iScheduler is a reinforcement‑learning‑driven framework that tackles large‑scale Resource Investment Problems (RIP) by modeling them as a Markov decision process over decomposed subproblems and building schedules through sequential process selection. The approach speeds up optimization and allows efficient reconfiguration by reusing unchanged process schedules and only rescheduling affected processes. Using the new L‑RIPLIB benchmark, iScheduler achieves competitive resource costs while cutting time to feasibility by up to 43× compared to leading solver‑backed baselines.
By Yi-Xiang Hu, Yuke Wang, Feng Wu, Zirui Huang, Shuli Zeng, Xiang-Yang Li
arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.
By Ziheng Zhang, Wei Zhang
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
By Kaiwen Chen, Xin Tan, Jingzong Li, Hong Xu
arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.
By Jonathan Hoss, Moritz Link, Noah Klarmann
arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.
By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu
arXiv:2609.17193v1 Announce Type: new
Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed...
By Zhen Li, Jun Cai, Haoran Gao, An Li, Tan Li
arXiv:2602. 18109v3 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets.
By Rong Fu, Yibo Meng, Zeyu Zhang, Ziming Guo, Jia Yee Tan, Xiaojing Du, Simon James Fong
arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.
By Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang
arXiv:2501. 12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities.
By Zhuoran Li, Ruishuo Chen, Hai Zhong, Longbo Huang
arXiv:2609.14952v1 Announce Type: new
Abstract: Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-...
By Zongjin Li, Shaohan Feng, Chunxi Yang, Wenbo Wang
arXiv:2508. 06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths.
By Meixuan Wang, Yinyu Ye, Zijie Zhou
The paper introduces COMLLM, a generative framework that combines Group Relative Policy Optimization with a Look‑Ahead Collaborative Simulation to enable multi‑turn reasoning for task offloading in Mobile Edge Computing. By performing multi‑step Monte Carlo rollouts that jointly model server queue dynamics, COMLLM incorporates long‑term system evolution into its reward design, achieving near‑optimal latency and improved load‑balancing fairness. The framework demonstrates zero‑shot scalability to larger network topologies, outperforming supervised fine‑tuning, deep reinforcement learning, and heuristic baselines without requiring retraining.
By Ning Yang, Chuangxin Cheng, Haijun Zhang