Google App Engine Research Articles

Literature Review on Cloud Computing: A Paradigm Combining Service-Oriented Architecture with Internet-Based Solutions

Cloud computing, a paradigm combining service-oriented architecture with Internet-based network solutions, has rapidly transformed the IT industry by offering on-demand access to shared resources. Key models of cloud services include Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). These models provide cost-effective, scalable, and flexible alternatives to traditional IT infrastructure, enabling enterprises to outsource hardware, software, and network services. Cloud systems such as Amazon EC2, Google App Engine, and IBM’s Blue Cloud illustrate these benefits, significantly lowering costs through pay-as-you-go pricing structures. However, data security and privacy have become critical concerns, as cloud users must entrust sensitive information to third-party providers. Ensuring data confidentiality and implementing fine-grained access control are essential to maintaining user trust and enabling compliance with legal and organizational regulations. The success of cloud computing in the future depends on overcoming these security challenges, allowing enterprises to fully embrace its potential

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

Web-Based Application for Malaria Parasite Detection Using Thin-Blood Smear Images

Malaria is an infectious disease caused by the Plasmodium parasite. In 2019, there were 229 million cases of malaria with a death toll of 400.900. Malaria cases increased in 2020 to 241 million people with the death toll reaching 627,000. Malaria diagnosis which is carried out by observing the patient’s blood sample requires experts and if it is not done correctly, misdiagnosis can occur. Deep Learning can be used to help diagnose Malaria by classifying thin blood smear images. In this study, transfer learning techniques were used on the Convolutional Neural Network to speed up the model training process and get high accuracy. The architecture used for Transfer Learning is EfficientNetB0. The training model is embedded in a pythonbased web application which is then deployed on the Google App Engine platform. This is done so that it can be used by experts to help diagnose. The training model has a training accuracy of 0.9664, a training loss of 0.0937, a validation accuracy of 0.9734, and a validation loss of 0.0816. Prediction results on test data have an accuracy of 96.8% and an F1- score value of 0.968.

RecutClub 2.0: Deployment of a Trainee-Led, Didactic-Centered Whole Slide Imaging Platform

Abstract Objectives The Houston Methodist Pathology training program encourages trainee participation in quality improvement (QI) projects for improving the didactics experience. One of these initiatives is RecutClub, a whole slide imaging (WSI) web-based platform for presenting surgical pathology unknown conference cases. With the evolving nature of pathology didactics in an ever-changing world where remote work plays an increasingly important role, several refinements were needed to ensure that the platform could continue to provide an unparalleled WSI experience for incoming trainees. Additionally, the burden of updating site content needed to be distributed from the site administrators to the end-users. Methods RecutClub is operated through Google Cloud via a Google App Engine instance. It connects to Google Cloud Databases to store persistent tabular data and Google Cloud Storage for pyramidal JPEG files used to display whole slide images in a web-browser. In the next generation platform (RecutClub 2.0), the largest change was to migrate case uploading from a developer-controlled solution to a decentralized, trainee-driven web portal. To ensure a superior user experience, the entire site as well as the upload form was migrated to Bootstrap 4. An AJAX-native, asynchronous uploader was built to interface the existing Google Cloud platform to a new upload portal that allows authorized trainees to build cases from scratch. Additionally, a tagging solution was implemented to allow for server-side persistent storage of trainee-selected cases, such as “interesting” cases or cases for further review. This tagging complements the existing region of interest (ROI) and case search functionalities. Results The deployment of RecutClub 2.0 involved multiple rounds of resident-led testing and feedback. Several regular testers offered ongoing user feedback and suggestions for visual layout and usability. The response from the general trainee population after deployment was overwhelming positive, with many trainees commenting on the “improved” visual aesthetics as well as the convenience of the upload functionality. Administrator involvement in case-upload time decreased from 2 hours/month to 0 hours/month due to enabling trainee-driven uploading. Conclusions The deployment of RecutClub 2.0 fulfilled the dual primary objectives of enabling straightforward trainee-led content updates, as well as decreasing administrator involvement in case-upload time. These and other changes ensure that RecutClub can continue to provide an unparalleled WSI experience for pathology trainees at Houston Methodist, while also providing fresh content in a landscape with several competing WSI platforms.

