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
ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments
Sukhpal Singh Gill,Sukhpal Singh Gill,Shreshth Tuli,Adel Nadjaran Toosi,Felix Cuadrado,Peter Garraghan,Rami Bahsoon,Hanan Lutfiyya,Rizos Sakellariou,Omer Rana,Schahram Dustdar,Rajkumar Buyya +11 more
TL;DR: The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.
Book ChapterDOI
A Market-Based Scheduler for JXTA-Based Peer-to-Peer Computing System
TL;DR: Peer-to-Peer (P2P) computing is said to be the next wave of computing after client-server and web-based computing and a flexible and efficient job scheduling is needed to harvest the idle computing power as cheaply and economically as possible.
Proceedings ArticleDOI
Progressive Search Algorithm for Service Discovery in an IoT Ecosystem
Santosh Pattar,Dwaraka S Kulkarni,Darshil Vala,Rajkumar Buyya,Venugopal K R,S. Sitharama Iyengar,Lalit M. Patnaik +6 more
TL;DR: A Progressive Search Algorithm (ProSA) is proposed that maps the user's requirements to the attributes of the IoT resources and smart services and thereby provide personalized search results and thereby establish the practicability of the proposed search algorithm.
Posted Content
A Grid Service Broker for Scheduling Distributed Data-Oriented Applications on Global Grids
TL;DR: A Grid broker that mediates access to distributed resources by discovering suitable data sources for a given analysis scenario, and optimally mapping analysis jobs to resources is proposed and developed.
Journal Article
A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition
TL;DR: A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA -II) to get the better convergence and diversity.