Comparison of using Apache OpenWhisk and Google Cloud Functions for development of serverless applications on Google Cloud Platform

Cloud Functions for Firebase lets you run code on a cloud-based back end in response to events triggered by some of Firebase features and/or HTTPS requests. You'll code using Node.js, and the code is stored and executed on Google's cloud in a managed environment. The goal is to help you avoid managing middleware servers and just focus on the code. Scaling happens automatically because the code runs on the Google Cloud Platform. It's based on Google Cloud Functions, which you can learn about at . This book will focus on how these functions work on Firebase.

Serverless computing brings another revolution to cloud computing as function-as-a-service (FaaS) where the applications are abstracted as a group of functions. Serverless applications are cost-effective and manage resources efficiently but the lack of performance modeling and energy optimization affects the potential users' broad adoption of serverless computing. Performance enhancement and energy optimization are necessary to guarantee the serverless applications' service level agreement (SLA). This review paper presents various performance metrics in serverless computing, including cost, scalability, latency, energy consumption, resource utilization, fault tolerance, and response time. Based on these metrics, various performance modeling and energy optimization techniques have been explored to reduce energy consumption and improve system efficiency. Furthermore, the review investigates software platforms for implementing serverless computing, including AWS Lambda, Apache OpenWhisk, Azure Functions, and Google Cloud Functions, highlighting key findings and limitations. This comprehensive review serves as a guide for researchers, directing them toward new and promising research directions in the field.

Proceeding for an elucidating draft for the robot’s path planning, there are several aspects which need to be addressed and discuss in detail. Some key points include the definition of the working environment, type of technique used to solve a particular type of problem, their subclassification and interdependencies with each other. It is also required to discuss in detail to understand and solve the problem of path planning in the right perspective. To do so, this paper presents the evolution of path planning algorithms of the last 5 decades, and then a broad classification of all those algorithms. After that, algorithms are categorized into different sections and has been discussed which are very often used in recent times to make the path planning approach more effective and efficient. This paper presents an extensive and elaborative survey of path planning algorithms and associated techniques for robots whose excellent contribution to the filed is invaluable. There are multiple issues related to the working environment of robots, whether it belongs to the two dimensional or three dimensional, static or dynamic, single robot or multi-robot, single objective/multi-objective and many more. This paper addresses all these issues in a detailed way. Another critical issue is related to the scope of algorithms which has been discussed at length in this paper like whether the algorithm is compatible for global/local path planning, it is Exact or heuristic in nature. This issue is systematically and hierarchically described to get a clear understanding of the problem domain. The effort is to bring an insight into the classification and evolution of path planning algorithms with its technical detail and discussions. The paper presents some emerging technologies which can be clubbed with robots to take it to another level. Cloud computing is one which is being extensively absorbed in many technologies. This paper discusses some cloud platforms like OpenStack and Google Cloud to deploy path planning applications for robots. This paper tries explicitly to conclude by testing path planning robots using Google cloud platform (using it as Infrastructure as a Service IaaS) citing its advantages and capability to expand its acceptability and assumes to be the future scope for robot path planning.

Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

<title>Abstract</title> Serverless computing reduces management overhead and scales automatically, yet its latency and performance differ significantly across cloud platforms. This study evaluates serverless function execution and infrastructure-level characteristics on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We benchmark function invocation latency in edge deployments, conduct regional latency comparisons across AWS and Azure, and use PerfKit Benchmarker to measure network throughput, network latency, and storage I/O on AWS and GCP. Results show that Azure recorded the lowest minimum latency (555 ms) but suffered from high tail delays, while GCP delivered the most consistent average performance (1.14 s). AWS exhibited moderate latency with a relatively stable distribution. At the infrastructure level, GCP nearly doubled single-stream throughput (9.35 Gb/s) compared to AWS (4.97 Gb/s), whereas AWS achieved slightly lower round-trip network latency (60 vs. 67 $\mu$s). Storage I/O performance was nearly identical across both providers. These findings link infrastructure characteristics to observed serverless behavior, providing actionable insights for latency-sensitive and multi-cloud application deployments.