LEARNING HOAX NEWS PADA LOCAL DAN CLOUD COMPUTING DEPLOYMENT MENGGUNAKAN GOOGLE APP ENGINE

Abstract. Hoax news is fake news that contains information that intentionally misleads people and has a specific political agenda. Along with the development of technological developments, the news is increasingly unclear whether the truth is only in accordance with facts or mere hoaxes. Based on this problem, hoax news is carried out, one of which uses a machine that can process news classification automatically. Machine learning is implemented using a framework called the Flask framework that can run on both on-premises servers and cloud computing. Local servers with computational limitations that are static in nature have problems running large computations on the hoax news classification system framework which is characterized by a predictable time schedule so that a distributed automatic-scaling computing system can handle cloud computing loads. Cloud computing offers compute load-sharing automatic scalability that can provide stable compute computing. So this research focuses on the application of the machine learning hoax news classification model into the framework of the machine learning deployment Platform as a Service (PaaS) model in Cloud computing called Google App Engine (GAE). The application of the hoax news classification system in the Google App Engine environment runs with an average prediction time of 11.53 seconds, better and more stable than the local server's average prediction of 17.50 seconds.

STAGER: Semantic-Based Framework for Generating Adapters of Service-Based Generic-API for Portable Cloud Applications

In PaaS model, providers have different proprietary APIs, which make developers locked inside a specific platform and not able to easily port their applications among different platforms. So, vendor lock-in problem appeared. One solution to this problem is to use generic APIs with specific adapters. However, any update in a PaaS specific-API makes its corresponding adapter is unusable which causes, what we call, API synchronization problem. Therefore, STAGER (SemanTic-based GenERation of Generic-API Adapters) framework is proposed. STAGER framework provides a semi-automatic adapter generation process, which generates specific adapters of generic APIs for PaaS services (e.g., blob storage and datastore services) for target PaaS platforms. The adapter generation process is based on semantic annotations of the generic APIs and their corresponding PaaS specific-APIs. In order to evaluate STAGER framework, two generic APIs for blob storage and NoSQL datastore services have been proposed. STAGER framework is used to generate the adapters of these generic APIs for two PaaS platforms: Google App Engine (GAE) and Windows Azure. Although there is some overhead for semantically annotating the PaaS APIs, the evaluation results prove the feasibility of STAGER framework and promote the usage of the generated adapters for implementing portable cloud applications.

Cloud Systems and Applications : A Review

Cloud computing has become a buzz word over the last decade. It has continued to attract businesses due to its on-demand service, applications and cost-effectiveness. There exist different Cloud Computing service providers today with inherent ambiguity of services and billing structure. To overhaul these ambiguities, in this paper, we discussed an overview of cloud computing, evaluated and diagrammatically stratified the Cloud Computing stack with their examples and their common elements. Further, the biggest Cloud Computing service providers are Amazon Web Services (AWS), Google App Engine and Microsoft Azure. We selected the service providers to analyse critically, compare and contrast their services, billing, performance and functionalities.

Design and implementation of trusted sensing framework for IoT environment

Even though Internet of things (IoT) sensing services are introduced in a wide range of areas, it is not applicable to mission-critical services due to the lack of the trustworthiness of the IoT sensing data. To address this problem, we propose TruSense, a novel trusted sensing framework for the IoT environment that covers end-to-end implementation from an IoT device to a cloud service. The TruSense framework includes a small sensing board, a communication protocol, and a cloud service for the trusted sensing in the IoT environment. To show our framework's feasibility, we design an ARM TrustZone-based IoT sensing board and implement an application and a trusted sensing service running in the Secure world. We also implement a cloud service for the trusted sensing in Google app engine, which is one of the widely used cloud services.

The Comparison of Cloud Migration Effort on Platform as a Service

Platform as a Service offers users to easily and quickly deploy applications in the cloud environment without thinking about the availability of supporting resources for developing the application. However, migrating on-premise applications to cloud vendors still has risks ranging from the compatibility of application technology with the cloud platform to the efforts needed. One obstacle in the adoption of cloud technology is the lack of visibility in planning migration efforts. So this study aims to measure and compare the feasibility of PaaS cloud vendors based on the calculation of the effort required during the migration process through an empirical approach. Specifically, an experiment was carried out by migrating on-premise application to Amazon Elastic Beanstalk, Azure App Service, and Google App Engine. Empirical results show that Azure App Service and Amazon Elastic Beanstalk have similar complexity of effort, while Google App Engine has the highest complexity of effort.

A Retrospect of Prominent Cloud Security Algorithms

The concept of Cloud Computing has been distinguished as one of the major computing models in recent years. This distinguish comes as a reflection of a rapid and intense interest from academic and industrial organisations which present it as an active area of research. Security has been always raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Furthermore, issues related to service availability and interoperability has gained a substantial interest. Failing to address such issues would jeopardise consumers’ satisfactions. This article mainly discusses the causes of obstacles and challenges related to security, reliability, privacy and service availability. In this work, we analyse and compare the performance of selected algorithms over “Google App Engine” which is a web framework and could computing platform. A comparative analysis of the efficiency of Advanced Encryption Standard “AES” (Rigndael), Rivest-Shamir-Adleman “RSA” and Triple Data Encryption “DES” have been performed locally and over the cloud as well. The efficiency analyses covered encryption and decryption procedures separately. Experiments results are given to analyses the effectiveness of each algorithm. The results show that Triple DES manifested a higher time efficiency ratio over cloud when subjected to different data loads compares to AES and RSA.

Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider

Picking up public cloud service providers is now becoming a harder task in an enterprise organization. This paper will help in reducing more hesitation to choose a public cloud service provider. This paper is highlighting computation, storage, and infrastructure is important to service features that have an impact when choosing cloud service providers. Compare these three (AWS, Microsoft Azure, GCP) CSPs concerning service, price, advantages, and highlight significant service features. Studies discuss the primary reason to choose a CSP that normally enhance features, familiarity with the brand, suitable for organization and security parameters considered when choosing CSP. Amazon Web Services proved its leadership by maintaining about 33% share in the market throughout for several quarters irrespective of the market size increased by a factor of 3. Microsoft has shown prominent performance in SaaS. Since 2008, after introducing PaaS in the form of Google App Engine, Google is continuously enhancing its cloud computing services of Google Cloud Platform.

Analysis of platform decisions for implementation a internet of things networks

Currently, cloud technology is widespread, despite some criticism related to security, information retention and security of access. At the same time, all key divisions of cloud technologies and services are equally in demand, they are implemented by - IaaS, PaaS, SaaS, DaaS. The market for cloud solutions and services is quite diverse, providing both individual components and services and a wide range of complex solutions (multi-model implementations), such as Microsoft Azure, Google App Engine, Heroku, Amazon Web Services (AWS), and more.

Mobile Learning with Google App Engine

Mobile Learning with Google App Engine

An Optimized Model for Energy Efficiency on Cloud System using PSO & CUCKOO Search Algorithm

Cloud computing is one of the growing technologies, these days. Cloud computing is a paradigm that is surrounded by multiple resources, which helps in resource utilization. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (Saas) are named as services of cloud computing. In the IaaS models, users can rent infrastructure of the data center as a service. Some of the examples of IAAS are Google Compute Engine (GCE) and Amazon Web Service (AWS). In the PaaS models, users can take services like operating system and database. Some of the examples of PAAS are Microsoft Azure and Google App Engine. In the SaaS models, users can access and install application software and databases via Internet. Examples of SAAS are Citrix GoToMeeting and Google Docs. In this paper algorithms named as PSO and CSA are discussed The objective of optimization for energy consumption on cloud has also been discussed in the paper. Along with the optimization techniques, the detailed literature reviews have been presented. To achieve the proposed work, CloudSim simulators and standard programming languages have been used. The performance of the proposed work will be analyzed by using the various performance parameters such as response time, energy efficiency and execution time.

FEATURES OF USING CLOUD TECHNOLOGIES IN EDUCATION

The article investigates cloud technologies and features of their use in educational institutions. The essence of cloud technologies in education is determined. It is noted that the modern institution of higher education provides usage of information and communication technologies, and in particular, cloud technologies. The main characteristics (features) which characterize cloud services are described here. It is noted that usage of cloud services has benefits and problems as well. There are such mandatory characteristics of cloud computing as self-service on demand, universal access to the network, resource pooling, elasticity, consumption accounting. Four models of deployment of cloud technologies are revealed in the article. They are corporate, public, group and hybrid. It is admitted that the most appropriate model of deployment of cloud technologies in the infrastructure of higher education institutions is hybrid. Cloud technologies support such activities as: communication, collaboration and co-operation, which determines certain areas of their usage. The architecture of cloud technologies is presented in three levels: software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS). The most popular cloud services which are used in various spheres, including educational institutions, are analyzed here. Office 365, Microsoft Planner, Microsoft SkyDrive, Google Drive, Google Talk, Google Docs, these are the common cloud services which are selected in SaaS. Popular software platforms which are presented in the PaaS segment are Windows Azure, Google App Engine, Cloud9 IDE, Ideone IDE. Each of the cloud platforms that is described in the article has its own peculiarities. Possible areas of application of cloud technologies in the educational process are identified. They are free access to the programs and services used in the educational process, planning activities, implementation of the working regime of teachers and employees, the organization and conduct of scientific online conferences, seminars, forums, round tables, trainings, joint project work of students, individual independent work of students, electronic interaction with entrants.

