PaaS Cloud Research Articles

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile

CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile

IMPLEMENTASI PLATFORM AS A SERVICE (PAAS) PADA DATABASE E-COMMERCE BERBASIS CLOUD COMPUTING

Business competition in the field of information technology continues to increase, one of the technologies itself is cloud computing. Cloud computing can be public or private. One type of cloud computing service used in this study is PaaS. The PaaS cloud computing service provides a platform that users can use to create applications. The Byna Cake shop is a shop that opens a cake business in Aceh, which is located on Pulau Tiga, Aceh, Tamiang. This shop sells various types of cakes which have been produced manually by themselves since 2021. The sales system uses WhatsApp and Instagram media and comes directly to the store, so this system is considered to be less effective and efficient. Therefore, an online sales system is needed, namely by building a web-based E-Commerce application. In implementing this web-based e-commerce application, the author utilizes cloud infrastructure as a cloud provider, namely Microsoft Azure. Computing service used in this research is CMS AbanteCart. Web-based e-commerce application at the Byna Cake store created by configuring a virtual machine to install CMS AbanteCart Packaged By Bitnami on the Microsoft Azure platform. The design used in creating web-based e-commerce applications at the Byna Cake store uses Use Cases and Class Diagrams.

A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER

Within the context of the Competence Centre for the Conservation of Cultural Heritage (4CH) project, the design and deployment of a platform-as-a-service cloud infrastructure for the first European competence centre of cultural heritage (CH) has begun, and some web services have been integrated into the platform. The first integrated service is the INFN-CHNet web application for FAIR storage of scientific analysis on CH: THESPIAN-Mask. It is based on CIDOC-CRM-compatible ontology and CRMhs, describing the scientific metadata. To ease the process of metadata generation and data injection, another web service has been developed: THESPIAN-NER. It is a tool based on a deep neural network for named entity recognition (NER), enabling users to upload their Italian-written report files and obtain labelled entities. Those entities are used as keywords either to serve as (semi)automatically custom queries for the database, or to fill (part of) the metadata form as a descriptor for the file to be uploaded. The services have been made freely available in the 4CH PaaS cloud platform.

Potential Security Issues in Implementing IaaS and PaaS Cloud Service Models

As the digital world evolves, so does potential problem that computer users encounter. Cybersecurity threats are still evolving and expanding. Unfortunately, most computer users do not understand this properly. The cloud models offered by various public cloud providers remain concentrated on infrastructure resources, application platforms, and software services despite the recent increase in the popularity of cloud computing. The first step in this study will be a literature review to get an understanding of accessible cloud service models. The papers chosen for the study spans 2010 to 2020. All data was gathered from pertinent and related literature on cyber security and cloud computing. The following tenets serve as the foundation for this architecture. First, in the described architecture, the perimeter scanner serves as the first entry point for external cyberattacks. Firewall and other security layers become next barriers if the attack can get past first layer. On the other side, the machine learning system will detect every successful assault that gets past the security layers. As a result, there are numerous viewpoints and categorization systems for diverse attacks. It is possible to advance cyber security research in the context of cloud technology by merging the results of existing studies and developing international guiding standards

Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud

Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud

Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud

Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud

2PC*: a distributed transaction concurrency control protocol of multi-microservice based on cloud computing platform

The two-phase commit (2PC) protocol is a key technique for achieving distributed transactions in storage systems such as relational databases and distributed databases. 2PC is a strongly consistent and centralized atomic commit protocol that ensures the serialization of the transaction execution order. However, it does not scale well to large and high-throughput systems, especially for applications with many transactional conflicts, such as microservices and cloud computing. Therefore, 2PC has a performance bottleneck for distributed transaction control across multiple microservices. In this paper, we propose 2PC*, a novel concurrency control protocol for distributed transactions that outperforms 2PC, allowing greater concurrency across multiple microservices. 2PC* can greatly reduce overhead because locks are held throughout the transaction process. Moreover, we improve the fault-tolerance mechanism of 2PC* using transaction compensation. We also implement a middleware solution for transactions in microservice support using 2PC*. We compare 2PC* to 2PC by applying both to Ctrip MSECP, and 2PC* outperforms 2PC in workloads with varying degrees of contention. When the contention becomes high, the experimental results show that 2PC* achieves at most a 3.3x improvement in throughput and a 67% reduction in latency, which proves that our scheme can easily support distributed transactions with multi-microservice modules. Finally, we embed our middleware scheme in the PaaS cloud platform and demonstrate its strong applicability to cloud computing through long-term analysis of the monitoring results in the cloud platform.

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.

Detecting Performance Anomalies in Cloud Platform Applications.

We present Roots, a full-stack monitoring and analysis system for performance anomaly detection and bottleneck identification in cloud platform-as-a-service (PaaS) systems. Roots facilitates application performance monitoring as a core capability of PaaS clouds, and relieves the developers from having to instrument application code. Roots tracks HTTP/S requests to hosted cloud applications and their use of PaaS services. To do so it employs lightweight monitoring of PaaS service interfaces. Roots processes this data in the background using multiple statistical techniques that in combination detect performance anomalies (i.e. violations of service-level objectives). For each anomaly, Roots determines whether the event was caused by a change in the request workload or by a performance bottleneck in a PaaS service. By correlating data collected across different layers of the PaaS, Roots is able to trace high-level performance anomalies to bottlenecks in specific components in the cloud platform. We implement Roots using the AppScale PaaS and evaluate its overhead and accuracy.

