Serverless Computing Research Articles

Boki: Towards Data Consistency and Fault Tolerance with Shared Logs in Stateful Serverless Computing

Bokiis a new serverless runtime that exports a shared log API to serverless functions. Boki shared logs enable stateful serverless applications to manage their state with durability, consistency, and fault tolerance. Boki shared logs achieve high throughput and low latency. The key enabler is the metalog , a novel mechanism that allows Boki to address ordering, consistency and fault tolerance independently. The metalog orders shared log records with high throughput and it provides read consistency while allowing service providers to optimize the write and read path of the shared log in different ways. To demonstrate the value of shared logs for stateful serverless applications, we build Boki support libraries that implement fault-tolerant workflows, durable object storage, and message queues. Our evaluation shows that shared logs can speed up important serverless workloads by up to 4.2×.

Optimizing Web Application Development: A Proposed Architecture Integrating Headless CMS and Serverless Computing

Optimizing Web Application Development: A Proposed Architecture Integrating Headless CMS and Serverless Computing

Demystifying Serverless Architecture for Scalable Web Applications

Serverless computing has revolutionized the development and deployment of scalable web applications by abstracting infrastructure management and enabling automatic scaling. This article aims to demystify serverless architecture by thoroughly analyzing its core concepts, benefits, and practical implementation strategies. We differentiate between Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) models and explore how they contribute to scalability and cost efficiency. Deployment best practices are outlined using AWS Lambda, Azure Functions, and Google Cloud Functions. This article addresses common challenges associated with serverless computing, including cold starts and execution time limitations, and propose solutions to mitigate these issues. Integration with DevOps practices is also discussed, demonstrating how serverless applications can be incorporated into continuous integration and continuous deployment (CI/CD) pipelines. Through case studies, this article illustrates the effective utilization of serverless architecture in building scalable and resilient web applications.

Reducing the cost of cold start time in serverless function executions using granularity trees

In serverless computing, cold starts significantly impede performance. This paper presents a granularity tree-based scheduling strategy, dynamically adjusting serverless function deployment by package dependencies to mitigate cold starts and optimize resource usage. This approach notably reduces cold start and response times. Empirical results from evaluating functions across various datasets show the strategy outperforms existing methods. Specifically, it consistently delivers lower response times and decreases resource consumption, demonstrating its effectiveness in managing computational resources while ensuring swift function invocation. In particular scenarios, the proposed scheduler impressively reduced response times from 8134.1 ms to 392.8 ms and idle memory usage from 15.2 GB to 11.2 GB per machine. In other scenarios, it reduced response times from 12,152.7 ms to 504.2 ms while maintaining a 100% function execution percentage. These quantified improvements underscore the significant enhancements in cold start mitigation and overall system performance, highlighting the potential of granularity tree-based scheduling in enhancing serverless computing architectures by effectively balancing rapid response with reduced resource usage.

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.

Mastering Microservices Architecture and Cloud Computing : In-Depth Strategies for Domain-Specific Optimization

This comprehensive article explores the intricate landscape of microservices architecture and cloud computing, specifically focusing on domain-specific optimization strategies. It delves into the foundational principles of microservices, examining their synergistic relationship with cloud technologies and their transformative impact on modern software development. The article addresses critical aspects such as service-to-service communication, API management, and data security in distributed environments. It also investigates the role of cloud-native services, including serverless computing and container orchestration, in enhancing microservices performance. Analyzing real-world case studies and emerging trends, the article highlights how organizations can leverage these technologies to drive innovation and maintain a competitive edge. Key challenges in implementing and managing microservices architectures and strategies for overcoming them are discussed. The research emphasizes the importance of tailoring microservices to specific business needs and provides insights into performance tuning for high-traffic systems. This article offers a forward-looking perspective on the evolution of microservices and cloud computing by exploring future trends and considerations, including the potential impact of emerging technologies like edge computing and artificial intelligence. It serves as a valuable resource for IT professionals, architects, and decision-makers seeking to master these technologies and implement domain-specific optimizations effectively in their organizations.

Serverless Computing: Optimizing Resource Utilization and Cost Efficiency

Serverless computing has emerged as a transformative paradigm in cloud infrastructure, offering organizations the ability to scale their applications dynamically without the burden of managing underlying servers. By abstracting away the provisioning and scaling of infrastructure, serverless computing enables developers to focus on building and deploying their applications, while the cloud provider handles the auto- scaling, load balancing, and fault tolerance. This paper examines the key benefits and challenges of serverless computing, with a particular emphasis on optimizing resource utilization and cost efficiency. The findings suggest that serverless computing can lead to significant improvements in resource utilization and cost savings, but organizations must also address challenges related to cold starts, vendor lock-in, and monitoring complexity to fully realize the potential of this cloud computing paradigm.

Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.