Tele-ophthalmology is gaining recognition for its role in improving eye care accessibility via cloud-based solutions. The Google Cloud Platform (GCP) Healthcare API enables secure and efficient management of medical image data such as high-resolution ophthalmic images. This study investigates cloud-based solutions' effectiveness in tele-ophthalmology, with a focus on GCP's role in data management, annotation, and integration for a novel imaging device. Leveraging the Integrating the Healthcare Enterprise (IHE) Eye Care profile, the cloud platform was utilized as a PACS and integrated with the Open Health Imaging Foundation (OHIF) Viewer for image display and annotation capabilities for ophthalmic images. The setup of a GCP DICOM storage and the OHIF Viewer facilitated remote image data analytics. Prolonged loading times and relatively large individual image file sizes indicated system challenges. Cloud platforms have the potential to ease distributed data analytics, as needed for efficient tele-ophthalmology scenarios in research and clinical practice, by providing scalable and secure image management solutions.

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

As software engineers, it is not an easy task to build a software solution that customers consider easy to use, cost-benefit balanced, secured, high available and prepared for high demand. There are many solutions for building a monitoring platform with these previous attributes and we need to consider all the technical factors to achieve this goal. We proposed an agile methodology for the project management approach, acquired the necessary equipment and services and hired the staff to design and develop a monitoring platform in Google Cloud Platform (GCP) with edge frameworks and technologies. We implemented a software working solution composed of five applications (app) or services and configured services in the GCP: IoT data web service, frontend web app, backend web app, IoT app, and mobile app. They were developed using Python, ReactJS, and Java and deployed in Google Cloud Platform for being used in personal computers, laptops, mobile devices, and IoT devices. We defined this architecture with its main basis in a cloud platform that used the publish/subscribe pattern for efficient data ingestion and reporting.

Serverless computing has fundamentally transformed cloud technology by enabling developers to deploy applications without the burden of managing server infrastructure. This paper delves into the serverless offerings from major cloud providers—AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform)—and explores how each contributes to this paradigm shift. The discussion highlights the core advantages of serverless computing, including cost efficiency through a pay-as-you-go model, automatic scaling that adjusts to changing demands, and reduced operational complexity, which allows developers to focus more on coding and less on infrastructure concerns. However, serverless computing is not without its challenges. Notable issues include "cold start" delays, where initial function executions may experience latency, impacting overall performance. Additionally, the shared nature of serverless environments raises potential security concerns, and the limited visibility into the execution environment can make debugging and optimization more complex. The paper also considers future trends in serverless computing across AWS, Azure, and GCP. It discusses emerging approaches such as combining serverless with traditional computing models to create hybrid architectures that leverage the strengths of both. Furthermore, it examines ongoing advancements aimed at improving support for complex, stateful applications, seeking to address current limitations and enhance the capabilities of serverless platforms. This exploration provides a detailed look at the current state of serverless computing and anticipates future developments, focusing on how AWS, Azure, and GCP are shaping the evolution of this technology

This chapter focuses on designing and building scalable and resilient architectures on Google Cloud Platform (GCP). The chapter begins by introducing the principles of scalable and resilient design, including fault tolerance, elasticity, and scalability. You will learn about the key components of scalable and resilient architectures on GCP, including load balancing, autoscaling, and distributed databases. The chapter also covers best practices for building scalable and resilient architectures on GCP, including using managed services, designing for failure, and monitoring and alerting. Overall, this chapter provides a comprehensive guide to building scalable and resilient architectures on GCP, with practical examples and real-world use cases.

