Cloud Computing Architecture Research Articles

Conceptual Oriented Analysis on the Security based on the SaaS Cloud Computing Architecture for the Cyber Security Issues

Conceptual Oriented Analysis on the Security based on the SaaS Cloud Computing Architecture for the Cyber Security Issues

Mobile Edge Computing

The overwhelming growth of resource-intensive and latency-sensitive applications trigger challenges in legacy systems of mobile cloud computing (MCC) architecture. Such challenges include congestion in the backhaul link, high latency, inefficient bandwidth usage, insufficient performance, and quality of service (QoS) metrics. The objective of this study was to find out the cost-efficient design that maximizes resource utilization at the edge of the mobile network which in return minimizes the task processing costs. Thus, this study proposes a cooperative mobile edge computing (coopMEC) to address the aforementioned challenges in MCC architecture. Also, in the proposed approach, resource-intensive jobs can be unloaded from users' equipment to MEC layer which is potential for enhancing performance in resource-constrained mobile devices. The simulation results demonstrate the potential gain from the proposed approach in terms of reducing response delay and resource consumption. This, in turn, improves performance, QoS, and guarantees cost-effectiveness in meeting users' demands.

English

English

A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7%–99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress.

WSN-AI based Cloud Computing Architectures for Energy Efficient Climate Smart Agriculture with Big Data analysis

WSN-AI based Cloud Computing Architectures for Energy Efficient Climate Smart Agriculture with Big Data analysis

Calculating the trust of providers through the construction weighted Sec-SLA

Information systems are often exposed to various types of threats, which can cause different types of damage and lead to significant financial losses. Security issues can range from small losses to a destruction of the whole information system. The effects of various threats vary considerably: some affect the confidentiality or integrity of data, while others affect the availability of a system. Organizations are now struggling to understand what the threats to their information assets are, and how to fight them, which remains a challenge. We can consider than one of the key requirements to consolidate cloud computing as a robust and reliable solution is security. Organizations that seek to embrace the cloud as a solution should be aware that this technology inherits all the security vulnerabilities from traditional solutions. These are aligned with the complexity and heterogeneity of configurations related to the architecture, privacy and compliance of cloud computing. Uniform management practices should be applied between providers in relation to the security control policies agreed with their clients - defined by the Security Service Level Agreement (Sec-SLA). Because of this, we present a standardization of responsibilities, taking into account the security perimeter of the cloud, and the main mitigation and resolution measures for the types of attacks enumerated by a vulnerability base, to be included in Sec-SLAs. This enables us to measure and classify the provider’s reliability regarding the services offered. Thus, the cloud is expected to improve its control and security and get efficient response to incidents. We propose a calculation model to determine the reliability of a provider based on the solution measures and mitigation of security incidents offered in its service catalog, as well as the development of an abstract method to support consumers in the definition of weights to be attributed to the architecture, privacy and compliance used in the calculation of trust. From the vulnerabilities found, impacts and mitigation measures in the subcategories of Architecture, Privacy and Compliance of the suggested providers, considering the weights given by the consumers, allowed us to validate the abstract model of the proposed confidence calculation. This allows us to classify the providers according to the confidence presented to the consumer in a context of the security vulnerabilities and policies adopted.

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.

Privacy Protection and Data Security in Cloud Computing: A Survey, Challenges, and Solutions

Privacy and security are the most important issues to the popularity of cloud computing service. In recent years, there are many research schemes of cloud computing privacy protection based on access control, attribute-based encryption (ABE), trust and reputation, but they are scattered and lack unified logic. In this paper, we systematically review and analyze relevant research achievements. First, we discuss the architecture, concepts and several shortcomings of cloud computing, and propose a framework of privacy protection; second, we discuss and analyze basic ABE, KP-ABE (key policy attribute-based encryption), CP-ABE (ciphertext policy attribute-based encryption), access structure, revocation mechanism, multi-authority, fine-grained, trace mechanism, proxy re-encryption (PRE), hierarchical encryption, searchable encryption (SE), trust, reputation, extension of tradition access control and hierarchical key; third, we propose the research challenge and future direction of the privacy protection in the cloud computing; finally, we point out corresponding privacy protection laws to make up for the technical deficiencies.