A deep reinforcement learning approach towards distributed Function as a Service (FaaS) based edge application orchestration in cloud-edge continuum

Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable for bandwidth limited, real time and secure applications. Therefore, shifting the resources of the cloud-edge continuum towards the edge can mitigate these limitations. In serverless architecture, applications implemented as Function as a Service (FaaS), include a set of chained event-driven microservices which have to be assigned to available instances. IoT microservices orchestration is still a challenging issue in serverless computing architecture due to IoT dynamic, heterogeneous and large-scale environment with limited resources. The integration of FaaS and distributed Deep Reinforcement Learning (DRL) can transform serverless computing by improving microservice execution effectiveness and optimizing real-time application orchestration. This combination improves scalability and adaptability across the edge-cloud continuum. In this paper, we present a novel Deep Reinforcement Learning (DRL) based microservice orchestration approach for the serverless edge-cloud continuum to minimize resource utilization and delay. This approach, unlike existing methods, is distributed and requires a minimum subset of realistic data in each interval to find optimal compositions in the proposed edge serverless architecture and is thus suitable for IoT environment. Experiments conducted using a number of real-world scenarios demonstrate improvement of the number of successfully composed applications by 18%, respectively, compared to state-of-the art methods including Load Balance, Shortest Path algorithms.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Toward security quantification of serverless computing

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.

Reducing cold start delay in serverless computing using lightweight virtual machines

In recent years, serverless computing has gained considerable attention in academic, professional, and business circles. Unique features such as code development flexibility and the cost-efficient pay-as-you-go pricing model have led to predictions of widespread adoption of serverless services. Major players in the cloud computing sector, including industry giants like Amazon, Google, and Microsoft, have made significant advancements in the field of serverless services. However, cloud computing faces complex challenges, with two prominent ones being the latency caused by cold start instances and security vulnerabilities associated with container escapes. These challenges undermine the smooth execution of isolated functions, a concern amplified by technologies like Google gVisor and Kata Containers. While the integration of tools like lightweight virtual machines has alleviated concerns about container escape vulnerabilities, the primary issue remains the increased delay experienced during cold start instances in the execution of serverless functions. The purpose of this research is to propose an architecture that reduces cold start delay overhead by utilizing lightweight virtual machines within a commercial architecture, thereby achieving a setup that closely resembles real-world scenarios. This research employs supervised learning methodologies to predict function invocations by leveraging the execution patterns of other program functions. The goal is to proactively mitigate cold start scenarios by invoking the target function before actual user initiation, effectively transitioning from cold starts to warm starts. In this study, we compared our approach with two fixed and variable window strategies. Commercial platforms like Knative, OpenFaaS, and OpenWhisk typically employ a fixed 15-minute window during cold starts. In contrast to these platforms, our approach demonstrated a significant reduction in cold start incidents. Specifically, when calling a function 200 times with 5, 10, and 20 invocations within one hour, our approach achieved reductions in cold starts by 83.33%, 92.13%, and 90.90%, respectively. Compared to the variable window approach, which adjusts the window based on cold start values, our proposed approach was able to prevent 82.92%, 91.66%, and 90.56% of cold starts for the same scenario. These results highlight the effectiveness of our approach in significantly reducing cold starts, thereby enhancing the performance and responsiveness of serverless functions. Our method outperformed both fixed and variable window strategies, making it a valuable contribution to the field of serverless computing. Additionally, the implementation of pre-invocation strategies to convert cold starts into warm starts results in a substantial reduction in the execution time of functions within lightweight virtual machines.

Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments

This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.

A survey on the cold start latency approaches in serverless computing: an optimization-based perspective

A survey on the cold start latency approaches in serverless computing: an optimization-based perspective

Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions

Serverless computing has revolutionized cloud services by abstracting infrastructure management, enabling developers to focus on application logic. This research examines the security and compliance features of three major serverless platforms: AWS Lambda, Azure Functions, and Google Cloud Functions. By evaluating authentication mechanisms, data encryption practices, vulnerability management, and compliance certifications, we aim to provide a comparative analysis that informs businesses and developers on the most secure and compliant platform for their needs. Keywords: Serverless Computing, Security, Compliance, AWS Lambda, Azure Functions, Google Cloud Functions, Cloud Security Alliance, Cloud Controls Matrix

A method to shorten the time of high-dimensional matrix operations and convolution operations

Serverless computing, or Function-as-a-Service (FaaS), is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers which is a new way to solve the problem that the need of client.By serverless computing clients can easily solve their problems anytime,anywhere.However, the complex convolution operations and high-dimensional matrix operations can still consume a lot of resources such as computation time and memory occupancy in serverless computing. In this paper, firstly, the author selects two frequently used models in serverless computing, and then divide them into different numbers of partitions respectively to study the impact of partitioning methods on the computation time and memory occupancy. The author show that different partitioning methods can make an obviously impact on the computation time and memory occupancy,whitch shows that the proper partitioning of high-dimensional matrices and tensors can make a significantly reduction of calculation time and memory occupancy.

