge–cloud architecture and reduces communication overhead by a Bayesian Convolutional Neural Network (BCNN) data selection strategy. To improve the sharing model optimize efficiency, we further propose an asynchronous parameters aggregation algorithm to automatically adjust the mixing weight of each edge model parameter. Extensive experiments demonstrate that the FedSup method is suitable for IoV scenarios and outperforms related federated learning methods in terms of communication overhead and model accuracy.

Design and Implementation of Online Water Quality Monitoring System

Water quality detection plays an important role in water pollution alarm, water source pollution detection and water source diagnosis and treatment. The online water quality monitoring system based on the Internet of Things technology can monitor the water quality in real time and dynamically, and can serve the strategic requirements of the country for water resources. Currently, the water quality on-line monitoring system is mainly composed of upper computer system and lower base station in domestic researches. This mode of construction has deficiencies such as lack of channel diversity, large investment in communication network, and insufficient capacity of data. Aiming at these problems, this study attempts to use LoRa network technology, cloud architecture combined with water quality monitoring sensors to build a low cost and high reliable water quality monitoring system. Indexes of water quality are collected by intelligent sensors, and are transmitted to cloud server through LoRa network. They can be intelligently analysed by the technology of big data. The system includes both mobile and computer terminals. Indexes of water quality can be monitored timely and accurately by the system, which can further enhance the omnidirectional intelligent management of rivers. By case analysis to verify that the system has certain value of promotion and application.

Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping

With the wide penetration of smart robots in multifarious fields, the simultaneous localization and mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this article promotes the emerging edge-computing paradigm into multirobot SLAM and proposes RecSLAM, a multirobot laser SLAM system that focuses on accelerating the map construction process under the robot–edge–cloud architecture. In contrast to the conventional multirobot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots’ raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multirobot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39.31% processing latency reduction over the state of the art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness.

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs).

The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end–edge–cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.

A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection

COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a Multi-Objective Modified Heat Transfer Search (MOMHTS) optimized Hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.

Privacy Shield: A System for Edge Computing using Asynchronous Federated Learning

Due to increase in IoT devices, the data produced every day are also increasing rapidly. The growth in data means more processing and more computations are required without delay. This introduced us to a new horizon of the computing infrastructure, i.e., edge computing. Edge computing gained prominence as a solution to the problem of delayed transmission, processing, and response by the cloud architecture. It was further augmented with the field of artificial intelligence. It has become a topic in research with preservation of data privacy as the focal point. This paper provides Privacy Shield, a system for edge computing using asynchronous federated learning, where multiple edge nodes perform federated learning while keeping their private data hidden from one another. Contrary to the pre-existing distributed learning, the suggested system reduces the calls between the edge nodes and the main server during the training process ensuring that there is no negative impact on the accuracy of the mode. We used different values of Ω that affect the accuracy and the compression ratio. In the interval of Ω = [0.2, 0.4], the C-ratio increases and the value of Ω and the compression ratio value are directly proportional regardless of the fluctuations. We analyzed the accuracy of the model which increases along with the increase in compression ratio. Compressing the gradient communications reduces the likelihood of the data of being attacked. As the nodes train asynchronously on limited and different kinds of samples, the binary weights adjustment method was used to handle the resulting “unbalanced” learning. The MNIST and Cifar10 dataset were used for testing in both tasks, respectively.

Computer Security Issues and Legal System Based on Cloud Computing.

To effectively improve the security and accuracy of computer information storage, a computer security problem and legal system based on cloud computing are proposed. Firstly, this article details the evolution of cloud computing, its characteristics, architecture, and application status of cloud computing in detail. Second, we discussed security strategies to ensure the confidentiality and integrity of cloud computing information, focuses on the data encryption technology of cloud data security, and designs and implements the data backup and recovery system based on the cloud platform. The core layers of the system are the system layer and data operation layer. The system uses multithreading technology based on epoll and thread pool to improve the efficiency of data transmission. At the same time, the basic visual page is realized, and users can use the page to create a convenient operating system. Finally, the system is built in the laboratory environment and tested as a whole. The test results show that through the performance comparison with the current commonly used systems, it is found that the system in this paper has a certain improvement in data transmission rate, but the utilization rate of node CPU is as high as 40%, which leads to certain requirements for node CPU performance. Therefore, the system meets the functional requirements proposed in the design. Compared to the existing system, its performance has been found to meet the actual requirements of use, proving that the system is accessible and efficient.

