Sökning: "Performance Analytics"

Visar resultat 1 - 5 av 14 avhandlingar innehållade orden Performance Analytics.

  1. 1. Performance anomaly detection and resolution for autonomous clouds

    Detta är en avhandling från Umeå : Umeå University

    Författare :Olumuyiwa Ibidunmoye; Umeå universitet.; [2017]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Cloud Computing; Distributed Systems; Performance Management; Anomaly Detection; Quality of Service; Performance Analytics; Machine Learning; Computer Systems; datorteknik; Computing Science; administrativ databehandling; Computer Science; datalogi;

    Sammanfattning : Fundamental properties of cloud computing such as resource sharing and on-demand self-servicing is driving a growing adoption of the cloud for hosting both legacy and new application services. A consequence of this growth is that the increasing scale and complexity of the underlying cloud infrastructure as well as the fluctuating service workloads is inducing performance incidents at a higher frequency than ever before with far-reaching impact on revenue, reliability, and reputation. LÄS MER

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

    Detta är en avhandling från Stockholm : KTH Royal Institute of Technology

    Författare :Ahsan Javed Awan; KTH.; [2017]
    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

  3. 3. Performance Optimization Techniques and Tools for Data-Intensive Computation Platforms An Overview of Performance Limitations in Big Data Systems and Proposed Optimizations

    Detta är en avhandling från Stockholm : KTH Royal Institute of Technology

    Författare :Vasiliki Kalavri; KTH.; [2014]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; performance optimization; data-intensive computing; big data; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : Big data processing has recently gained a lot of attention both from academia and industry. The term refers to tools, methods, techniques and frameworks built to collect, store, process and analyze massive amounts of data. Big data can be structured, unstructured or semi-structured. LÄS MER

  4. 4. Improving Computer Communication Performance by Reducing Memory Bandwidth Consumption

    Detta är en avhandling från Department of Computer Systems, Uppsala University

    Författare :Bengt Ahlgren; Uppsala universitet.; Networks and Analytics lab. Decisions; [1997]
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Computer Systems; Datorteknik;

    Sammanfattning : .... LÄS MER

  5. 5. Architecture and Applications of a Geovisual Analytics Framework

    Detta är en avhandling från Linköping : Linköping University Electronic Press

    Författare :Quan Ho; Linköpings universitet.; Linköpings universitet.; [2013]
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; GeoVisual Analytics; toolkits; frameworks; web-enabled; visualization; interactive visualizations; interaction; interaction techniques; visual data analysis; component architecture; storytelling;

    Sammanfattning : The large and ever-increasing amounts of multi-dimensional, multivariate, multi-source, spatio-temporal data represent a major challenge for the future. The need to analyse and make decisions based on these data streams, often in time-critical situations, demands integrated, automatic and sophisticated interactive tools that aid the user to manage, process, visualize and interact with large data spaces. LÄS MER