АРХИТЕКТУРНЫЕ ПАТТЕРНЫ ДЛЯ БЕССЕРВЕРНЫХ ПРИЛОЖЕНИЙ: СРАВНИТЕЛЬНЫЙ АНАЛИЗ ПОДХОДОВ И ПРАКТИЧЕСКИЕ АСПЕКТЫ РЕАЛИЗАЦИИ

In this paper, we provide a multivocal literature review of Function as a Service (FaaS) infrastructures. FaaS is an important, emerging category of cloud computing, which requires that software applications are designed and deployed using distributed, highly-decoupled service-based architectures, one example of which is the microservices architecture paradigm. FaaS is associated with on-demand functionality and allows developers to build applications without the overhead associated with server management. As such, FaaS is a type of serverless provisioning model wherein a provider dynamically manages and allocates machine resources, with the developers deploying source code into a production environment. This research provides an analysis of scalability, cost, execution times, integration support, and the constraints associated with FaaS services provided by several vendors: AWS Lambda, Google Cloud Functions, and Azure Functions. We discuss the implications of the findings for software developers.

Real-time, competitive gaming has also resulted in higher demands of ultra-low latency in multiplayer gaming environments. Scalability and performance of the Traditional client-server models are a problem when the number of players involved and the speed of interactions is high, which introduces problems of latency, lag and synchronization errors. The current paper discusses the usage of serverless architectures as the means of addressing such issues. Serverless computing fully manages the infrastructure, providing developers with an opportunity to write event-driven code, automatically scaling the resources depending on the demand. AWS Lambda, Azure Functions, and Google Cloud Functions are services that offer low latency and scalable solutions to real-time multiplayer games. This paper presents an architectural overview, design, and implementation of serverless systems, analyses the performance monitor, and finally states the benefits of serverless computing when used in serving dynamical gaming workloads. It also discusses how serverless technologies may affect multiplayer gaming industry in future by improving scalability, cutting down operational costs and ensuring uninterrupted gaming experience to the players.

This research paper examines the evolution and current state of serverless computing frameworks for deploying artificial intelligence (AI) models in real-time applications. As AI adoption accelerates across industries, the need for efficient, scalable, and cost-effective deployment solutions has become increasingly critical. Serverless computing offers a promising approach for AI model deployment by providing on-demand computational resources without infrastructure management overhead. This study evaluates five prominent serverless computing frameworks—AWS Lambda, Google Cloud Functions, Azure Functions, OpenWhisk, and Knative—against key performance metrics relevant to AI workloads. Through empirical testing with various AI model types and sizes, we analyze cold start latency, execution time, throughput, cost efficiency, and resource utilization. Our findings indicate that while all frameworks demonstrate viable paths for AI deployment, significant differences exist in their performance characteristics depending on model complexity, input size, and concurrency requirements. We identify specific optimization strategies and architectural patterns that enhance real-time AI deployment on serverless platforms and propose a decision framework to guide implementation choices based on application requirements and constraints.

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.

Serverless functions [6, 9, 13, 14], like AWS Lambda [3] or Google Cloud Functions [7], are new techniques for running short-lived workloads over a cloud, which are particularly preferred by users for their easy deployment, fine-grained billing and automatic scaling. Unlike traditional cloud offerings such as VMs and containers, these functions are stateless, where each function execution starts with a fresh state of memory, disk, and other resources. A multi-tenant serverless cloud platform has two primary components, a gateway controlled by the cloud service provider (CSP) and the user functions which are users' programs. A typical serverless function execution is stateless and involves broadly the following steps: (1) User request is received by the gateway, (2) Gateway executes the function and passes the request arguments, (3) The function performs necessary computation, (4) Optionally, it can call other functions through the gateway or access external Database, and (5) It returns the result to the user.

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

Serverless architecture encompasses many things, and before jumping into creating serverless applications, it is important to understand exactly what serverless computing is, how it works, and the benefits and use cases for serverless computing. Generally, when people think of serverless computing, they tend to think of applications with back-ends that run on third-party services, also described as code running on ephemeral containers. In my experience, many businesses and people who are new to serverless computing will consider serverless applications to be simply “in the cloud.” While most serverless applications are hosted in the cloud, it’s a misperception that these applications are entirely serverless. The applications still run on servers that are simply managed by another party. Two of the most popular examples of this are AWS Lambda and Azure functions. We will explore these later with hands-on examples and will also look into Google’s Cloud functions.

