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Leaf and fruit infections are the primary cause of the maximum harm to the crop, which decreases the quality and amount of the goods. To improve the productivity of plants, the timely identification of the infection is vital, which is a highly challenging task. Deep learning (DL) with image processing allows farmers to distinguish between healthy and infected crops. This work intends to identify healthy and diseased citrus leaf images using a convolutional neural network (CNN) on the Platform as a Service (PaaS) cloud. The dataset of five types of healthy and unhealthy citrus images was used, namely, black spot, melanose, canker, greening, and healthy. Furthermore, the four-transfer learning (TL) pre-trained deep CNN (DCNN) models, namely, ResNet152V2, InceptionResNetV2, DenseNet121, and DenseNet201, were used to classify the leaf type. The performance of the CNN and four DCNNs were assessed using the confusion matrix (accuracy, precision, recall, and F1-score) and receiver operating characteristic-area under the curve (ROC-AUC) curve. An augmentation technique was utilised to enhance the dataset images, which helped to improve the model's performance and achieved an accuracy of 98% precision and recall and an F1 score of 99% and an ROC-AUC score of 0.99. Moreover, the suggested CNN has only 15 layers, 427317 parameters, and 1.68MB size, while DCNN models have more layers, parameters, and large size. The small-size CNN was deployed to the Platform as a Service (PaaS) cloud. The deployed model link is available on a smartphone to upload a citrus leaf image to the cloud, and the result is instantly available on a mobile screen. [ABSTRACT FROM AUTHOR]

Platform‐as‐a‐Service (PaaS) is one of the cloud computing services which will be offered to the clients through the virtualization platform. The problem is constructed in the PaaS platform effectively excluding internet service suppliers which do not meet highly secure standards, and so security issues can be a significant a hurdle to online computation. To address the aforementioned issues, the suggested method primarily concentrated proceeding the safety of PaaS infrastructure by using a novel fuzzy based access control technique, in which the information source is realized by using CDO security feature. To further enhance the security, security using trust based signature has been used in which the class and packet filter for each specified authorization were mapped to our class by modifying the security feature code. This public key will be utilized for authenticate that transaction relevant signature if the identity provider (IdP) has provided public safety data about this same two major components represents this little token on whose trusted trademark components are built put, that is, the entire item or even the claims contained. The suggested technique outperforms other current methods in terms of several performance parameters, with a high throughput of 14, CPU utilization of 12.5, and a low execution time of 5.2 s. The proposed 'PaaS platform security enhancement with the use of fuzzy and trust‐based signatures' provides high throughput and low execution time. This research work proposes a novel approach to enhancing the security of PaaS infrastructure, which is offered to clients through the virtualization platform in cloud computing. The current problem with PaaS is that it excludes internet service providers that do not meet high‐security standards, which poses a significant challenge to online computation. The suggested approach focuses on improving the safety of the PaaS platform by employing a fuzzy‐based access control technique, using the connectionless data objects security feature as the information source. To further strengthen security, the approach utilizes a trust‐based signature technique where the class and packet filter for each authorization are mapped to the class of the proposed method by modifying the security feature code. The proposed public key is then used to authenticate the transaction relevant signature, provided the IdP has given public safety data about these two components of the token on which Trusted Trademark components are built, that is, the entire item or even the claims contained within it. The proposed approach outperforms current methods in terms of several performance parameters, such as a high throughput of 14, a high CPU utilization of 12.5, and a low execution time of 5.2 s. In summary, the proposed PaaS platform security enhancement using fuzzy and trust‐based signatures provides an effective way to achieve high throughput and low execution time while ensuring platform security. [ABSTRACT FROM AUTHOR]

