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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Fink, Andreas Mogensen, Rasmus Suhr Rodriguez, Ignacio Kolding, Troels Karstensen, Anders Pocovi, Guillermo Aalborg Univ Dept Elect Syst Wireless Commun Networks Sect Aalborg Denmark Nokia Bell Labs Aalborg Denmark

ISBN: (纸本)9781665442664

Due to mobility and interference, using enterprise Wi-Fi for communication in industrial networks can result in control loop latencies exceeding 100 ms for at least 0.1% of the time, even in those cases where Wi-Fi handover-specific parameters have been optimized, making the technology unfit for Industrial IoT (IIoT) with strict communication reliability requirements. To improve its performance, this paper presents a novel approach towards the design and implementation of a radio-aware multi-connectivity concept using a layer-4 scheduling mechanism. Two packet scheduling mechanisms are presented: packet duplication and best path scheduling. A mobility coordinator scheme is used to improve the performance of the packet schedulers by preventing simultaneous handovers and ensures the STAs connect to different APs. By using this multi-connectivity solution, a significant performance improvement was observed, cutting down the latencies of the system to 30-80 ms at the 99.9%-ile of reliability (depending on the operational conditions). Furthermore, by applying the proposed schemes, Wi-Fi handovers delays can be fully mitigated allowing for true seamless roaming in mobile conditions.

关键词： Performance evaluation Job shop scheduling scheduling algorithms Switches Interference Handover Roaming

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Intelligent Equipment scheduling Optimization Model for Transmission Lines Based on Improved BFO Algorithm

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2025年 12卷 1-12页

作者： Zheng, Wulue Zhang, Xin Zhang, Fuchun Wang, Ning Zheng, Yangliang Wang, Zhi China Southern Power Grid Co. Ltd. EHV Transmission Company Guangzhou51000 China Co. Ltd. Guangzhou51000 China

INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task. OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm. METHODS: This model achieves global and local search capabilities through an improved Bacterial Foraging Optimization algorithm, maintaining the diversity of equipment states and effectively improving the optimization level of scheduling results. RESULTS: At 3000 iterations, the model was able to reach its optimal state, and its optimization results showed excellent performance in terms of convergence and uniformity, which was very close to the optimal solution. In practical applications, the performance of the intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm is also excellent. The average line usage rate of the scheduling scheme proposed by the model reached 70.69%, while the average line usage rate of the manual scheduling scheme was only 64.63%. In addition, the optimal relative error percentage of this model is less than 2.1%, while the BRE of other algorithms reaches around 10%. CONCLUSION: The intelligent equipment scheduling optimization model for transmission lines on the ground of improved Bacterial Foraging Optimization algorithm has important practical significance for improving the operational efficiency of the power system, reducing operating costs, and making sure the stable and reliable operation of the power system. © 2025 W. Zheng et al.

关键词： scheduling algorithms

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Universal Policy Tracking: scheduling for Wireless Networks with Delayed State Observation 58

Universal Policy Tracking: Scheduling for Wireless Networks ...

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58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Liu, Bai Modiano, Eytan MIT Lab Informat & Decis Syst Cambridge MA 02138 USA

ISBN: (数字)9798350399981

ISBN: (纸本)9798350399981

Numerous scheduling algorithms have been proposed to optimize various performance metrics like throughput, delay and utility in wireless networks. However, these algorithms often require instantaneous access to network state information, which is not always available. While network stability can sometimes be achieved with delayed state information, other performance metrics such as latency may degrade. Thus, instead of simply stabilizing the system, our goal is to design a framework that can mimic arbitrary scheduling algorithms with performance guarantees. A naive approach is to make decisions directly with delayed information, but we show that such methods may lead to poor performance. Instead, we propose the Universal Tracking (UT) algorithm that can mimic the actions of arbitrary scheduling algorithms under observation delay. We rigorously show that the performance gap between UT and the scheduling algorithm being tracked is bounded by constants. Our numerical experiments show that UT significantly outperforms the naive approach in various applications.

