Image de couverture de livre

Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bai, Zeyu
Format:	Preprint
Publié:	2026
Sujets:	Distributed, Parallel, and Cluster Computing Databases Machine Learning Performance Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Balancing Fairness and Performance in Multi-User Spark Workloads with Dynamic Scheduling (extended version)
par: Kažemaks, Dāvis, et autres
Publié: (2025)

FluxSieve: Unifying Streaming and Analytical Data Planes for Scalable Cloud Observability
par: Vogel, Adriano, et autres
Publié: (2026)

Execution time budget assignment for mixed criticality systems
par: Khelassi, Mohamed Amine, et autres
Publié: (2023)

An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
par: De Silva, Suvi, et autres
Publié: (2026)

Workload composition smooths aggregate power demand while sustaining short-horizon ramps in AI data centers
par: Majumder, Subir, et autres
Publié: (2026)

Visual Insights into Agentic Optimization of Pervasive Stream Processing Services
par: Sedlak, Boris, et autres
Publié: (2026)

AdapTBF: Decentralized Bandwidth Control via Adaptive Token Borrowing for HPC Storage
par: Rashid, Md Hasanur, et autres
Publié: (2026)

Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms
par: Taufique, Zain, et autres
Publié: (2023)

Equilibrium in the Computing Continuum through Active Inference
par: Sedlak, Boris, et autres
Publié: (2023)

AARC: Automated Affinity-aware Resource Configuration for Serverless Workflows
par: Jin, Lingxiao, et autres
Publié: (2025)

SP-IMPact: A Framework for Static Partitioning Interference Mitigation and Performance Analysis
par: Costa, Diogo, et autres
Publié: (2025)

Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS
par: Pradeep, Preethika, et autres
Publié: (2025)

Less is More: Optimizing Function Calling for LLM Execution on Edge Devices
par: Paramanayakam, Varatheepan, et autres
Publié: (2024)

Terabyte-Scale Analytics in the Blink of an Eye
par: Wu, Bowen, et autres
Publié: (2025)

Kairos: Efficient Temporal Graph Analytics on a Single Machine
par: da Trindade, Joana M. F., et autres
Publié: (2024)

Mayura: Exploiting Similarities in Motifs for Temporal Co-Mining
par: Singapuram, Sanjay Sri Vallabh, et autres
Publié: (2025)

Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
par: Li, Zhuojin, et autres
Publié: (2025)

Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
par: Paramanayakam, Varatheepan, et autres
Publié: (2025)

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
par: Sridharan, Srinivas, et autres
Publié: (2026)

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
par: Xu, Guanyu, et autres
Publié: (2025)

Evaluating Asynchronous Semantics in Trace-Discovered Resilience Models: A Case Study on the OpenTelemetry Demo
par: Krasnovsky, Anatoly A.
Publié: (2025)

Distributed Record Linkage in Healthcare Data with Apache Spark
par: Heydari, Mohammad, et autres
Publié: (2024)

GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programs
par: Chu, Ruifan, et autres
Publié: (2025)

Scalable Time-Series Causal Discovery with Approximate Causal Ordering
par: Jiao, Ziyang, et autres
Publié: (2024)

Setchain Algorithms for Blockchain Scalability
par: Karmegam, Arivarasan, et autres
Publié: (2025)

GPU Cluster Scheduling for Network-Sensitive Deep Learning
par: Sharma, Aakash, et autres
Publié: (2024)

cedar: Optimized and Unified Machine Learning Input Data Pipelines
par: Zhao, Mark, et autres
Publié: (2024)

Vector-Centric Machine Learning Systems: A Cross-Stack Approach
par: Jiang, Wenqi
Publié: (2025)

When Less is More: Achieving Faster Convergence in Distributed Edge Machine Learning
par: Basani, Advik Raj, et autres
Publié: (2024)

Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
par: Lin, Zhongyi, et autres
Publié: (2024)

ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition
par: Helal, Ahmed E., et autres
Publié: (2025)

Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation
par: Qin, Jianxing, et autres
Publié: (2025)

xMem: A CPU-Based Approach for Accurate Estimation of GPU Memory in Deep Learning Training Workloads
par: Shi, Jiabo, et autres
Publié: (2025)

Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications
par: Suri, Raunaq, et autres
Publié: (2025)

Emergence-as-Code for Self-Governing Reliable Systems
par: Krasnovsky, Anatoly A.
Publié: (2026)

ARCAS: Adaptive Runtime System for Chiplet-Aware Scheduling
par: Fogli, Alessandro, et autres
Publié: (2025)

Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona
par: Colangelo, Philip, et autres
Publié: (2025)

Design and Implementation of an IoT Cluster with Raspberry Pi Powered by Solar Energy: A Theoretical Approach
par: Portillo, Noel
Publié: (2025)

Mathematical Foundations of Modeling ETL Process Chains
par: Maier, Levin, et autres
Publié: (2026)

EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
par: Merkel, Nikolai, et autres
Publié: (2026)