Rajkummar Buyya

Future Generation Computer Systems

Future Generation Computer Systems

Darpan: A Digital Twin Framework for the Next-Generation Computing Continuum

Computing-continuum applications distribute work across devices, edge systems, fog resources, and clouds. While a placement, scheduling, or recovery decision is being made, resource availability, network conditions, and application progress may change, so the decision can be invalid by the time it is executed. Existing...

Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments

A double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS) that enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling.

Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions

This survey provides a systematic taxonomy of recent research on rollout efficiency for reasoning-oriented reinforcement learning, classifying existing approaches from both mechanism and bottleneck perspectives.

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.

AgentR A Stateful and Recovery-Aware Software Architecture for LLM-based Auditable Workflows

A stateful architecture for LLM workflow systems that enables improved observability, recoverability, and operational accountability in LLM workflow systems, and preliminary proof-of-concept that persistent state machine design, asynchronous orchestration, and cost-aware usage logging can enable improved observability,...

CLASP: Chained-Request-Aware Scaling and Operator Placement for Serverless Stream Processing

CLASP, a scaling and scheduling strategy for stream processing in stateful serverless environments, which improves throughput by up to 3.3x and reduces median end-to-end latency by up to 76% compared with state-of-the-art scaling strategies.

Optimizing resource allocation for federated LLM training via workload forecasting and autoscaling in edge environments

An integrated framework that combines LLM fine-tuning using federated adaptive local low-rank adaptation (FedALoRA) with forecasting-driven autoscaling policies is proposed, making it well-suited for federated-LLM Autoscaling in dynamic edge environments.

Trust-Aware Topology Learning for Dynamic Decentralized Federated Learning under Adversaries

DMTT (Dynamic MURMURA with Trusted Topology), a decentralized personalized FL protocol built on MURMURA, which uses evidential deep learning to down-weight distribution-mismatched peers, extended here to time-varying graphs under topology-manipulation attacks.

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...

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.