Sökning: "datorsystem"

Visar resultat 1 - 5 av 624 avhandlingar innehållade ordet datorsystem.

  1. 1. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning

    Författare :Zainab Abbas; Vladimir Vlassov; Peter Van Roy; Paris Carbone; Vasiliki Kalavri; Vincenzo Massimiliano Gulisano; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Stream processing; graph processing; time series; big data; machine learning; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : 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. LÄS MER

  3. 2. Self-Management for Large-Scale Distributed Systems

    Författare :Ahmad Al-Shishtawy; Vlassov Vlassov; Seif Haridi; Per Brand; Leandro Navarro Moldes; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Self-Management; Autonomic Computing; Control Theory; Distributed Systems; Grid Computing; Cloud Computing; Elastic Services; Key-Value Stores; SRA - ICT; SRA - Informations- och kommunikationsteknik;

    Sammanfattning : 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. LÄS MER

  4. 3. Programming Model and Protocols for Reconfigurable Distributed Systems

    Författare :Cosmin Ionel Arad; Seif Haridi; Gregory Chockler; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; distributed systems; programming model; message-passing concurrency; nested hierarchical composition; reactive components; software architecture; dynamic reconfiguration; multi-core; discrete-event simulation; peer-to-peer; testing; debugging; distributed key-value stores; data replication; consistency; linearizability; network partition tolerance; consistent hashing; self-organization; scalability; elasticity; fault tolerance; consistent quorums;

    Sammanfattning : 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. LÄS MER

  5. 4. On the Performance Analysis of Large Scale, Dynamic, Distributed and Parallel Systems

    Författare :John Ardelius; Seif Haridi; Supriya Krishnamurthy; Mark Jelasity; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Performance analysis distributed systems; SRA - ICT; SRA - Informations- och kommunikationsteknik;

    Sammanfattning : 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. LÄS MER

  7. 5. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server

    Författare :Ahsan Javed Awan; Eduard Ayguade; Mats Brorsson; Vladimir Vlassov; Lieven Eeckhout; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Workload Characterization; Big Data Analytics; Multicore Performance; Apache Spark; Near Data Processing; NUMA; Hyperthreading; Prefetchers; Coherently attached accelerators; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : 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. LÄS MER