R
Rajkumar Buyya
Researcher at University of Melbourne
Publications - 1143
Citations - 108162
Rajkumar Buyya is an academic researcher from University of Melbourne. The author has contributed to research in topics: Cloud computing & Grid computing. The author has an hindex of 133, co-authored 1066 publications receiving 95164 citations. Previous affiliations of Rajkumar Buyya include Walter and Eliza Hall Institute of Medical Research & Infosys.
Papers
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Book ChapterDOI
Principles of Parallel and Distributed Computing
TL;DR: This chapter presents the fundamental principles of parallel and distributed computing and discusses models and conceptual frameworks that serve as foundations for building cloud computing systems and applications.
Book ChapterDOI
Virtual Networking with Azure for Hybrid Cloud Computing in Aneka
TL;DR: This work explains how Aneka resource provisioning module is extended to support Azure Resource Manger (ARM) application programming interfaces (APIs) and walks through the process of establishment of an Azure Point-to-Site VPN to provide connectivity between AneKA nodes in the hybrid cloud environment.
Journal ArticleDOI
Cloud scalability: building the Millennium Falcon
TL;DR: This special issue covers some of the most relevant trends on scaling cloud infrastructures and platforms and describes the strategies that researchers are working on to improve the scalability of clouds.
Proceedings ArticleDOI
Market-oriented cloud computing: Opportunities and challenges
TL;DR: This keynote talk will cover the 21st century vision of computing and identifies various IT paradigms promising to deliver the vision of Computing utilities, the architecture for creating market-oriented Clouds by leveraging technologies such as VMs, and market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation.
Proceedings ArticleDOI
DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning
TL;DR: The approach embeds a debt-aware two level constraint reasoning algorithm in DATESSO to improve the efficiency, effectiveness and sustainability of self-adaptive service composition.