Serverless Computing Research Articles

Composing Serverless Azure Functions in NodeJS with MySQL Database Integration

This paper delves into the concept and implementation of serverless computing, particularly focusing on Azure Functions as a platform for deploying serverless applications. It introduces the notion of a Function app, a container that amalgamates functions into a single logical unit, thereby simplifying management, deployment, and resource sharing. Serverless computing is elucidated as Function as a Service (FaaS), a paradigm where business logic is executed in the form of functions without the need for manual infrastructure management. Azure Functions enables developers to host and execute business logic seamlessly, without the concerns of provisioning infrastructure. The platform supports a multitude of programming languages, including C#, Java, JavaScript, TypeScript, and Python, offering versatility in development approaches. The paper further explores the utilization of triggers, specifically HTTP triggers, for executing code in response to HTTP requests. It highlights the development process within Visual Studio Code, facilitated by the Azure Functions extension, which allows for the comprehensive creation and deployment of projects. Additionally, the distinction between Consumption service plans and App Service Plans is examined, along with the prerequisite of linking every function app to a storage account. Deployment methodologies, both locally and through the Azure portal, are outlined, along with the capabilities for monitoring logs via the Azure portal’s Monitor section. This comprehensive overview provides insights into leveraging Azure Functions for efficient, scalable, and manageable serverless application development.

Latency-Sensitive Function Placement among Heterogeneous Nodes in Serverless Computing.

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.

Self-Provisioning Infrastructures for the Next Generation Serverless Computing

Serverless computing has ushered in a transformative paradigm, with a promise to alleviate developers from the intricacies of infrastructure management. However, current serverless platforms predominantly offer only serverless compute capabilities. As a consequence, the application developers are once again tasked to explicitly provision and manage the backend services (BaaS), such as object stores or API gateways, the infrastructure, and the configuration models. This violates the main promise of serverless computing and erases much of the practical benefits of the serverless paradigm. It also introduces the challenges of managing the application execution environment, which includes maintaining provisioning and deployment scripts, configuring and managing access permissions, and scaling the services during runtime. To address these challenges, in this paper we introduce a novel paradigm for the next generation of serverless computing, called self-provisioning infrastructure. The self-provisioning infrastructure is an infrastructure that is capable to automatically and autonomously (with zero-configuration and zero-touch) provision serverless functions, their infrastructure, and their supporting BaaS services. To achieve this vision, we introduce novel design principles, models, and mechanisms that are formalized via novel programming, function, and system models. With this novel paradigm, we intend to fortify the core design principles of serverless computing but also extend them to the entire application execution environment. By doing so, we aim to enable the next-generation serverless computing in the Edge-Cloud continuum.

Transforming business scalability and operational flexibility with advanced cloud computing technologies

In the rapidly evolving landscape of digital transformation, cloud computing has emerged as a pivotal technology enabling businesses to achieve unparalleled scalability and operational flexibility. This review explores the transformative impact of advanced cloud computing technologies on business operations, highlighting key innovations and their implications for organizational growth and efficiency. Cloud computing offers a dynamic and scalable environment where resources can be provisioned and managed on-demand, allowing businesses to respond swiftly to changing market conditions and customer demands. By leveraging cloud infrastructure, companies can scale their operations seamlessly without the need for significant upfront investments in physical hardware. This flexibility not only reduces capital expenditure but also enhances the ability to innovate and adapt in a competitive marketplace. Advanced cloud technologies, such as multi-cloud and hybrid cloud solutions, further augment operational flexibility by enabling organizations to optimize their IT environments. Multi-cloud strategies allow businesses to distribute workloads across multiple cloud providers, mitigating the risks associated with vendor lock-in and ensuring high availability and redundancy. Hybrid cloud solutions, which integrate on-premises infrastructure with public and private clouds, provide a balanced approach to managing sensitive data and workloads while benefiting from the scalability of the cloud. Moreover, cloud-native technologies like containerization and serverless computing have revolutionized application development and deployment. Containers encapsulate applications and their dependencies, ensuring consistency across different computing environments and facilitating rapid deployment. Serverless computing reviews the underlying infrastructure, allowing developers to focus solely on code, thus accelerating the development cycle and reducing operational overhead. The integration of advanced analytics and artificial intelligence (AI) with cloud computing further enhances business capabilities. Cloud platforms offer robust analytics tools and AI services that can process vast amounts of data in real-time, providing actionable insights and enabling predictive decision-making. This integration empowers businesses to optimize operations, improve customer experiences, and drive strategic initiatives with data-driven precision. In conclusion, advanced cloud computing technologies are instrumental in transforming business scalability and operational flexibility. By harnessing the power of the cloud, organizations can achieve greater agility, cost efficiency, and innovation, positioning themselves for sustained growth and competitive advantage in the digital age. As cloud technologies continue to evolve, their potential to redefine business operations and drive economic value will only expand, making cloud adoption a critical imperative for modern enterprises. Keywords: Operational Flexibility, Transforming, Business Scalability, Advanced, Cloud Computing Technologies.

SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems

Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.

Data pipeline approaches in serverless computing: a taxonomy, review, and research trends

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments.

Role of Emergent Technologies in Entrepreneurial Firms

New technologies are one of the most obvious parts of the emotional change of culture in developing or developed countries. These emergent technologies offer enormous opportunities for the entrepreneurial firms interested in searching for a competing lead in business as well as pose challenges to the firms. The emergent technologies can go a long way in helping the organisations to tap the newly emerging markets with high potential. The present study aims at identifying the role of emergent technologies in influencing the survival and expansion of the business. The study has compiled the reviews of various scholars on the usage of the latest technologies by the firms to achieve a competitive edge. This is a descriptive study and makes use of the secondary data. The findings of the study suggest that technologies, such as AI, robotics and drones, 3D Printing, serverless computing, block chain and so forth have been most important technologies for the firms, as they have completely changed their traditional way of organisation functioning. These technologies enable effective and efficient decision-making and promote the growth and development of the firms. The study makes a value addition to the existing literature on the role of emergent and futuristic technologies in enabling the entrepreneurial firms to pave their way towards sustainable growth in the competitive business environment through ingenious business solutions.

AI-driven resource management strategies for cloud computing systems, services, and applications

Cloud computing is a way for businesses and individuals to It has changed and revolutionized the way we access and use resources. However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. To address these challenges, artificial intelligence (AI) technology has emerged as an effective solution to improve resource management efficiency. This paper provides an overview of AI-based strategies for efficient resource management in cloud computing systems, services, and applications. This paper first reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. Below is an overview of the various AI techniques used for resource management. B. Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. Next, this paper considers specific AI-based strategies for efficient resource management. These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. This paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including B. real-time resource optimization and coordination.

Scalable Cloud Architectures for Deploying AI Applications

The integration of Artificial Intelligence (AI) into cloud computing represents a pivotal shift in the technological landscape, offering unprecedented opportunities for innovation across industries. Deploying AI applications at scale on the cloud poses unique challenges, necessitating the development of scalable cloud architectures tailored to meet the computational demands and data processing needs inherent to AI. This article explores the foundational aspects of cloud computing architectures, emphasizing the principles of scalability essential for AI applications. It delves into the core challenges faced when deploying AI on the cloud, such as computational requirements, data management, network constraints, and security concerns. Further, it presents various architectural models that facilitate scalability, including containerization, serverless computing, and cloud-native AI services, drawing from realworld case studies to illustrate effective strategies and best practices. Additionally, the article examines performance optimization techniques, security considerations, and the future directions of cloud-based AI deployments, highlighting the role of emerging technologies such as quantum computing and edge AI. By providing a comprehensive overview of scalable cloud architectures for AI applications, this article aims to guide researchers, practitioners, and organizations in leveraging cloud computing to its full potential, thereby enabling more efficient, secure, and scalable AI solutions.

Resource Allocation and Pricing in Energy Harvesting Serverless Computing Internet of Things Networks

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC’s profit and MU’s demand. Moreover, both MUs and SECs gain benefits from renewable energy.

Elevating Cloud Security via Serverless Computing: An in Depth Exploration

The research aims to elucidate the transformative potential of serverless architecture in enhancing cloud security, addressing common threats, and mitigating vulnerabilities. Through a systematic review of existing literature, empirical experiments, and case studies, the study delves into the underlying principles of serverless architecture, explores its advantages, challenges, and practical applications, and evaluates its performance and scalability compared to traditional computing models. The findings underscore the scalability, cost-effectiveness, and simplicity of serverless computing, while also highlighting challenges such as cold start latency and vendor lock-in. Moreover, the research identifies key recommendations and best practices for designing, deploying, and managing serverless applications, offering valuable insights for industry practitioners, researchers, and policymakers. Overall, the study contributes to a deeper understanding of serverless computing and its role in shaping the future of cloud-native application development

SecureLaw Portal: Empowering Citizens with Transparent Legal Records via Blockchain and Cloud eVault