IoT presently being one of the most pertinent application domains for cloud computing. However, even though most of the traditional PaaS solutions such as Google App Engine, Microsoft Azure, Cloud Foundry enable provisioning and scheduling of IoT applications, but all of them do not support the provisioning of concurrent applications efficiently. The main reason not to support the execution of the concurrent applications is the absence of direct pipe-lining between two applications processes. There is a need to design a resource scheduling algorithm to achieve the target of the completion of the application's execution with the external domain process. In this paper, we presented an IoT-based PaaS architecture and cuckoo search based resource scheduling algorithm for concurrent applications. The proposed approach caters the communication interface among processes by uniquely allocating network interface to a particular container. An implementation of the proposed approach is also demonstrated. The performance of the proposed algorithm is evaluated in the simulation environment. Cuckoo search based algorithm outperformed in comparison to the existed algorithms, as shown in the performance evaluation section. [ABSTRACT FROM AUTHOR]

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service "Microservices" to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service "Microservices" and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian Petroleum Sector. After developing and strengthening the ALS using the cloud computing with the benefits of using Function as a Services "FaaS", the functions are start rapidly in order to allow handling of individual requests by using the Microservice Architecture. Thisstudy includes a description of the statistic field study approach (The study's community and its sample. As well as used tools, methodologies, and their validity and reliability. It also includes used procedures for tools codification and their application. Finally, statistical processes that were relied upon in study analysis). [ABSTRACT FROM AUTHOR]

Cloud computing provides the availability of computer resources. It is used for data storage and computing power. Large clouds have spread over many location and each location is considered as a data center. The main advantage of cloud computing is to reduce the costs that enlarge the business. Amazon Web Services provide IT infrastructure services. Databases and data management is one of the amazon services. It gives the relational database management system for the users of aws. Amazon S3 is simple storage service. In this research work, biological data is focused. Bioinformatics is one of the application of tools which is used for the computational and analysis to capture the data. It combines many fields such as computer science, mathematics, physics etc., Different user, policy creation, data manipulation for ligand data, bucket creation etc., features are developed for biological data. More focus has been given for the ligand data. [ABSTRACT FROM AUTHOR]

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer's overhead of implementing each service for different clouds to make it portable. [ABSTRACT FROM AUTHOR]

Bonus computing is a new metacomputing form that takes advantage of free computing power from the public Cloud market. To maximize the value of free Cloud resources and facilitate dividing Bonus computing tasks, it would particularly be crucial to understand the performance of candidate Cloud services before using them in production. By offering free quotas in its standard environment, Google App Engine (GAE) has become a popular public Platform-as-a-Service (PaaS) for Bonus computing. Since GAE natively supports various programming languages with flexible configurations (e.g., region selection), it will be possible and valuable to squeeze GAE’s free computing power if there is an optimal choice of its different runtimes. Following the performance evaluation methodology DoKnowMe, we implemented several versions of the Fibonacci(-like) calculation as benchmarks to fundamentally investigate GAE’s standard environment. Our investigation results reveal that GAE does not support its runtime environments homogeneously in terms of their computation speed and memory efficiency. The heterogeneity could be related not only to the characteristics of different programming languages but also to the diverse GAE infrastructures. For example, Go runtime seems to be a well-trade-off to satisfy Bonus computing among all the options, while the GAE service located in southamerica-east1 and us-central1 performs dramatically worse than that in the other regions.

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable.

Summary: In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature. [ABSTRACT FROM AUTHOR]

This article presents the Templet Web cloud service. The service is designed for high-performance scientific computing automation. The use of high-performance technology is specifically required by new fields of computational science such as data mining, artificial intelligence, machine learning, and others. Cloud technologies provide a significant cost reduction for high-performance scientific applications. The main objectives to achieve this cost reduction in the Templet Web service design are: (a) the implementation of “on-demand” access; (b) source code deployment management; (c) high-performance computing programs development automation. The distinctive feature of the service is the approach mainly used in the field of volunteer computing, when a person who has access to a computer system delegates his access rights to the requesting user. We developed an access procedure, algorithms, and software for utilization of free computational resources of the academic cluster system in line with the methods of volunteer computing. The Templet Web service has been in operation for five years. It has been successfully used for conducting laboratory workshops and solving research problems, some of which are considered in this article. The article also provides an overview of research directions related to service development.

