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2024 International Conference on Intelligent Robots and Systems

作者： Lumpp, Francesco Fummi, Franco Bombieri, Nicola Univ Verona Dept Innovat Med Verona Italy

ISBN: (纸本)9798350377712;9798350377705

This study addresses the challenge of deploying robotic software with Quality of Service (QoS) constraints in Edge-Cloud computing clusters. The paper introduces HEFT4K, an event-driven scheduling method tailored for Kubernetes-managed systems based on the Heterogeneous Early Finish Time (HEFT) algorithm. This algorithm reduces software execution time (makespan) and facilitates re-mapping in case of node failures, involving only essential containers to maintain uninterrupted robot functionality. Experimental results, conducted on a real-world robot and synthetic benchmarks, show a 75% speedup in makespan compared to the standard Kubernetes scheduler, enhancing the efficiency of QoS-focused scheduling for robotic applications in distributed systems.

关键词： scheduling algorithms

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SAT-based scheduling Algorithm for High-level Synthesis Considering Resource Sharing

SAT-based Scheduling Algorithm for High-level Synthesis Cons...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jiang, Hao Yang, Fan Yan, Changhao Zeng, Xuan Fudan Univ State Key Lab ASIC & Syst Shanghai 201203 Peoples R China

ISBN: (纸本)9781665484855

The resource-sharing constraints can be imposed by limiting the maximum allowable number of components for individual functional units in the scheduling process. However, the sharing of the functional units is not explicitly considered in the scheduling procedure. In this paper, we propose a SATbased scheduling algorithm for high-level synthesis considering the resource-sharing problem. Several pruning strategies have been proposed to reduce the search

关键词： Limiting scheduling algorithms Circuits and systems scheduling Space exploration Resource management

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MICCO: An Enhanced Multi-GPU scheduling Framework for Many-Body Correlation Functions 36

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-B...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Wang, Qihan Ren, Bin Chen, Jie Edwards, Robert G. William & Mary Dept Comp Sci Williamsburg VA 23185 USA Jefferson Lab Newport News VA USA

ISBN: (纸本)9781665481069

Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the dataintensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPUs.

关键词： Distributed processing Correlation Tensors Quantum computing Scientific computing scheduling algorithms Lattices

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Effective Reward Schemes for Tardiness Optimization 26th

Effective Reward Schemes for Tardiness Optimization

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26th International Conference on Data Warehousing and Knowledge Discovery (DaWaK)

作者： Idan, Lihi Texas A&M Univ College Stn TX 77843 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9783031683220;9783031683237

Over recent years, reinforcement learning has become a prominent method for the optimization of sequential decision-making problems. One group of sequential decision-making problems that has benefited significantly from reinforcement-learning-based optimization techniques is scheduling problems. However, most existing reinforcement learning works on scheduling optimization aim at optimizing a single, makespan-based objective. While the makespan-the overall time from the start of the first task to the end of the last task-is indeed important in some endeavors, other endeavors benefit more from the optimization of other types of objectives. In this work, we focus on Tardiness-based objectives and present a new reward scheme that aims at simultaneously optimizing multiple notions of Tardiness.

关键词： scheduling algorithms

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Data Importance-Assisted Multi-User scheduling in MIMO Edge Learning Systems

Data Importance-Assisted Multi-User Scheduling in MIMO Edge ...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Huang, Hongqing Wu, Peiran Zhao, Junhui Xia, Minghua Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China

ISBN: (数字)9781665459778

ISBN: (纸本)9781665459778

With the wide development of intelligent communication systems, efficient data transmission is critical to fast edge learning in multi-user multiple-input multiple-output (MIMO) systems since the data acquisition from massive edge devices has become a bottleneck. To cope with the mismatch between the empirical probability of the transmitted data and the expected one, this paper first proposes to quantify data importance using the Kullback-Leibler divergence. Then, we design a multi-user scheduling criterion that combines the channel state information and data importance indicators, followed by an iterative multi-user scheduling algorithm. Finally, experimental results demonstrate that the proposed multi-user scheduling strategy significantly improves the learning efficiency and the test accuracy of edge learning systems.

关键词： Learning systems scheduling algorithms Simulation Particle measurements scheduling Iterative algorithms MIMO

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Energy-Aware scheduling of Parallel Application in Hybrid Computing System

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Chinese Journal of Electronics 2025年第4期23卷 688-694页

作者： Yan Ma Bin Gong Zhihong Guo Yufeng Chen Lida Zou State Grid Shandong Electric Power Research Institute Jinan China School of Computer Science and Technology Shandong University Jinan China

Energy management is emerging as an important issue for High performance computing (HPC) owning to high operational cost and low reliability. Compared with low-power architectural approach, energy-aware scheduling based on Dynamic voltage scaling (DVS) and Dynamic power management (DPM) is regarded as a promising way since it is practical and low-cost. At present, most studies focus on pure DVS or non-DVS environment, while most high performance computing systems are hybrid non-DVS/DVS platforms. We propose an energy-aware scheduling algorithm for parallel application to consider both DVS and non-DVS characteristics of hybrid system. We present the rule of task assignment, make analysis on DVS and DPM technique and give their mathematical formulation, which maintains makespan optimization and energy conservation. The clustering and merging algorithm, and priority computation method consider the situation of resource constraints. The extensive simulations demonstrate that the proposed algorithm has stronger ability of energy saving and time optimization than Heterogeneous earliest finish time (HEFT), Energy-efficient task duplication scheduling (EETDS) and Heterogeneous energy-aware duplication scheduling (HEADUS) algorithm no matter for synthetic workload or realistic workload.

