:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Ping, Su, Lei, Yang, Jinjie, Chen, Xin
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Online Access:	https://arxiv.org/abs/2411.11560
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Duration-Informed Workload Scheduler
by: Loreti, Daniela, et al.
Published: (2026)

HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
by: Agyemang, Justice Owusu, et al.
Published: (2026)

Workload Schedulers -- Genesis, Algorithms and Differences
by: Sliwko, Leszek, et al.
Published: (2025)

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
by: Liu, Ziyue, et al.
Published: (2026)

Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
by: Liang, Feng, et al.
Published: (2024)

Power- and Fragmentation-aware Online Scheduling for GPU Datacenters
by: Lettich, Francesco, et al.
Published: (2024)

SparOA: Sparse and Operator-aware Hybrid Scheduling for Edge DNN Inference
by: Zhang, Ziyang, et al.
Published: (2025)

An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
by: Dong, Hang, et al.
Published: (2024)

Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads
by: Wilkins, Grant, et al.
Published: (2024)

Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference
by: Siavashi, Mohammad, et al.
Published: (2025)

TCM-Serve: Modality-aware Scheduling for Multimodal Large Language Model Inference
by: Papaioannou, Konstantinos, et al.
Published: (2026)

CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing
by: Hu, Yujiao, et al.
Published: (2024)

TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
by: Stojkovic, Jovan, et al.
Published: (2025)

PecSched: Preemptive and Efficient Cluster Scheduling for LLM Inference
by: Zhang, Zeyu, et al.
Published: (2024)

LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications
by: Zhu, Botao, et al.
Published: (2025)

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
by: Saad, Zainab, et al.
Published: (2025)

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters
by: Dongare, Shruti, et al.
Published: (2025)

Tesserae: Scalable Placement Policies for Deep Learning Workloads
by: Bian, Song, et al.
Published: (2025)

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

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling
by: Da, Wei, et al.
Published: (2025)

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
by: Jadhav, Prachi, et al.
Published: (2025)

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving
by: Hsieh, Chia-chi, et al.
Published: (2026)

Equinox: Holistic Fair Scheduling in Serving Large Language Models
by: Wei, Zhixiang, et al.
Published: (2025)

Quantifying Energy and Cost Benefits of Hybrid Edge Cloud: Analysis of Traditional and Agentic Workloads
by: Alamouti, Siavash
Published: (2025)

InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling
by: Jiang, Xiaoxiao, et al.
Published: (2025)

Mixture-of-Schedulers: An Adaptive Scheduling Agent as a Learned Router for Expert Policies
by: Wang, Xinbo, et al.
Published: (2025)

Justitia: Fair and Efficient Scheduling of Task-parallel LLM Agents with Selective Pampering
by: Yang, Mingyan, et al.
Published: (2025)

Prompt-Aware Scheduling for Low-Latency LLM Serving
by: Tao, Yiheng, et al.
Published: (2025)

T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
by: Wei, Jianyu, et al.
Published: (2024)

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
by: Wang, Haodong, et al.
Published: (2025)

Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows
by: Yang, Yuting, et al.
Published: (2024)

Seesaw: High-throughput LLM Inference via Model Re-sharding
by: Su, Qidong, et al.
Published: (2025)

A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
by: Wu, Qi, et al.
Published: (2026)

Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
by: Wang, Zhibin, et al.
Published: (2025)

IsoSched: Preemptive Tile Cascaded Scheduling of Multi-DNN via Subgraph Isomorphism
by: Zhao, Boran, et al.
Published: (2025)

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
by: Li, Xiangchen, et al.
Published: (2025)

EWSJF: An Adaptive Scheduler with Hybrid Partitioning for Mixed-Workload LLM Inference
by: Sidik, Bronislav, et al.
Published: (2026)

Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
by: Li, Yan, et al.
Published: (2025)

Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments
by: Patel, Chinkit, et al.
Published: (2025)

ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor
by: Choi, Seungbeom, et al.
Published: (2025)