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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Journal ArticleDOI
Software-Defined Security-by-Contract for Blockchain-enabled MUD-aware Industrial IoT Edge Networks
TL;DR: The integrated framework combines Blockchains and SxC security contracts, MUD-based behavioral fingerprinting, and Software-Defined-Networking (SDN) for managing the security of IIoT ecosystems is proposed.
Proceedings ArticleDOI
MELODY-JOIN: Efficient Earth Mover's Distance similarity joins using MapReduce
TL;DR: A novel framework, named MELODY-JOIN, is proposed, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these E MD lower bounds has a constant complexity.
Book ChapterDOI
An Introduction to the InfiniBand Architecture
TL;DR: The InfiniBand Architecture (IBA) as discussed by the authors is a new industry standard architecture for server I/O and inter-server communication, which was developed by IBM to provide the levels of reliability, availability, performance, and scalability necessary for present and future server systems.
Journal ArticleDOI
Secure Healthcare Monitoring Sensor Cloud With Attribute-Based Elliptical Curve Cryptography
TL;DR: In this article, a security mechanism called attribute-based elliptical curve cryptography (ABECC) is proposed that guarantees data integrity, data confidentiality, and fine-grained access control.
Posted Content
Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations
Qiheng Zhou,Minxian Xu,Sukhpal Singh Gill,Chengxi Gao,Wenhong Tian,Cheng-Zhong Xu,Rajkumar Buyya +6 more
TL;DR: This paper compares several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics, and implements and evaluates these algorithms with the same experimental settings in CloudSim toolkit.