Cover Image

Dispatching Odyssey: Exploring Performance in Computing Clusters under Real-world Workloads

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yildiz, Mert, Rolich, Alexey, Baiocchi, Andrea
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

"Two-Stagification": Job Dispatching in Large-Scale Clusters via a Two-Stage Architecture
by: Yildiz, Mert, et al.
Published: (2025)

The Merit of Simple Policies: Buying Performance With Parallelism and System Architecture
by: Yildiz, Mert, et al.
Published: (2025)

Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption
by: Yildiz, Mert, et al.
Published: (2026)

BandPilot: Towards Performance- and Contention-Aware GPU Dispatching in AI Clusters
by: Zhang, Kunming, et al.
Published: (2025)

Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads
by: Zojer, Patrick, et al.
Published: (2026)

BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems
by: Wang, Yuxin, et al.
Published: (2024)

Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloads
by: Hidayetoglu, Mert, et al.
Published: (2025)

AI Surrogate Model for Distributed Computing Workloads
by: Park, David K., et al.
Published: (2024)

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters
by: Jain, Rutwik, et al.
Published: (2026)

Data Management System Analysis for Distributed Computing Workloads
by: Hsu, Kuan-Chieh, et al.
Published: (2025)

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
by: Iserte, Sergio, et al.
Published: (2025)

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
by: Jain, Rutwik, et al.
Published: (2024)

PRISM: Dynamic Primitive-Based Forecasting for Large-Scale GPU Cluster Workloads
by: Wu, Xin, et al.
Published: (2026)

Running Cloud-native Workloads on HPC with High-Performance Kubernetes
by: Chazapis, Antony, et al.
Published: (2024)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
by: Yu, Liangkun, et al.
Published: (2024)

Leveraging Hardware Performance Counters for Predicting Workload Interference in Vector Supercomputers
by: Shubham, et al.
Published: (2024)

Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads
by: Zhao, Wei, et al.
Published: (2024)

Crossword: Adaptive Consensus for Dynamic Data-Heavy Workloads
by: Hu, Guanzhou, et al.
Published: (2025)

A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors
by: Bappy, Faisal Haque, et al.
Published: (2023)

Workflow-Driven Modeling for the Compute Continuum: An Optimization Approach to Automated System and Workload Scheduling
by: Sharma, Aasish Kumar, et al.
Published: (2025)

Offloading Artificial Intelligence Workloads across the Computing Continuum by means of Active Storage Systems
by: Barceló, Alex, et al.
Published: (2025)

Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
by: Svedas, Jonas, et al.
Published: (2026)

Collaborative Resource Management and Workloads Scheduling in Cloud-Assisted Mobile Edge Computing across Timescales
by: Tang, Lujie, et al.
Published: (2024)

A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores
by: Hewage, Tharindu B., et al.
Published: (2024)

Agentic AI Workload Characteristics
by: Yuan, Yichao, et al.
Published: (2026)

Improving SpGEMM Performance Through Matrix Reordering and Cluster-wise Computation
by: Islam, Abdullah Al Raqibul, et al.
Published: (2025)

Workload Distribution with Rateless Encoding: A Low-Latency Computation Offloading Method within Edge Networks
by: Guo, Zhongfu, et al.
Published: (2023)

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
by: Hu, Jianmin, et al.
Published: (2025)

Ksurf: Attention Kalman Filter and Principal Component Analysis for Prediction under Highly Variable Cloud Workloads
by: Dang'ana, Michael, et al.
Published: (2024)

iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration
by: Guan, Wen, et al.
Published: (2025)

Odyssey: An End-to-End System for Pareto-Optimal Serverless Query Processing
by: Jesalpura, Shyam, et al.
Published: (2025)

Wave-Based Dispatch for Circuit Cutting in Hybrid HPC--Quantum Systems
by: García-Raigada, Ricard S., et al.
Published: (2026)

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads
by: Stoyanov, Radostin, et al.
Published: (2025)

Accelerating Compound LLM Training Workloads with Maestro
by: Yuan, Xiulong, et al.
Published: (2026)

Optimal Workload Placement on Multi-Instance GPUs
by: Turkkan, Bekir, et al.
Published: (2024)

Harpagon: Minimizing DNN Serving Cost via Efficient Dispatching, Scheduling and Splitting
by: Zhao, Zhixin, et al.
Published: (2024)

Intelligent Router for LLM Workloads: Improving Performance Through Workload-Aware Load Balancing
by: Jain, Kunal, et al.
Published: (2024)

Union: An Automatic Workload Manager for Accelerating Network Simulation
by: Wang, Xin, et al.
Published: (2024)

Distributed Load Balancing with Workload-Dependent Service Rates
by: Zhang, Wenxin, et al.
Published: (2024)

Eventually-Consistent Federated Scheduling for Data Center Workloads
by: Thiyyakat, Meghana, et al.
Published: (2023)