A TAXONOMY OF SECURITY AND RESEARCH CHALLENGES IN CLOUD COMPUTING

Cloud computing is delivered as a storage service by third party. It gains wide acceptance from various Business organizations & Information Technology (IT) Industries. Cloud computing provides various services to users through the internet; those services are like Applications, computation, and storage etc. In spite of these advantages, cloud technology faces different types of privacy and security related issues. These issues become major barriers to adopt cloud technology into various organizations. This survey paper addresses the cloud architecture, various security and privacy issues, challenges and threats, attacks, and future research directions to overcome the security and privacy related problems in the cloud environment.

Research and Application of Cloud Platform-Oriented Intelligent Information Management System

In order to realize unified deployment and system planning of various types of information and diverse needs, the author proposes an educational information management platform using cloud computing technology. The author introduces the layered architecture and platform advantages of cloud computing technology. Combining the advantages of cloud computing technology with the characteristics of the construction of educational information management platforms in colleges and universities, the B/S architecture is established, and the architecture model of the educational information management platform based on cloud computing technology is designed; the model includes user access layer, SaaS layer, PaaS layer, and IaaS layer, then analyze the functional modules of the platform design. Based on the Linux operating system server terminal, using Apache as the Web application server, the JSP system page was developed and established a SQL Server database, which includes information tables such as users, departments, teachers, courses, teaching resources, and statistical analysis. Finally, the application effect of the education information management platform based on cloud computing technology is analyzed. Experimental results show that the platform was tested, and it was found that the accuracy of system data operation was greater than 90%; the system ran stably and smoothly for more than 2 hours; 200 people could use the system at the same time; the system response delay was less than 3 seconds. Conclusion. The educational information management platform can be user-oriented, meet the personalized application needs of teachers and students in schools, and provide strong technical support for teaching experience innovation.

Migrating on-premise application workloads to a hybrid cloud architecture

Organizations are moving towards adapting cloud strategies for various reasons. The important benefits realized with the adaption of cloud computing are - scalability, availability, performance and cost. With the benefits, come the challenges such as - security, privacy, regulatory compliances, trust, etc. With adoption of cloud, organizations are relieved from hardware infrastructures up to a large extent as well as overheads of managing, operating and physical security of infrastructure hardware. Having legacy applications on-premise, enterprises are looking forward to modernize the applications in terms of planning to strategize the future vision, need for scaling those applications, be competitive in the market with an ability as well as capability to cope and adapt the new demand and changes with ease, flexibility and cost effectiveness. One of the immediate adaptions with the cloud that the enterprises are looking forward is migrating their on-premise workloads to the cloud infrastructure, so that they can realize the benefit of cost-effective solutions along with minimizing operational and management overheads. In this paper we are proposing a hybrid cloud architecture that an enterprise may adapt while considering migrating one of its legacy applications’ workload to the cloud environment; with minimal changes and migration effort.

Need for Cloud Security Audit

Adoption of cloud architecture has increased tremendously over the last few years. The cloud market is currently placed at around USD 250 billion and as per some estimates this is supposed to grow to a trillion dollars by 2030 with a CAGR of around 17 per cent. With such massive scale of implementation of cloud based technologies comes the responsibility of ensuring integrity and reliability of applications hosted on the cloud. Breach of systems and data not only causes monetary loss to an organization but also impacts its reputation and credibility. Hence, security of cloud based solutions, and audit of such security is of critical importance. This article focuses on some basic aspects of cloud security audit.

Multi Cloud & Hybrid Cloud Architecture Patterns for Fintech Space

This technical report aims at exploring multi-cloud and hybrid cloud architecture concern to the fintech industry. First, it provides a brief introduction to Cloud computing; second, it explores the fundamentals of multi-cloud and hybrid cloud and discusses them in the context of financial technology organizations. This paper compares the strengths and weaknesses of these architectures, identifies future trends, and provides guidance for fintech firms seeking cloud migration best practices.

Multi Cloud & Hybrid Cloud Architecture Patterns for Fintech Space

This technical report aims at exploring multi-cloud and hybrid cloud architecture concern to the fintech industry. First, it provides a brief introduction to Cloud computing; second, it explores the fundamentals of multi-cloud and hybrid cloud and discusses them in the context of financial technology organizations. This paper compares the strengths and weaknesses of these architectures, identifies future trends, and provides guidance for fintech firms seeking cloud migration best practices.