Architecture design evaluation of PaaS cloud applications using generated prototypes: PaaSArch Cloud Prototyper tool

Platform as a Service (PaaS) cloud domain brings great benefits of an elastic platform with many prefabricated services, but at the same time challenges software architects who need to navigate a rich set of services, variability of PaaS cloud environment and quality conflicts in existing design tactics, which makes it almost impossible to foresee the impact of architectural design decisions on the overall application quality without time-consuming implementation of application prototypes. To ease the architecture design of PaaS cloud applications, this paper proposes a design-time quality evaluation approach for PaaS cloud applications based on automatically generated prototypes, which are deployed to the cloud and repeatedly evaluated in the context of multiple quality attributes and environment configurations. In this paper, all steps of the approach are described and demonstrated on an example of a real-world complex IoT system for collection and processing of Smart Home sensor data. The approach has been implemented and the automated prototype generation and evaluation tool, referred to as PaaSArch Cloud Prototyper, is presented together with the approach.

PaaS Cloud Application and Database Portability: An Initial View

A new paradigm to cater to the demand for increasingly complex software systems and to shape the way software applications are developed, has emerged, called Cloud Computing. Evolved from the already prevailing and established technologies such as web services, SOA (Service Oriented architecture), virtualization, grid, and cluster computing, Cloud computing proved it to realize the dream of transforming computing as a utility to the customers. Applications developed at Platform as a Service level (Infrastructure as a Service and Software as a Service being the other two levels) face vendor lock-in issue as the proprietary and non-standard APIs offered by providers results in a lack of interoperability and portability among cloud providers. This paper reports on an experiment done to assess the difficulties encountered while porting an application that uses various SQL and NoSQL data stores, message queue service and blob storage of Microsoft Azure, Amazon Web services and Google Cloud platform among each other. The heterogeneity of the incompatible proprietary interfaces makes the porting a non-trivial task. Various problems faced during the portability of the application are discussed and a middleware solution approach to these problems is proposed in this paper.

A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

Docker container based PaaS cloud computing comprehensive benchmarks using LAPACK

Docker container based PaaS cloud computing comprehensive benchmarks using LAPACK

DeMETER in clouds: detection of malicious external thread execution in runtime with machine learning in PaaS clouds

Current state of PaaS allows rapid outsourcing of web applications without noticeable configuration effort. It could be foreseen that a noteworthy security guarantee in this cloud deployment model make organizations adopt PaaS easier. To date, provisioning security-guaranteed PaaS offerings required isolated processes, which is computationally-intensive and therefore expensive for the cloud provider. A novel security mechanism is proposed in this study to protect the PaaS providers against malicious behavior; thereby, their tenants. The mechanism does not strictly isolate tenants, but let them share the resources as in conventional web applications; therefore the computational efficiency is competitive. The novelty lies in classifying the malicious behavior of worker threads of web applications in a privacy-friendly way; where possible, without interfering with the threads. These threads may execute many code snippets in the same process context on behalf of the provider, the tenants or the tenants’ users in a web application server. It is cumbersome and error-prone to isolate each code snippet separately. Instead, classifying thread behavior helps to detect malicious flow of execution. The proposed mechanism is significantly different from intrusion detection systems or virus scanners as it only focuses on the processor usage and critical resource access. Historical web application attacks based on OWASP reports as well as future trends are analyzed and a sample web traffic of 100,000 requests, which includes 1% malicious traffic rooted from the most common attacks, is generated to prove the concept. The generated web traffic is tested on a cloud-based demo application on a live cloud environment. The thread behavior is monitored only based on CPU load and database access to keep the mechanism privacy-friendly for all cloud stakeholders. Even though the executed instructions are not monitored, the collected telemetry forms a vast amount of trace for classification. This privacy-friendly feature set is extracted and evaluated on several classifiers to detect malicious threads. It is observed that the classification accuracy is remarkably successful.

Merging Securely M2M Protocols, Internet of Things and Cloud Computing

The Internet of Things provides new ways for communication through the Web world using object-enabled networks. At the same time, M2M devices intercommunication and their communication through the web if they were connected to the Internet, presents new challenges, especially in security, that traditional communication models have not yet fully solved. Because of their inborn un-watched, minimal effort and mass-sent nature, M2M devices, and remote communication architectures and solutions for these devices, would encapsulate new dangers in security. These threats are not fully faced by use of security technologies and methods implemented in existing wireless devices, cellular networks or WLANs. The use of cloud computing gives a convenient, on demand and scalable network access to a shared pool of configurable computing resources and devices. This paper concentrates on a secure method to integrate the M2M protocols with the Internet of Things (IoT) and Cloud Computing under the name of Secure Machine-to-Internet Clouding (SM2IC) architecture. The secure design for integrating M2M protocols, along with IoT and cloud computing is proposed. To apply this design, an IoT enabled smart home scenario was examined to analyze secure communication between M2M devices and IoT applications. Also, the cloud computing is used to include different cloud applications, such as, IaaS, PaaS, and SaaS for monitoring the quality of service of M2M devices through IoT applications. Then, simulations were performed to test the proposed security technique, followed by conclusions and future work.