Design and implementation of efficient distributed deep learning model inference architecture on serverless computation

Although the distributed algorithm and architecture have been widely used in the development of traditional large-scale machine model reasoning tasks, but it is unavoidable about the source waste caused by synchronization between learners and participants. However, serverless computing is charged based on usage, which makes it a popular alternative for model inference. This article mainly proposes the architecture and algorithm that can be used for the inference task of the distributed deep learning model and adopts coarse-grained parallelization strategy to solve the problem of high communication cost between Serverless platform functions. The paper tested the performance of the model on the deep learning model and found that the model reasoning based on the Serverless platform can better cope with the requirements and changes, and ensure the stability of the system. It is worth noting that both the data owner and the model owner can benefit from the process of carrying out the machine learning model inference task, but existing solutions cannot satisfied with them privacy requirements, which is a problem needs to think about in the field of machine learning inference tasksbased on the Serverless platform in the future.

Efficient and scalable covariate drift detection in machine learning systems with serverless computing

As machine learning models are increasingly deployed in production, robust monitoring and detection of concept and covariate drift become critical. This paper addresses the gap in the widespread adoption of drift detection techniques by proposing a serverless-based approach for batch covariate drift detection in ML systems. Leveraging the open-source OSCAR framework and the open-source Frouros drift detection library, we develop a set of services that enable parallel execution of two key components: the ML inference pipeline and the batch covariate drift detection pipeline. To this end, our proposal takes advantage of the elasticity and efficiency of serverless computing for ML pipelines, including scalability, cost-effectiveness, and seamless integration with existing infrastructure. We evaluate this approach through an edge ML use case, showcasing its operation on a simulated batch covariate drift scenario. Our research highlights the importance of integrating drift detection as a fundamental requirement in developing robust and trustworthy AI systems and encourages the adoption of these techniques in ML deployment pipelines. In this way, organizations can proactively identify and mitigate the adverse effects of covariate drift while capitalizing on the benefits offered by serverless computing.

Data-Driven Framework for Cloud Storage Security Optimization: Leveraging Predictive Analytics and Machine Learning to Enhance Threat Detection and Incident Response

As cloud storage adoption accelerates, securing sensitive data against evolving threats, including Advanced Persistent Threats (APTs), Zero-Day Exploits, and Insider Threats, has become paramount. This research introduces a data-driven framework that harnesses predictive analytics, machine learning (ML), and deep learning (DL) techniques to fortify threat detection and incident response in cloud storage environments. By integrating real-time monitoring via Security Information and Event Management (SIEM) systems, anomaly detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and predictive modeling through Graph-Based Anomaly Detection (GBAD), our framework identifies potential security risks and optimizes countermeasures. Leveraging ML algorithms, such as Random Forest and Support Vector Machines (SVM), our approach analyzes historical incident data, user behavior, and system logs to predict and prevent attacks. Key benefits include proactive security measures, reduced response times via Security Orchestration, Automation, and Response (SOAR), and minimized data breaches through Containerization (Docker) and Serverless Computing (AWS Lambda). This research advances the development of intelligent cloud storage security solutions, ensuring robust protection for sensitive data in cloud-based infrastructure, compliant with PCI-DSS, HIPAA, GDPR, NIST Cybersecurity Framework, and ISO 27001.

A REVIEW OF THE IMPACT OF COVID-19 ON SERVERLESS COMPUTING TECHNOLOGY

The Covid-19 pandemic had a profound effect on technology in general, and serverless computing is no exception. Covid-19 pandemic has shaped the field of serverless computing, and how technology has evolved in response. Serverless computing technology have been adapted to meet the need for remote working, and how the technology has changed in terms of scalability and cost-effectiveness. This pandemic has affected virtually every aspect of daily life as significant measures are being taken to limit the spread of the virus. The pandemic has changed not only the way companies operate, but also the way they have been able to survive. Studies indicate increased requests for cloud services ranging from resident users, particularly for telecommuting, entertainment, commerce, to education, and as a result, causing traffic shifts at the core of the Internet. Covid-19 had such a significant impact on cloud services that there is an unprecedented amount of demand for cloud service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This study used data from a variety of sources to analyse the impact of serverless computing during the pandemic and to justify its significance for a pandemic-affected business. It also reviewed the pre-Covid, Covid and post-Covid-19 era. Two survey reports were used in this study and the effect of Covid-19 on Serverless computing. This paper emphasizes the benefits and adoption of Serverless computing during the pandemic, in contrast to other studies that concentrated on the impact of the Covid-19 epidemic on the cloud computing environment.