In the digital age, where data integrity is essential, the management of legal data requires innovative solutions. SecureLaw is a new project that combines the immutable power of blockchain, the flexibility of Cloud. The agility of the Cloud, Express.js, React.js, Node .js suite. This integration forms the backbone of the transformation of data protection laws, providing unparalleled security, transparency and easy access. Highlights of this project: SecureLaw is instrumental in redefining information law in India. Traditional systems often face security breaches and lack of transparency, undermining the integrity of the legal process. SecureLaw solves these problems by integrating blockchain technology, which provides immutable data that ensures legal documents are not tampered with and ensure their authenticity. The introduction of smart contracts automates the process, controls permissions and makes data immutable, thus increasing efficiency and reducing errors. Leveraging Cloud services such as S3 for storage, API gateways for seamless communications, Lambda for serverless computing, and DynamoDB for scalable databases provides a powerful, scalable, and cost-effective infrastructure. Scalability is important to accommodate legal system growth, and Cloud’s Dynamic Services provide a solution. The AERN group (AWS, Express.js, React.js, Node.js) strengthens the foundation of the project. Express.js is a simple and easy backend framework that seamlessly integrates for efficient data processing and processing. React.js is a front-end library with many features that make it user responsive and intuitive. Node.js is known for its performance that enables dynamic recovery and makes data fast and recoverable. SecureLaw's user centric approach includes managing access to personal and legal personal details. Personal auditors, regional directors, directors (DGs), judges, etc. It deals with many user roles, personal control panels, and access rights. This change promotes cooperation, transparency and communication in the legal system. Features of a self-explanatory legal system encourage people to understand the legal process, fostering transparency, trust, and collaborative knowledge. Keywords: Blockchain, Application Programming Interface (API), Cloud , Express.js, React.js, Node.js

Exploiting microservices and serverless for Digital Twins in the cloud-to-edge continuum

In the Industry 4.0 era, Digital Twins (DTs) serve as virtual representations of physical objects and intermediaries between the physical world and the digital realm. DTs require proper modeling, design, and development to ensure their seamless integration along the cloud-to-edge continuum. In particular, this work introduces a microservices-based and serverless-ready model for DTs, laying the foundation for cost-effective DT deployment and orchestration. The joint adoption of microservices and serverless computing offers significant potential to address various challenges, including accommodating variable application requirements, managing load imbalances, and mitigating network faults. The proposed DT model has been implemented in different flavors: two serverless implementations—one that relies on a serverless framework of a cloud provider and one running at the edge on-premises—and a microservices one. These implementations have been experimentally evaluated with particular emphasis on the quality of cyber–physical entanglement. This work not only discusses the advantages and drawbacks of different implementations from a qualitative perspective but also quantitatively evaluates them with the in-the-field collection of experimental performance results. Notably, we report that a serverless implementation typically performs an order of magnitude worse than a microservices one in terms of entanglement, i.e., hundreds vs. tens of milliseconds.

Cheaper and Faster: Distributed Deep Reinforcement Learning with Serverless Computing

Deep reinforcement learning (DRL) has gained immense success in many applications, including gaming AI, robotics, and system scheduling. Distributed algorithms and architectures have been vastly proposed (e.g., actor-learner architecture) to accelerate DRL training with large-scale server-based clusters. However, training on-policy algorithms with the actor-learner architecture unavoidably induces resource wasting due to synchronization between learners and actors, thus resulting in significantly extra billing. As a promising alternative, serverless computing naturally fits on-policy synchronization and alleviates resource wasting in distributed DRL training with pay-as-you-go pricing. Yet, none has leveraged serverless computing to facilitate DRL training. This paper proposes MinionsRL, the first serverless distributed DRL training framework that aims to accelerate DRL training- and cost-efficiency with dynamic actor scaling. We prototype MinionsRL on top of Microsoft Azure Container Instances and evaluate it with popular DRL tasks from OpenAI Gym. Extensive experiments show that MinionsRL reduces total training time by up to 52% and training cost by 86% compared to latest solutions.

Incorporating Serverless Computing into P2P Networks for ML Training: In-Database Tasks and Their Scalability Implications (Student Abstract)

Distributed ML addresses challenges from increasing data and model complexities. Peer to peer (P2P) networks in distributed ML offer scalability and fault tolerance. However, they also encounter challenges related to resource consumption, and communication overhead as the number of participating peers grows. This research introduces a novel architecture that combines serverless computing with P2P networks for distributed training. Serverless computing enhances this model with parallel processing and cost effective scalability, suitable for resource-intensive tasks. Preliminary results show that peers can offload expensive computational tasks to serverless platforms. However, their inherent statelessness necessitates strong communication methods, suggesting a pivotal role for databases. To this end, we have enhanced an in memory database to support ML training tasks.

