Cloud Computing and Resource Management

Deep reinforcement learning improves CPU frequency scaling for edge computing systems powered by renewable energy, reducing prediction error by 35% and optimizing the energy-latency tradeoff.

Study presents Replay-as-a-Service technique to reduce tail latency in storage-disaggregated OLTP databases by decoupling log replay from storage engine, achieving 40% latency reduction.

System offloads key-value caches to remote FPGA memory using CXL interconnects, achieving 3.2× throughput gains and 2.8× memory cost reduction for datacenter LLM serving.

AI-driven hybrid architecture for multi-cloud storage using telemetry-guided dynamic fragmentation, encryption, and distributed trust to resist data reconstruction attacks and provider breaches.

Enhanced optimization algorithm for cloud load balancing achieves 25% improvement in resource utilization and outperforms standard metaheuristic approaches in benchmark testing.

PAT optimizes LLM decode-phase attention by exploiting shared request prefixes and adaptive kernel tiling, reducing memory bandwidth bottlenecks in multi-request serving scenarios.

Middleware-enforced Timed Causal Consistency for Cassandra enables adaptive runtime consistency tuning without database modifications, with energy-performance-consistency trade-offs evaluated.

Study proposes queuing-based service model to optimize energy consumption and performance in ternary optical computers through threshold-based scheduling.

Framework for VM consolidation combining workload prediction and physics-constrained reinforcement learning. Achieves 23.2% energy reduction and 43.5% SLA violation reduction in cloud datacenters.

Deep reinforcement learning framework for adaptive task scheduling in edge-cloud computing with improved SLA compliance, reduced operational costs, and lower task rejection rates.

Modular assessment framework for automated evaluation of data center thermal and energy efficiency using standardized KPI calculations from monitoring systems and historical datasets.

AGENT framework improves task allocation in cloud systems using elitism-guided genetic algorithm with adaptive parameters, achieving 3-29% makespan improvements for heterogeneous VM scheduling.

Evaluation of Delta Lake architecture, transaction models, and optimization techniques for analytic workload performance and data reliability at scale.

Framework-aware query orchestration for Angular micro-frontends optimizes GraphQL data fetching through compile-time type safety and runtime deduplication, reducing API calls by 62% and improving.

Retrospective analysis of Docker's technical architecture, cross-platform expansion, and evolution toward modular standardization over a decade of development.

WaSC hardens WebAssembly sandboxes through system interface decoupling, achieving machine-level isolation while maintaining WASM performance advantages for serverless computing environments.

Quantum data centres overcome NISQ limitations through distributed quantum computing, leveraging entanglement orchestrators for dynamic network reconfiguration toward large-scale quantum internet.

A practical guide for integrating quantum software tools using MLIR infrastructure, demonstrated through a case study connecting PennyLane and Munich Quantum Toolkit.

Cloud-native ERM framework for multi-sector operations using microservices, cloud data management, and real-time analytics to enhance scalability, responsiveness, and cross-sector coordination.