Implementing Serverless Architectures for Ultra-Low Latency Data Pipelines in Multiplayer Gaming Environments

In this paper, scalability and performance of AWS Lambda are discussed with emphasis on serverless technologies. It delves into best practices for efficient resource management, cost optimization, and code execution. Through detailed case studies, it provides real world examples and potential issues like cold start latency and restrictions in the amount of time versus CPU cores available for execution. It also divulges an analysis of solutions to some of the evident problems and enables developers and organizations intending to use AWS Lambda for developing versatile and high-performance applications. Altogether, this research provides directions for improving the serverless infrastructure [1].

The article provides a comprehensive analysis of architectural patterns for serverless applications in the context of modern cloud platforms. The study covers three key patterns: Strangler Fig for gradual migration of monolithic systems, Event-Driven for asynchronous event processing, and Saga for distributed transaction management. A detailed comparative analysis of the patterns is presented according to the criteria of complexity of implementation, type of interaction, areas of application and potential risks. Particular attention is paid to the practical aspects of implementing a serverless architecture, including cold start issues, function execution time constraints, and distributed system monitoring issues. Based on the analysis of modern FaaS platforms (AWS Lambda, Azure Functions, Google Cloud Functions), recommendations have been formulated for choosing the optimal architectural approach depending on business requirements and load characteristics. The results of the study demonstrate that choosing the right architectural pattern can significantly increase the scalability, fault tolerance, and cost-effectiveness of cloud applications.

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.

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.

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.

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

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.

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)

Discrete time stochastic multi-player competitive games with affine payoffs

Combat arenas are a popular theme in multiplayer games, because they create extremely compelling gameplay from very simple ingredients. This can often just be an environment filled with weapons that the players can use to wipe each other out. The game that we're going to create in this chapter is exactly that, with futuristic battle tanks. Although games like this are relatively easy to make, care must be taken in their design to ensure that both players feel they are being treated fairly. We'll discuss this more in Chapter 11 .

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.

Mate value is tied to appearance and age in women, and social status and wealth in men. Theory surrounding self-perceived mate value suggests that mate value shows relatively little situational plasticity. Here we challenge this concept by asking participants to compete in video games to test (a) whether self-perceived and actual performance affects subjective mate value, and (b) whether subjective mate value affects mate preferences. By randomly allocating participants to play either a violent or nonviolent game in three separate experiments, we show that both men and women who played a violent game had significantly lower self-perceived performance, and as a result, reported a lower self-perceived mate value. We also demonstrate that this effect led to different preferences for short-term mating partners, mainly among women. Our results strongly suggest that mate value in humans is situationally dynamic and responds to recent contest experiences, as it does in nonhumans. We discuss our results with reference to online multiplayer digital gaming experiences and how variation in self-perceived mate value affects mating market dynamics. (PsycInfo Database Record (c) 2021 APA, all rights reserved) Public Significance Statement—People’s perceptions of their own quality as romantic partners (i.e., their mate value) often depends on physical attractiveness for women or social status for men. Here we show that self-perceived mate value is not a fixed trait but varies according to subjective performance in a competitive video game. Compared with people who played nonviolent video games, the men and women that played a violent video game believed they performed more poorly and rated themselves as having a relatively lower mate value. These experiments provide evidence that people’s perception of their quality as romantic partners varies according to the social experiences they have and their performance in online contests. (PsycInfo Database Record (c) 2021 APA, all rights reserved)