Vendor lock-in can occur at any layer of the cloud stack-Infrastructure, Platform, and Software-as-a-service. This paper covers the vendor lock-in issue at Platform as a Service (PaaS) level where applications can be created, deployed, and managed without worrying about the underlying infrastructure. These applications and their persisted data on one PaaS provider are not easy to port to another provider. To overcome this issue, we propose a middleware to abstract and make the database services as cloud-agnostic. The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers. It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases. NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance, availability, and scalability. The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system. A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database (move existing data from a database on one cloud to a new database even on a different cloud). Although the middleware adds some overhead compared to the native code for the cloud services being used, the experimental evaluation on Twitter (a Big Data application) data set, proves this overhead is negligible. [ABSTRACT FROM AUTHOR]

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This article is devoted to the review of main features of cloud computing that can be used in education. Particular attention is paid to those learning and supportive tasks, that can be greatly improved in the case of the using of cloud services. Several ways to implement this approach are proposed, based on widely accepted models of providing cloud services. Nevertheless, the authors have not ignored currently existing problems of cloud technologies , identifying the most dangerous risks and their impact on the core business processes of the university.

The combination of cloud computing and industrial applications is the key point of “internet +”，the mode of platform as a service can effectively reduce the costs of development and construction for industrial application，so that an effective innovation mechanism could be set up.The status and problems of industrial application incubation platform were introduced.By designing the service function and business model of the platform，the architecture based on PaaS technology was put forward.The key technologies involved such as container and mobile App development were analyzed.At last the development of the platform was prospected.

Cloud computing is nowadays becoming a popular paradigm for the pro- vision of computing infrastructure that enables organizations to achieve financial savings. On the other hand, there are some known obstacles, among which ven- dor lock-in stands out. Furthermore, due to missing standards and heterogeneities of cloud storage systems, the migration of data to alternative cloud providers is expensive and time-consuming. We propose an approach based on Semantic Web services and AI planning to tackle cloud vendor data lock-in problem. To complete the mentioned task, data structures and data type mapping rules between different types of cloud storage systems are defined. The migration of data among different providers of platform as a service is presented in order to prove the practical ap- plicability of the proposed approach. Additionally, this concept was also applied to software as a service model of cloud computing to perform one-shot data migration from Zoho CRM to Salesforce CRM. [ABSTRACT FROM AUTHOR]

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications. [ABSTRACT FROM AUTHOR]

The paper presents an approach to the design and implementation of web-based environments for practical exercises in parallel and distributed computing (PDC). The presented approach introduces minimal development and operational costs by relying on Everest, a general-purpose platform for building computational web services. The flexibility of proposed service-oriented architecture enables the development of different types of services targeting various use cases and PDC topics. The generic execution services support the execution of different types of parallel and distributed programs on corresponding computing systems, while the assignment evaluation services implement the execution and evaluation of solutions to programming assignments. As was demonstrated by teaching two introductory PDC courses, the presented approach helps to enhance students’ practical experience while avoiding low-level interfaces, reducing the grading time and providing a level of automation necessary for scaling the course to a large number of students. In contrast to other efforts, the exploited Platform as a Service model provides the ability to quickly reuse this approach by other PDC educators without installation of the Everest platform. [ABSTRACT FROM AUTHOR]

The cloud computing in the world of IT industry has a huge value. It made things reliable and comfortable. As the number of consumers or customers are increasing on cloud computing they are dispatchers of their workload on it. Some different qualities make it unique from others like providing high resources in performance, providing huge space for data storage, etc. In this article, the authors briefly discuss about privacy and security in cloud computing, examining different techniques and methods for securing information and also their drawbacks. The purpose of this article is to understand the security issues and to defeat these events in parliamentary procedure to secure users' assets. In the result of this study, they were able to understand the issues and provide some new methods or algorithm to resolve these issues. [ABSTRACT FROM AUTHOR]