Rajkumar Buyya

About

Dr.Buyya has advanced the discipline of computer science and engineering through my contributions to Grid and Cloud computing in the following ways: 1. Carried out fundamental research in distributed resource management and application scheduling in global Grids. 2. Pioneered the principles of computational economy as well as techniques and mechanisms that enable the delivery of Grid and Cloud services as utility-like services, 3. Invented several adaptive scheduling algorithms for deploying applications on global Grids optimizing resource usage and meeting users’ quality of service (QoS) requirements. 4. Co-developed fundamental Grid and Cloud technologies that enable the creation of scalable computing environments as well as support rapid development of applications. 5. Applied Grid and Cloud technologies to several domains in collaboration with scientists and deployed them on both national and international Cyber-infrastructures.

Employment

• University of Melbourne Professor

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (377)

• High Availability Ultra-Large Cloud-Oriented Storage Systems: A Review, Mind Mapping and Open Research Directions
  ACM Computing Surveys 2026 DOI: 10.1145/3831665
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• Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
  ACM Computing Surveys 2026 DOI: 10.1145/3821534
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• CELL Serve: An SLO-Aware and C ost E fficient LL Ms Serving System for Serverless Computing Environments
  ACM Transactions on Architecture and Code Optimization 2026 DOI: 10.1145/3830086
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• Energy-Efficient Resource Management in Microservices-Based Fog and Edge Computing: State-of-the-Art and Future Directions
  ACM Computing Surveys 2026 DOI: 10.1145/3797911
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• MMK: A Hybrid Scheduling Framework for Fine-Grained GPU Sharing for Deep Learning Applications
  ACM Transactions on Architecture and Code Optimization 2026 DOI: 10.1145/3820163
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• Security in the Fine‐Tuning Lifecycle of Large Language Models: Threats, Defenses, Evaluation, and Future Directions
  Software: Practice and Experience 2026 DOI: 10.1002/spe.70098
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• A Knowledge Distillation-Empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
  IEEE Transactions on Mobile Computing 2026 DOI: 10.1109/TMC.2026.3672603
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• FaaShare: Enabling Generic Low-Latency and SLO-Aware GPU Sharing in Serverless Computing
  IEEE Transactions on Parallel and Distributed Systems 2026 DOI: 10.1109/TPDS.2026.3704608
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• Skewness-Aware Stream Partitioning: A Key Splitting Suppression Method for Distributed Stream Processing
  IEEE Transactions on Services Computing 2026 DOI: 10.1109/TSC.2026.3707515
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• Service Colonies: A Novel Architectural Style for Developing Software Systems with Autonomous and Cooperative Services
  ACM Transactions on Software Engineering and Methodology 2026 DOI: 10.1145/3821209
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