Titelaufnahme

Titel
Survey and taxonomy of autonomic large-scale computing / von Gabriel Kittel
VerfasserKittel, Gabriel
Begutachter / BegutachterinDustdar, Schahram
Erschienen2010
Umfang165 S. : Ill., graph. Darst.
HochschulschriftWien, Techn. Univ., Dipl.-Arb., 2010
Anmerkung
Zsfassung in dt. Sprache
SpracheEnglisch
DokumenttypDiplomarbeit
Schlagwörter (DE)Cloud-Computing / Grid-Computing / Autonomic Computing / Utility-Computing / Ressourcenmanagement / Scheduling / Dienstgüte / Serviceorientierte Architekturen
Schlagwörter (EN)cloud computing / grid computing / autonomic computing / utility computing / resource management / scheduling / quality of service / service oriented architectures / self-governing systems
URNurn:nbn:at:at-ubtuw:1-40692 Persistent Identifier (URN)
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
Dateien
Survey and taxonomy of autonomic large-scale computing [3.87 mb]
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Zusammenfassung (Englisch)

In the area of distributed systems, several approaches have emerged with the objective to deliver computing power as a public utility like water, gas, electricity and telephony. Cloud computing is the latest of those approaches where virtual computing infrastructure, software development platforms and applications are provisioned on demand over the Internet. Autonomic computing is a computing paradigm that promises to deliver systems adapting themselves to environmental changes by employing self-management mechanisms guided by policies as an effort to address the management complexity that arises from the dynamics of resource availability and system load in large-scale computing systems. The Foundations of Self-governing ICT Infrastructures project (FoSII) at TU Vienna intends to enhance self-management support in existing service-oriented architectures. A survey of existing projects that introduce autonomic computing to large-scale computing systems like grids and clouds, and a taxonomy that provides classification criteria for that research field would allow to assess the current state of research by suggesting criteria to help identify application areas, subproblems and approaches within that field.

However, such a survey and taxonomy have not yet been proposed to this day. The goal of this thesis is to systematically investigate the state of art of self-management by providing a taxonomy of autonomic large-scale computing. It presents a survey of projects and theoretical work in that field and proposes a taxonomy that classifies autonomic large-scale computing. Survey and taxonomy allow to assess the current state of research in autonomic large-scale distributed systems, thus supporting further advancements in the field of autonomic large-scale distributed computing.