The exponential growth of Internet of Things (IoT) has given rise to a new wave of edge computing due to the need to process data on the edge, closer to where it is being produced and attempting to move away from a cloud-centric architecture. This provides its own opportunity to decrease latency and address data privacy concerns along with the ability to reduce public cloud costs. The serverless computing model provides a potential solution with its event-driven architecture to reduce the need for ever-running servers and convert the backend services to an as-used model. This model is an attractive prospect in edge computing environments with varying workloads and limited resources. Furthermore, its setup on the edge of the network promises reduced latency to the edge devices communicating with it and eliminates the need to manage the underlying infrastructure. In this book chapter, first, we introduce the novel concept of serverless edge computing, then, we analyze the performance of multiple serverless platforms, namely, OpenFaaS, AWS Greengrass, Apache OpenWhisk, when set up on the single-board computers (SBCs) on the edge and compare it with public cloud serverless offerings, namely, AWS Lambda and Azure Functions, to deduce the suitability of serverless architectures on the network edge. These serverless platforms are set up on a cluster of Raspberry Pis, and we evaluate their performance by simulating different types of edge workloads. The evaluation results show that OpenFaaS achieves the lowest response time on the SBC edge computing infrastructure, while serverless cloud offerings are the most reliable with the highest success rate.KeywordsIoTOpenFaaSApache OpenWhiskAWS lambdaAzure functionsAWS greengrassServerlessFaaSServerless edge computing

The increasing focus on sustainability has prompted organizations to adopt carbon-neutral solutions for IT infrastructure, including event pipelines. Serverless architectures have emerged as a promising approach, offering scalability, reduced operational costs, and minimal idle resources. This manuscript explores the potential of serverless event-driven pipelines for achieving carbon neutrality. It investigates the advantages, limitations, and environmental benefits, while presenting a simulation of event processing tasks on cloud-based platforms like AWS Lambda and Azure Functions. The results demonstrate the reduction in carbon emissions by leveraging auto-scaling serverless platforms, and a statistical analysis quantifies the trade-offs between cost, energy consumption, and latency.

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.

Cloud Computing is widely recognized as distributed computing paradigm for the next generation of dynamically scalable applications. Recently a novel service model, called Function-as-a-Service (FaaS), has been proposed, that enables users to exploit the computational power of cloud infrastructures, without the need to configure and manage complex computations systems. FaaS paradigm represents an opportunity to easily develop and execute extreme-scale applications as it allows fine-grain decomposition of the application with a much more efficient scheduling on cloud provider infrastructure.We introduce fly, a domain-specific language for designing, deploying and executing scientific computing applications by exploiting the FaaS service model on different cloud infrastructures. In this paper, we present the design and the language definition of fly on several computing (local and FaaS) back-ends: Symmetric multiprocessing (SMP), Amazon AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, and IBM Bluemix/Apache OpenWhisk. We also present the first fly source-to-source compiler, publicly available on GitHub, which supports SMP and AWS back-ends. KeywordsDomain-Specific LanguagesScientific computingParallel computingDistributed computingServerless computingFunctions as a Service (FaaS)

The evolution of mobile products leads to increased workload, geographical distribution of users, and complex requirements for security, availability, and speed of change delivery. In a cloud environment, the backend for mobile applications must ensure scalability without degrading the user experience, resilience to partial failures, and measurable quality control through observability. This article examines application patterns in cloud backend architecture: edge layer (CDN/WAF/API gateway), stateless services with autoscaling, caching and data management strategies, asynchronous processing via queues and events, and the design of SLOs and disaster recovery (DR) mechanisms. It is shown that increasing target availability levels leads to a nonlinear increase in cost and complexity, so engineering decisions should be based on a cascade of metrics (user scenarios ? SLOs ? engineering signals) and managed tradeoffs.

In the era of pervasive digital transformation, the need for real-time data processing and intelligent analytics has become paramount, especially in critical sectors like healthcare. This paper presents a novel serverless cloud-based architecture designed for efficient sensor data analytics in real-time healthcare environments. The proposed model integrates modular components including API gateways, serverless functions, stream buffering, and analytical engines to ensure scalability, fault tolerance, and cost-efficiency with higher throughput. Nature-inspired optimization technique is included to expedite data search in the server, for the same a comparative study has been conducted on a hybrid model of Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) it is used to augment data discovery in uninformed search environments. Experimental evaluations using AWS Lambda, S3, and API Gateway reveal the proposed model's superior performance in execution time, accuracy, and scalability under varying load conditions. This architecture demonstrates significant promise for healthcare applications requiring intelligent, responsive, and resource-efficient cloud solutions.