Priority-Aware VM Allocation and Network Bandwidth Provisioning in Software-Defined Networking (SDN)-Enabled Clouds

In the multi-tenant architecture of cloud computing, different applications have different requirements and priorities. In the case of network congestion within a data center, failure of transferring data on time may cause a significant performance degradation of an application. It may result in a severe failure for a critical application that needs a certain level of QoS satisfaction, therefore, efficient resource provisioning techniques are vital to ensure transferring the high-priority data over the other traffics even in network congestion. In Software-Defined Networking (SDN)-enabled clouds, this is possible by reconfiguring network flows dynamically adapting to network traffics. In this paper, we propose priority-aware resource placement algorithms considering both host and network resources. Our priority-aware VM allocation (PAVA) algorithm places VMs of the high-priority application to closely connected hosts to reduce the chance of network congestion caused by other tenants. The required bandwidth of a critical application is also guaranteed by bandwidth allocation with a configuration of priority queues on each networking device in a data center network managed by SDN controller. Our experiment results show that the combination of proposed approaches can allocate sufficient resources for high-priority applications to meet the application’s QoS requirement in a multi-tenant cloud data center.

Survey of Cloud Computing

With expanding volumes of knowledgeable production and the variability of themes and roots, shapes and languages, most detectable issues related to the delivery of storage space for the information and the variety of treatment strategies in addition to the problems related to the flow of information and methods go down and take an interest in the advantage of them face the researchers. In any case, such a great significance comes with a support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. The cloud is not a small, undeveloped branch of it, it is a type of computing that is based on the internet, an image from the internet. Cloud Computing is a developed technology, cloud computing, possibly offers an overall economic benefit, in that end users shares a large, centrally achieved pool of storing and computing resources, rather than owning and managing their own systems. But, it needs to be environment friendly also. This review paper gives a general overview of cloud computing, also it describes cloud computing, architecture of cloud computing, characteristics of cloud computing, and different services and deployment model of cloud computing. This paper is for anyone who will have recently detected regarding cloud computing and desires to grasp a lot of regarding cloud computing.

Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques

Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques

A review report on challenges and opportunities of edge, fog and cloud computing by employing IoT technology

In the future, various information and things will be connected to the network. People can now live more convenient and comfortable life where the things and information coordinated together. A world where things are connected to network is referred as IOT (Internet of Things). A huge amount of incomplete data is generated by IOT need to process and responded to very short time. This pose challenge of dealing with big data from many geometrically distributed data sources which are to be managed and processed. To achieve this objective, cloud computing is a treated as one of the popular choice due to its scalability, storage, computational and other capabilities. However current cloud models are not intended to handle the essentials of IOT– volume, variety, and velocity of data. Moreover, as the physical distance between cloud and user increases, transmission latency increases with it, increasing response time and stressing of the user. In addition to that, the processing speed in this environment is largely dependent on the performance of user device. The viable solution to these problems is identified as Edge Computing. The Edge Computing platform works by allowing some application processing to be performed by a small edge server position between the cloud and user, and crucially in a location physically closed to the user. This paper comprehensively presents various research trends that are available in Edge, Fog computing along with a comparison is made among Cloud. Particularly the architecture, characteristics, key technologies, potential applications, security issues and challenges of Edge, Fog and Cloud Computing are discussed and summarized.

Prediction-Based Mobile Data Offloading in Mobile Cloud Computing

Cellular network is facing a severe traffic overload problem caused by the phenomenal growth of mobile data. Offloading part of the mobile data traffic from the cellular network to alternative networks is a promising solution. In this paper, we study the mobile data offloading problem under the architecture of mobile cloud computing, where mobile data can be delivered by WiFi network and device-to-device communication. In order to minimize the overall cost for the data delivery task, it is crucial to reduce cellular network usage while satisfying delay requirements. In our proposed model, we formulate the data offloading task as a finite horizon Markov decision process. We first propose a hybrid offloading algorithm for mobile data with different delay requirements. Moreover, we establish sufficient conditions for the existence of threshold policy. Then, we propose a monotone offloading algorithm based on threshold policy in order to reduce the computational complexity. The simulation results show that the proposed offloading approach can achieve minimal communication cost compared with the other three offloading schemes.

A fully security included Cloud Computing Architecture

Many developers have designed their own architecture for installing the cloud computing infrastructure. The existing cloud computing architectures differs in many ways. Security has been the serious issue as client related data and processing is undertaken using the infrastructure provided by third party service providers varies greatly. It is necessary to know the extent of inclusion of security into the cloud computing system and then find the best architecture that includes best and tight security system. In this paper, a comparison of existing architectures from the perspective of inclusion of security infrastructure within cloud computing system is presented along with a comprehensive architecture that is included with every aspect of security taking into account the most of the vulnerabilities.