The Promise of Serverless Computing within Peer-to-Peer Architectures for Distributed ML Training

My thesis focuses on the integration of serverless computing with Peer to Peer (P2P) architectures in distributed Machine Learning (ML). This research aims to harness the decentralized, resilient nature of P2P systems, combined with the scalability and automation of serverless platforms. We explore using databases not just for communication but also for in-database model updates and gradient averaging, addressing the challenges of statelessness in serverless environments.

Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture

Smart cities and healthcare services have been gaining much attention in recent years, as the benefits offered by this field of research are significant and improve quality of life. Systems can proactively detect health problems by monitoring a person’s vital signs and make automated decisions to prevent these problems from worsening. With this in mind, we highlight two essential requirements that smart city architectures should consider to achieve elevated quality of experience in the healthcare domain. The first is to process high-priority vital signs with short response times, so people with comorbidities can have health problems identified as soon as possible. The second is to employ scalability techniques to deal with high usage peaks resulting from people concentrating in specific city neighborhoods. This paper contributes to this field of research by proposing SmartVSO - a computational model of a hierarchic, scalable, tree-based, fog-cloud architecture that executes healthcare services with reduced response time for urgent vital signs. Fog computing is employed to reduce response times, and cloud computing is used to provide virtually infinite computing resources. Serverless computing is the main technology we consider for deploying and running healthcare services because this allows authorized companies to implement their own services in a distributed and pluggable approach, without recompiling the proposed modules and reducing scalability concerns for engineers. An experiment with 80,000 vital signs indicates that our solution processes 60% of the very critical ones in no more than 5.3 s, while an architecture without fog computing and without prioritization takes up to 231 min (around 3 h and 51 min) to process 60% of very critical vital signs.

Impact of Serverless Computing on Scalability and Cost-Effectiveness in Cloud-based Applications

Impact of Serverless Computing on Scalability and Cost-Effectiveness in Cloud-based Applications

Unveiling the AWS SAM Magic for Serverless Restful APIs: Architecting with ALB Path-Based Routing in AWS

Purpose: This paper provides a thorough roadmap for developers, architects, and cloud enthusiasts who want to use the AWS Serverless Application Model (AWS SAM) to create a REST API and use the power of serverless computing. To handle HTTP requests effectively, the article focuses on deploying the API behind an Application Load Balancer (ALB) using path-based routing. The hands-on approach offers detailed instructions and valuable insights on planning, creating, and implementing serverless REST APIs. The focus is on the details of AWS SAM, examining its benefits and complexities. The paper makes the procedure easier to understand by providing thorough code excerpts, explanations, and pictures.
 Methodology: The methodology covers local testing using the SAM CLI, allowing developers to validate the API's functionality before deployment.
 Findings: The process also includes local testing with the SAM CLI, which enables developers to confirm the functioning of the API before deployment. To target the Lambda function, this paper will discuss AWS Lambda behind an ALB using a path-based listener rule on the ALB. The article’s conclusions cover essential topics like path-based routing, ALB integration, AWS SAM template structure, and recommended security and performance optimization practices.
 Unique Contributor to Theory, Policy and Practice: Based on these findings, recommendations offer information on optimizing templates, ensuring secure deployment, and using local testing to speed up development. Finally, the article walks readers through deploying the built API to AWS via the SAM CLI, facilitating a seamless transfer from a local development environment to an environment in production. Ultimately, this paper provides readers with the know-how and abilities to successfully negotiate AWS SAM's complexities and build reliable serverless REST APIs.

Optimizing serverless computing: A comparative analysis of multi-output regression models for predictive function invocations

In the rapidly evolving domain of serverless computing, the need for efficient and accurate predictive methods of function invocation becomes paramount. This study introduces a comprehensive suite of innovations to improve the predictability and efficiency of function invocation within serverless architectures. By employing multi-output regression models, we perform a multi-level analysis of function invocation patterns across user, application, and function levels, revealing insights into granular workload behaviors. We rigorously investigate the impact of windowing techniques and dimensionality reduction on model performance via Principal Component Analysis (PCA), offering a nuanced understanding of data complexities and computational implications. Our novel comparative analysis framework meticulously evaluates the performance of these methods against various windowing configurations, utilizing the Azure Functions dataset for real-world applicability. In addition, we assess the temporal stability of the models and the variation of day-to-day performance, providing a holistic view of their operational viability. Our contributions address critical gaps in the predictive modeling of serverless computing and set a new benchmark for operational efficiency and data-driven decision-making in cloud environments. This study is poised to guide future advancements in serverless computing, driving theoretically sound and practically viable innovations.