The great advance and affordability of technologies, communications and sensor technology has led to the generation of large amounts of data in the field of the Internet of Things and smart environments, as well as a great demand for smart applications and services adapted to the specific needs of each individual. This has entailed the need for systems capable of receiving, routing and processing large amounts of data to detect situations of interest with low latency, but despite the many existing works in recent years, studying highly scalable and low latency data processing systems is still necessary. In this area, the efficiency of complex event processing (CEP) technology is of particular significance and has been used in a variety of application scenarios. However, in most of these scenarios there is no performance evaluation to show how the system performs under various loads and therefore the developer is challenged to develop such CEP-based systems in new scenarios without knowing how the system will be able to handle different input data rates and address scalability and fault tolerance. This article aims to fill this gap by providing an evaluation of the various versions of one of the most reputable CEP engines—Esper CEP, as well as its integration with two renowned messaging brokers for data ingestion—RabbitMQ and Apache Kafka. For this purpose, we defined a benchmark with a series of event patterns with some of the most representative operators of the Esper CEP engine and we performed a series of tests with an increasing rate of input data to the system. We did this for three alternative software architectures: integrating open-source Esper and RabbitMQ, integrating one instance of Esper enterprise edition with Apache Kafka, and integrating two distributed instances of Esper enterprise edition with Apache Kafka. We measured the usage of CPU, RAM memory, latency and throughput time, looking for the data input rate with which the system overloads for each event pattern and we compared the results of the three proposed architectures. The results have shown a very low CPU consumption for all implementation options and input data rates; a balanced memory usage, quite similar among the three architectures, up to an input rate of 10,000 or 15,000 events per second, depending on the architecture and event pattern, and a quite efficient response time up to 10,000 or 15,000 events per second, depending on the architecture and event pattern. Based on a more exhaustive analysis of results, we have concluded that the different options offered by Esper for CEP provide very efficient solutions for real-time data processing, although each with its limitations in terms of brokers to be used for data integration, scalability, and fault tolerance; a number of suggestions have been drawn out for the developer to take as a basis for choosing which CEP engine and which messaging broker to use for the implementation depending on the of the system in question.

A mobile agent is a software program which migrates from a site to another site to perform tasks assigned by a user. For the mobile agent system to support agents in various application areas, the issues regarding reliable agent execution, as well as compatibility between two different agent systems or secure agent migration, have been considered. Some of the proposed schemes are either replicating the agents (Hamidi & Mohammadi, 2005) or check-pointing the agents (Park, Byun, Kim, & Yeom, 2002; Pleisch & Schiper, 2001;) For a single agent environment without considering inter-agent communication, the performance of the replication scheme and the check-pointing scheme is compared in Park et al. (2002) and Silva, Batista, and Silva (2000). In the area of mobile agents, only few works can be found relating to fault tolerance. Most of them refer to special agent systems or cover only some special aspects relating to mobile agents, such as the communication subsystem. Nevertheless, most people working with mobile agents consider fault tolerance to be an important issue (Izatt, Chan, & Brecht, 1999; Shiraishi, Enokido, & Takzawa, 2003). Mobile agents are becoming a major trend for designing distributed systems and applications in the last few years and foreseeable future. It can bring benefits such as reduced network load and overcoming of network latency (Chan, Won, & Lyu, 1993). Nevertheless, security is one of the limiting factors of the development of these systems. The main unsolved security problem lies in the possible existence of malicious hosts that can manipulate the execution and data of agents (Defago, Schiper, & Sergent, 1998). Most distributed applications we see today are deploying the client/server paradigm. There are certain problems with the client/server paradigm, such as the requirement of a high network bandwidth, and continuous user-computer interactivity. In view of the deficiencies of the client/server paradigm, the mobile code paradigm has been developed as an alternative approach for distributed application design. In the client/server paradigm, programs cannot move across different machines and must run on the machines they reside on. The mobile code paradigm, on the other hand, allows programs to be transferred among and executed on different computers. By allowing code to move between hosts, programs can interact on the same computer instead of over the network. Therefore, communication cost can be reduced. Besides, mobile agent (Fischer, Lynch, & Paterson, 1983) programs can be designed to work on behalf of users autonomously. This autonomy allows users to delegate their tasks to the mobile agents, and not to stay continuously in front of the computer terminal. The promises of the mobile code paradigm bring about active research in its realization. Most researchers, however, agree that security concerns are a hurdle (Greenberg, Byington, & Harper, 1998). In this article, we investigate these concerns. First, we review some of the foundation materials of the mobile code paradigm. We elaborate Ghezzi and Vigna’s classification of mobile code paradigms (Ghezzi & Vigna, 1997), which is a collection of the remote evaluation, code on demand, and mobile agent approaches. In the next section, we address the current status of mobile code security. The following section presents the model for fault-tolerant mobile agent. In the next section, security issues of the mobile agent are discussed, and we discuss security modeling and evaluation for the mobile agent in the section after. In the following section, simulation results and influence of the size of agent are discussed. We then conclude the article.