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Author

Rajkummar Buyya

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#edge computing Review Open access Aug 2026

Artificial intelligence-driven resource management in edge-cloud computing continuum for internet of things applications: new trends and future directions

This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: the transition from centralized to distributed and collaborative intelligence, cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and t...

Zhi-Yu Wang, Nilotpal Kapri, L. Bittencourt et al. · 1 citation
Preprint Aug 2026

PrefixPlace: Provable Prefix Key-Value Placement for Large Language Model Serving under Heterogeneous Compute and Transfer Costs

An epoch-level planner, PrefixPlace, which assigns prefix-complete targets under memory budgets and profiled demand, compute, and transfer costs, and solves a 50,000-node, 16-worker placement in 12.3 s on one processor, enabling timely replanning.

Zhi-Yu Wang, Rajkummar Buyya · 0 citations
Conference Open access Oct 2025

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud–Edge Continuum

REACH is proposed, a reinforcement learning-based microservice rescheduling framework that enables a sim-to-real deployment pipeline for adapting microservice placement under fluctuating resource availability and performance variations across distributed infrastructures.

Xu Bai, Muhammed Tawfiqul Islam, Rajkummar Buyya et al. · 0 citations
Preprint Aug 2026

How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits

Late fusion is an efficient, noise-robust, self-characterizing alternative to reconstruction for circuit-cutting QML and claims advantage over classical machine learning - consistent with recent benchmarking, quantum offers no accuracy edge on these datasets.

Prabhjot Singh, A. Toosi, Rajkummar Buyya · 0 citations
Preprint Aug 2026

Preserving Admission Responsibility in Multi-Tenant Large Language Model Prefix Caches

Results show that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure, which shows that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure.

Zhi-Yu Wang, Rajkummar Buyya · 2 citations
Open access Aug 2026

CELLServe: An SLO-Aware and Cost Efficient LLMs Serving System for Serverless Computing Environments

CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.

Zejian Wang, Nan Lin, Zinuo Cai et al. · 0 citations

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