Sökning: "Performance Analytics"
Visar resultat 1 - 5 av 22 avhandlingar innehållade orden Performance Analytics.
- Detta är en avhandling från Umeå : Umeå University
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
- Detta är en avhandling från KTH Royal Institute of Technology
Sammanfattning : The advent of 5th generation of mobile networks (5G) will introduce new challenges for the infrastructure providers (InPs). One of the major challenges is to provide a common platform for supporting a large variety of services. LÄS MER
- Detta är en avhandling från Stockholm : KTH Royal Institute of 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
4. Performance Optimization Techniques and Tools for Data-Intensive Computation Platforms An Overview of Performance Limitations in Big Data Systems and Proposed OptimizationsDetta är en avhandling från Stockholm : KTH Royal Institute of 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
- Detta är en avhandling från Department of Computer Systems, Uppsala University
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