关键词： Measurement scheduling algorithms Wireless networks Spread spectrum communication Throughput Stability analysis scheduling

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Task Duplication-Based scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing

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IEEE ACCESS 2021年 9卷 37262-37272页

作者： Yao, Fuguang Pu, Changjiu Zhang, Zongyin Chongqing Univ Educ Informat Ctr Chongqing 400065 Peoples R China

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks;and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

关键词： Task analysis Cloud computing Optimal scheduling scheduling algorithms Resource management Schedules Internet of Things Cloud computing task duplication workflow scheduling resource provision heuristic mechanism

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Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility

Dynamic Targeting of Satellite Observations Incorporating Sl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kangaslahti, Akseli Candela, Alberto Swope, Jason Yue, Qing Chien, Steve Univ Michigan Ann Arbor MI 48109 USA CALTECH Jet Prop Lab Pasadena CA 91011 USA

ISBN: (纸本)9798350384581;9798350384574

Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a lookahead sensor. However, current implementations have failed to account for realistic satellite operational constraints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dynamic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.

关键词： scheduling algorithms

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A DVFS-Weakly Dependent Energy-Efficient scheduling Approach for Deadline-Constrained Parallel Applications on Heterogeneous Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2021年第12期40卷 2481-2494页

作者： Huang, Jing Li, Renfa An, Jiyao Zeng, Haibo Chang, Wanli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan Univ Key Lab Embedded & Network Comp Hunan Prov Changsha 410082 Hunan Peoples R China Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches-that are independent of, or weakly dependent on DVFS-for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

关键词： Task analysis Energy consumption Time-frequency analysis Real-time systems Transient analysis Switches scheduling algorithms Dynamic voltage and frequency scaling (DVFS) energy efficiency heterogeneous systems real-time systems

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Optimal Power Control, scheduling, and Energy Harvesting for Wireless Networked Control Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第3期69卷 1789-1801页

作者： Karadag, Goksu Iqbal, Muhammad Shahid Coleri, Sinem Koc Univ Dept Elect & Elect Engn TR-34450 Istanbul Turkey

Communication system design for wireless networked control systems (WNCSs) requires strict timing, reliability and lifetime guarantees despite limited battery resources and the non-idealities introduced by wireless networking such as delays. In this paper, we introduce radio frequency (RF) energy harvesting paradigm into WNCS framework for the first time in the literature. We study the optimal power control, energy harvesting and scheduling problem with the objective of providing maximum level of adaptivity under periodicity, delay and reliability requirements. We show that the power allocation problem is separable from the scheduling problem at optimality and provide the exact expression for optimal power control. The scheduling problem is then formulated as a mixed integer linear programming (MILP) problem and proven to be NP-Hard. For the scheduling, we propose polynomial-time heuristic algorithms motivated by the analogy between scheduling sensor nodes with energy harvesting requirements over time units and jobs with sequence dependent setup times on identical machines. We prove the theoretical worst-case bound for the performance of these heuristics. We show via extensive simulations that the proposed algorithms perform close-to-optimal and significantly better than Earliest Deadline First (EDF) algorithm in terms of adaptivity, delay, reliability and average runtime.

关键词： scheduling Delays Energy harvesting Wireless sensor networks Wireless communication scheduling algorithms Reliability Wireless networked control systems RF energy harvesting adaptivity power control scheduling

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A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第21期33卷 e6432-e6432页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara Univ Inst Engn & Technol Chandigarh Punjab India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real-time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta-analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.

关键词： Fog computing job scheduling optimal scheduling scheduling algorithms task scheduling

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On the Power of Randomization for scheduling Real-Time Traffic in Wireless Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第4期29卷 1703-1716页

作者： Tsanikidis, Christos Ghaderi, Javad Columbia Univ Dept Elect Engn New York NY 10027 USA

In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants.

关键词： Real-time systems Wireless networks Markov processes Interference scheduling algorithms Throughput Schedules scheduling real-time traffic Markov processes stability wireless networks

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Machine Learning for Job-flow Online scheduling and Resources Allocation in Distributed Computing 6

Machine Learning for Job-flow Online Scheduling and Resource...

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6th International Conference on Information Technologies in Engineering Education, Inforino 2022

作者： Toporkov, Victor Yemelyanov, Dmitry Bulkhak, Artem National Research University MPEI Moscow Russia

ISBN: (纸本)9781665405775

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0-1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm. The interdisciplinary nature of the considered problem and its solution (including elements from the areas of optimization, scheduling algorithms, machine learning and simulation) makes it a good study example in technical disciplines. © 2022 IEEE.

关键词： Greedy algorithms scheduling algorithms Computational modeling Scalability Prototypes Machine learning Parallel processing

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