As enterprises increasingly migrate to cloud platforms, managing the cost-efficiency of data warehouses in environments such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure has become a critical concern. This paper proposes a conceptual model for optimizing the financial and operational management of cloud-based data warehouses. Through a synthesis of recent peer-reviewed studies, whitepapers, and real-world implementation reports from 2015 to 2024, the model integrates strategic design principles, workload optimization techniques, and governance frameworks across multi-cloud ecosystems. The proposed model emphasizes dynamic workload management, tiered storage optimization, intelligent scaling policies, and metadata-driven governance to ensure cost control without compromising performance. Key architectural components include serverless and autoscaling compute layers, storage lifecycle management, query optimization strategies, and automated performance tuning mechanisms. Particular focus is placed on the unique features and pricing models of AWS Redshift, GCP BigQuery, and Azure Synapse Analytics, detailing how organizations can exploit platform-specific capabilities to enhance cost-efficiency. Furthermore, the model incorporates modern innovations such as FinOps practices, usage-based cost allocation, predictive scaling powered by machine learning, and real-time cost observability dashboards. It also outlines potential pitfalls, such as overprovisioning, inefficient data partitioning, and underutilized reserved instances, and provides mitigation strategies to address them. By aligning technical architecture decisions with proactive financial operations, this conceptual model offers a pathway for organizations to balance performance, scalability, and budget constraints effectively. The study concludes by recommending future directions, including AI-driven autonomous warehouse management, unified billing optimization across multi-cloud deployments, and frameworks for continuous cost-performance evaluation. Mastering cost-efficient warehouse management is increasingly essential for organizations seeking to maximize the value of their data assets while maintaining fiscal responsibility in complex, distributed cloud environments.

Rapid expansion in cloud event technologies such as Amazon Web Service's Lambda, IBM Bluemix's OpenWhisk, Google Cloud Platform's Cloud Functions, and Microsoft Azure's Functions motivates study of software development in these services and their potential as a disruptive force in commercial cloud technologies. In addition to discussing the current state of cloud event services, this paper presents two real world applications utilizing these platforms: Lambdefy, a library designed to make traditional web application run effectively in AWS Lambda, and a performant media management service designed by Trek10, capable of resizing thousands of images per second. Furthermore, we discuss how cloud event technologies enable and/or limit these applications, motivate new software design paradigms in a cloud event environment, and highlight compelling use case scenarios and barriers to entry for cloud event services. AWS cloud technologies are exclusively used due to their maturity and the recent release of the other platforms, while Node.js and the Serverless Framework are utilized for deployment and application development.

MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

Complaint handling is essential to Syiah Kuala University’s Integrated Laboratory Technical Management Unit (ILTMU) services. However, manual complaint handling at ILTMU requires more time and energy and increases the staff workload. Complaint handling is necessary to continuously improve the performance efficiency and service quality provided by ILTMU. Therefore, to solve the problem of handling complaints manually, this research intends to design and develop a web-based complaint management system at ILTMU of Syiah Kuala University. The proposed system uses the Goal-Directed Design method, while the development process follows Scrum. The system was developed using Flask and some services from the Google Cloud Platform, namely, the Google App Engine and Google Cloud Datastore. The system is divided into four unique user groups. Each has access privileges and responsibilities: operators, Management, Follow-up, and Customers. Each of these user groups has its distinctive features. The entire features of this application were tested using both Black Box and White Box testing. All system functions were successfully executed using the Black Box testing technique. Meanwhile, the White Box testing returned a value of 100\% passed, indicating that the function being tested is error-free. A usability test was conducted with 20 respondents representing each user group using the USE Questionnaire on a 7-point Likert scale. The usability test results gave an overall average value of 6.13, which suggests that people have a positive attitude toward the system and that it is practical, easy to use, easy to learn, and satisfying.

The rapid advancement of cloud computing has revolutionized how businesses operate, offering scalable resources and cutting - edge technologies. This paper presents a comparative study of the three leading cloud providers?Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) - focused on their integration of Artificial Intelligence (AI) with DevOps practices. We evaluate their AI service offerings, DevOps toolchains, and the seamless integration of these technologies to enhance automation, scalability, and reliability. The analysis provides insights into the strengths and weaknesses of each provider, helping organizations make informed decisions on their cloud strategy. By analyzing the AI capabilities, DevOps toolchain integration, and overall performance of these cloud providers, this study aims to provide insights into selecting the most suitable platform for AI - driven DevOps workflows. The comparative analysis will focus on factors such as AI services, scalability, automation capabilities, and integration with DevOps tools to help organizations make informed decisions when choosing a cloud provider for their AI and DevOps initiatives.