RETRACTED ARTICLE: Research on the system of smart city park based on cloud computing

In order to effectively improve the management level and service level of the intelligent park, the information fusion in the intelligent park is studied. First, the main concepts of the Internet of things and cloud computing are discussed. It includes key technology, architecture, application field and key technology of cloud computing, platform architecture and so on. Based on the above, a hardware architecture and technical scheme based on cloud computing, cloud storage and cloud analysis is proposed. The design of the application system, the application support platform, the transmission and the sensors used in the park are discussed. In addition, a software architecture and technical scheme for information intelligent application system based on cloud computing, cloud storage and cloud analysis is proposed. The research target and function design of each application system are studied in detail. Finally, the implementation effect of intercommunication and fusion processing between the systems is analyzed. The results show that the system can effectively solve the information island phenomenon. The introduction of new technology will bring great social and economic benefits for the park. It provides a reference for the development of intelligent cities.

Towards Secure Risk-Adaptable Access Control in Cloud Computing

The emergence of pervasive cloud computing has supported the transition of physical data and machine into virtualization environment. However, security threat and privacy have been identified as a challenge to support the widespread adoption of cloud among user. Moreover, user awareness on the importance of cloud computing has increase the needs to safeguard the cloud by implementing access control that works on dynamic environment. Therefore, the emergence of Risk-Adaptable Access Control (RAdAC) as a flexible medium in handling exceptional access request is a great countermeasure to deal with security and privacy challenges. However, the rising problem in safeguarding users' privacy in RAdAC model has not been discussed in depth by other researcher. This paper explores the architecture of cloud computing and defines the existing solutions influencing the adoption of cloud among user. At the same time, the obscurity factor in protecting privacy of user is found within RAdAC framework. Similarly, the two-tier authentication scheme in RAdAC has been proposed in responding to security and privacy challenges as shown through informal security analysis.

Reliability Analysis for Multipath Communications in Mobile Cloud Computing Architectures

Mobile cloud computing (MCC) has gained much attention from both academia and industry in recent years. It can support new types of services, such as m‐commerce, m‐learning, and mobile healthcare, and enrich mobile users’ experience and satisfaction by taking full advantage of cloud computing. In MCC architectures multipath communications can be achieved with multihomed mobile devices, so as to utilize multiple paths for data transmission in parallel. They can achieve better utilization of bandwidth resource, split traffic for load balancing, and enhance reliability, fault tolerance, and robustness for applications. However, little attention has been paid to model the reliability of multipath communications in case of path failure. In this paper we investigate the reliability of concurrent multipath communications in MCC architectures and propose two reliability models when paths are failure. One is for static path failure where the failed paths cannot recover for communication in some delay time. The other is for dynamic path failure where the failed paths can recover in some delay time. Finally, numerical results are given to illustrate the reliability of multipath communications.

Random ensemble learning for EEG classification.

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility.

Smart vehicle monitoring and assistance using cloud computing in vehicular Ad Hoc networks

Abstract The increasing number of on road vehicles has become a major cause for congestion, accidents and pollution. Intelligent Transportation Systems (ITS) might be the key to achieve solutions that help in reducing these problems significantly. The connected vehicular networks stream is a rapidly growing field for research and development of various real-time applications. In this paper, novel techniques have been proposed to serve the speed based lane changing, collision avoidance and time of arrival (TOA) based localization in Vehicular Ad Hoc Networks (VANETs). As GPS requires clear line-of-sight for accurate services of positioning and localization applications, we designed a Time of Arrival (ToA) based algorithm for areas where strong GPS signals are unavailable. Collision avoidance using automatic braking and camera-based surveillance are a few other applications that we addressed. The feasibility and the viability of the algorithms were demonstrated through simulations in Simulation of Urban Mobility (SUMO) and Network Simulator-2 (NS-2). We prototyped a working hardware and tested it on actual vehicles to assess the effectiveness of the proposed system. We designed a mobile app interface for the on-board unit for smart, efficient and remote traffic monitoring. The integrated VANET Cloud Computing architecture acts as the platform for the proposed applications.

Web GIS and its architecture: a review

Phenomenal progress has been witnessed in the field of Geographic Information System (GIS) in recent times. The development of webGIS is the result of the growth of the Internet and consequently World Wide Web. The webGIS architecture is continuously changing according to contemporary technologies and requirements. In this paper, webGIS and its architectures are reviewed. Firstly, GIS and invention of the Internet are discussed. Evolution of webGIS is then covered along with major milestones of its development and open source initiatives. It is followed by discussion on client server architecture and its types. Thick and thin client architectures are then described and compared. As the paradigm in the computing world shifted towards web services, service-oriented architecture (SOA) is also discussed in context of webGIS. Spatial cloud computing and cloud-based architecture for webGIS are then described. The paper also provides a comparison of different webGIS architectures so that suitable architecture can be selected by the user based upon the requirements.