Cloud Mobile Storage for Mobile Applications

The data storage in mobile cloud computing domain is an important subject because of the large amount of data devices can provide. Different kinds of sensors retrieve information and send them to data storages. But the storage location can move even dynamically during their uses. So, these changes must be managed at runtime. In the other part, Android devices are good computers to acquire information and send them to a storage area. These devices are well-known, ergonomic and useful machines sorely used. In this paper, we present a software architecture from Android devices to Cloud storage areas to put down data when the data stores are dynamically moving. The different parts of these distributed applications are built around Android with JSON messages for REST services and moving NoSQL databases in the Google app engine. The exchanges between the cloud and the Android devices use the Google Cloud Messaging (GCM) protocol. Furthermore, even without network access, the saving is made locally in the smartphone. The synchronization with distant databases is automatically made when the network becomes again available.

A COMPARATIVE STUDY ON GOOGLE APP ENGINE AMAZON WEB SERVICES AND MICROSOFT WINDOWS AZURE

In today Internet has grown to be persistent in daily livelihood furthermore Cloud computing is a rising model where computing resources offered over the Internet as scalable, on-demand (Web) services. An association deploy internet service needs to use enormous amounts of money on infrastructure needs to serve feasible users which is not a problem for large venture but when it comes to Small and Medium Enterprises or Enterprises affordability becomes a huge factor with the huge infrastructure come problems like machines failure, hard drive noises, software bugs, etc. Here might be a big problem for such a community. Cloud Computing is the ultimate solution to this problem. Rather than buying, installing and operating its own systems, an organization can rely on a cloud provider to do this for them. Cloud Computing key market leaders like Google, Amazon and Microsoft etc, these providers introduce new operating and business models that allow customers to pay for the resources they completely use, instead of making tremendous upfront investments. The purpose of this paper is to analyze most popular platforms, The Google App Engine, Amazon Web Services, and Windows Azure Platform.

Do Google App Engine’s Runtimes Perform Homogeneously? An Empirical Investigation for Bonus Computing

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.

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

Reservoir construction and land use change are altering sediment transport within river systems at a global scale. Changes in sediment transport can impact river morphology, aquatic ecosystems, and ultimately the growth and retreat of delta environments. The Lower Mekong Basin is crucial to five neighboring countries for transportation, energy production, sustainable water supply, and food production. In response, countries have coordinated to develop programs for regional scale water quality monitoring that including surface sediment concentrations (SSSC); however, these programs are based on a limited number of point measurements and due to resource limitations, cannot provide comprehensive insights into sediment transport across all strategic locations within the Lower Mekong Basin. To augment in situ SSSC data from the current monitoring program, we developed an empirical model to estimate SSSC across the Lower Mekong Basin from Landsat observations. Model validation revealed that remotely sensed SSSC estimates captured the spatial and temporal dynamics in a range of aquatic environments (main stem of Mekong river, tributary systems, Mekong Floodplain, and reservoirs) while, on average, slightly underestimating SSSC by about 2 mg·L−1 across all settings. The operational SSSC model was developed and implemented using Google Earth Engine and Google App Engine was used to host an online application that allows users, without any knowledge of remote sensing, to access SSSC data across the region. Expanded access to SSSC data should be particularly helpful for resource managers and other stakeholders seeking to understand the dynamics between surface sediment concentrations and land use conversions, water policy, and energy production in a globally strategic region.

An Innovative SLA-Based Service Monitoring Framework in Cloud

Cloud computing encompasses powerful technology to perform complex computing for large applications. It provides access to various resources from any location with reduced Information Communication Technology overhead, enabling availability and scalability of resources based on consumer needs. Therefore, it has brought a paradigm shift in the way computing services are delivered. However, due to its increasing usage and rising expectations from cloud service provisioning, providing optimal and effective service is becoming an arduous task for service providers. Thus, maintaining quality of service (QoS) through regular monitoring is the utmost priority for the service provider. The key ingredient of managing QoS is a service level agreement (SLA). SLAs form a basis of measuring service quality. The cloud can be leveraged for provisioning e-Learning services to serve millions of users. The e-Learning service providers can enter into a contract with available cloud providers, which is transparent to the learners. Hence, it is critical to focus on the SLAs that need to be formulated and managed by the service providers for consumers of their e-Learning services. The objective of this paper is to propose SLA-based e-Learning service framework. This paper also identifies SLA parameters and related metrics to measure SLAs relevant to the provisioning of the proposed e-Learning service. It also proposes an innovative e-Learning service monitoring framework for managing QoS. This paper further evaluates the effectiveness of the proposed framework by provisioning of e-Learning service on Google App Engine cloud platform. It also presents experimental analysis using response graph in order to monitor associated SLAs in the proposed framework.