433 research outputs found

    20th SC@RUG 2023 proceedings 2022-2023

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    Serverless Cloud Computing: A Comparative Analysis of Performance, Cost, and Developer Experiences in Container-Level Services

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    Serverless cloud computing is a subset of cloud computing considerably adopted to build modern web applications, while the underlying server and infrastructure management duties are abstracted from customers to the cloud vendors. In serverless computing, customers must pay for the runtime consumed by their services, but they are exempt from paying for the idle time. Prior to serverless containers, customers needed to provision, scale, and manage servers, which was a bottleneck for rapidly growing customer-facing applications where latency and scaling were a concern. The viability of adopting a serverless platform for a web application regarding performance, cost, and developer experiences is studied in this thesis. Three serverless container-level services are employed in this study from AWS and GCP. The services include GCP Cloud Run, GKE AutoPilot, and AWS EKS with AWS Fargate. Platform as a Service (PaaS) underpins the former, and Container as a Service (CaaS) the remainder. A single-page web application was created to perform incremental and spike load tests on those services to assess the performance differences. Furthermore, the cost differences are compared and analyzed. Lastly, the final element considered while evaluating the developer experiences is the complexity of using the services during the project implementation. Based on the results of this research, it was determined that PaaS-based solutions are a high-performing, affordable alternative for CaaS-based solutions in circumstances where high levels of traffic are periodically anticipated, but sporadic latency is never a concern. Given that this study has limitations, the author recommends additional research to strengthen it

    20th SC@RUG 2023 proceedings 2022-2023

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    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Comparative Analysis of Apache 2 Performance in Docker Containers vs Native Environment

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    Web servers have become crucial to facilitate access to and distribute such content on the internet. In this case, Docker containerization technology offers a solution. Docker allows developers to package applications and dependencies in one container, making deploying web servers faster and easier. But with these features, is there any performance that must be sacrificed if we choose to use docker in our web server deployment process. We will look at how much performance will be sacrificed. However, we must thoroughly analyze how Apache2 performs when running in a Docker container compared to running natively. That's why we're conducting a study to compare the performance of Apache2 in a Docker container versus a native environment using experimental methods. For this study, we'll use the Apache bench tool to test Apache2's performance in both environments. By experimenting, it should become clear how the performance of Docker containers compares to native environments when developing web servers. The research shows that Apache2 performance on native hosts is about 5-10% better than in a docker environment in handling small request loads. The better performance here refers to the parameters we tested: total time results, requests per second, and transfer speed. The request load variation can differ depending on the server specification itself. Although Docker offers features in terms of application isolation and scalability, our results show that running Apache2 natively is more efficient without changing its default configuration. The additional overhead Docker can be required to run the docker system in isolating the application; in this case, the virtualization layer is required to run Apache2 inside a Docker container. This can affect application performance and cause a slight performance degradation compared to using the host operating system directly. This research aims to inform developers about the performance difference between apache2 in Docker and the native environment. It will help them make informed decisions about deployment environments. Docker offers appealing features, but its performance may need to improve.  Test results show that the native host performs better, although its feature set is not as extensive as that of Docker

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

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    The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics

    Analyzing Data-center Application Performance Via Constraint-based Models

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    Hyperscale Data Centers (HDCs) are the largest distributed computing machines ever constructed. They serve as the backbone for many popular applications, such as YouTube, Netflix, Meta, and Airbnb, which involve millions of users and generate billions in revenue. As the networking infrastructure plays a pivotal role in determining the performance of HDC applications, understanding and optimizing their networking performance is critical. This thesis proposes and evaluates a constraint-based approach to characterize the networking performance of HDC applications. Through extensive evaluations conducted in both controlled settings and real-world case studies within a production HDC, I demonstrated the effectiveness of the constraint-based approach in handling the immense volume of performance data in HDCs, achieving tremendous dimension reduction, and providing very useful interpretability.Doctor of Philosoph
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