Search for dissertations about: "Datorsystem"
Showing result 1 - 5 of 618 swedish dissertations containing the word Datorsystem.
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1. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning
Abstract : Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. READ MORE
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2. Self-Management for Large-Scale Distributed Systems
Abstract : Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. READ MORE
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3. Programming Model and Protocols for Reconfigurable Distributed Systems
Abstract : Distributed systems are everywhere. From large datacenters to mobile devices, an ever richer assortment of applications and services relies on distributed systems, infrastructure, and protocols. Despite their ubiquity, testing and debugging distributed systems remains notoriously hard. READ MORE
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4. On the Performance Analysis of Large Scale, Dynamic, Distributed and Parallel Systems
Abstract : Evaluating the performance of large distributed applications is an important and non-trivial task. With the onset of Internet wide applications there is an increasing need to quantify reliability, dependability and performance of these systems, both as a guide in system design as well as a means to understand the fundamental properties of large-scale distributed systems. READ MORE
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5. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server
Abstract : The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. READ MORE