关键词： scheduling algorithms High performance computing Power system management Energy conservation Clustering algorithms Dynamic scheduling Energy efficiency Reliability Voltage control Optimization

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Dynamic Jobshop scheduling Algorithm Based on Deep Q Network

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IEEE ACCESS 2021年 9卷 122995-123011页

作者： Zhao, Yejian Wang, Yanhong Tan, Yuanyuan Zhang, Jun Yu, Hongxia Shenyang Univ Technol Inst Artificial Intelligence Shenyang 100870 Peoples R China

Jobshop scheduling is a classic instance in the field of production scheduling. Solving and optimizing the scheduling problem of the jobshop can greatly reduce the production cost of the workshop and improve the processing efficiency, thereby improving the market competitiveness of the manufacturing enterprises. In order to make decisions on the complex dynamic scheduling process more accurately and simplify the solution process, the jobshop scheduling problem can be transformed into a reinforcement learning problem based on the Markov decision process. The performance of the adaptive scheduling algorithm in a dynamic manufacturing environment is improved based on the Deep Q Network (DQN). In the proposed scheduling algorithm, five state features of continuous value ranges are designed for input to a Deep Neural Network (DNN), as well as ten well-known heuristic dispatching rules are selected as the action set of the DQN. In the proposed scheduling algorithm, the target network and the prediction network are used to train the parameters. An action selection strategy based on the "softmax" function is designed in DQN. It selects dispatching rules with the largest action value as the execution action, thereby solving the problem that the suboptimal action value is greater than the optimal action Q value in the early learning stage. Furthermore, the non-optimal action is selected with a greater probability in the later learning stage. Ten benchmark jobshop test instances called "LA" used as simulation objects and operated in a simulation environment composed of Python. The simulation results confirm that the proposed scheduling algorithm based on DQN has better performance and universality than a single dispatching rule or traditional Q learning algorithm.

关键词： Heuristic algorithms Job shop scheduling scheduling Dynamic scheduling scheduling algorithms Production Dispatching Dynamic scheduling deep Q network deep reinforcement learning dispatching rules job shop scheduling

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A polynomial time approximation scheme for the scheduling problem in the AGV system

A polynomial time approximation scheme for the scheduling pr...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Li, Xinrui Wang, Chaoyang Hu, Hao Liang, Yanxue Westlake Univ Ctr Innovat Res Hangzhou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Logistics warehouses face the challenge of fulfilling large bulk pick orders limit in a given time, as the information of logistics orders is different and timeliness. Therefore, in automated warehouses, it is imperative to improve the efficiency and intelligence of order picking by robotic systems. However, the existing automated guided vehicle (AGV) system has only a few fixed functions (such as order sorting and transportation, etc.), which cannot be changed in time according to actual needs. Meanwhile, the scheduling algorithm has only mass heuristics results and a few approximate algorithm results. In this paper, we build a new AGV system, including two kinds of shelves and four kinds of stations, where the system can add new station types according to the actual situation. We establish the equivalent relationship between the order group picking task in this system and the multi-stage hybrid flow shop scheduling problem, without considering the order group transfer process between stations. Furthermore, we propose a polynomial time approximation scheme (PTAS) for the scheduling problem in this system which has been proved to be strongly NP-hard [13].

关键词： Job shop scheduling Remotely guided vehicles scheduling algorithms Transportation Production Parallel machines Approximation algorithms

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A "Big-Spine" Abstraction: Flow Prioritization With Spatial Diversity in The Data Center Network 25

A "Big-Spine" Abstraction: Flow Prioritization With Spatial ...

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IEEE 25th International Conference on High Performance Switching and Routing (HPSR)

作者： Cornacchia, Alessandro Bianco, Andrea Giaccone, Paolo Sviridov, German Politecn Torino Turin Italy Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9798350363869;9798350363852

Data center networks undergo the coexistence of latency-sensitive mice flows and bandwidth-intensive elephant flows. Jointly optimizing the performance of both traffic classes poses complex challenges. Existing flow schedulers either rely on detailed flow size information or require numerous physical priority queues (PQs) within network switches, thus facing practical challenges. In this work, we propose a novel flow scheduling algorithm, namely Multi-Path Multi-Level Feedback Queueing (MP-MLFQ), to overcome these limitations. MP-MLFQ leverages the spatial diversity and regularity of DCNs to realize a scheduler with numerous logical priority levels while occupying as few as 2 physical PQs at each switch port. We designed MP-MLFQ to run atop modern programmable networks, and highlighted how to implement it without modifications at the end-hosts' stacks. Our simulation results show that MP-MLFQ outperforms existing flow size-agnostic solutions in minimizing the flow completion time, when only two PQs are available.

关键词： scheduling algorithms

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Scalable Cyclic Schedulers for Age of Information Optimization in Large-Scale Status Update Systems

Scalable Cyclic Schedulers for Age of Information Optimizati...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Akar, Nail Liyanaarachchi, Sahan Ulukus, Sennur Bilkent Univ Ankara Turkiye Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350384482;9798350384475

We study open-loop cyclic scheduling for generateat-will (GAW) multi-source status update systems with heterogeneous service times and packet drop probabilities, with the goal of minimizing the weighted sum age of information (AoI), called system AoI, or the weighted sum peak AoI (PAoI), called system PAoI. In particular, we propose an offline method to obtain a well-performing cyclic schedule, which can scale to very large number of information sources. Moreover, the proposed schedule has a very low online implementation complexity. The proposed schedules are comparatively studied against existing age-agnostic scheduling algorithms in terms of computational load and system AoI/PAoI performance, to validate their effectiveness.

关键词： scheduling algorithms

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