Scheduling framework based on reinforcement learning in online-offline colocated cloud environment by Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG

“…Some reinforcement learning-based scheduling algorithms for cloud computing platforms barely considered one scenario or ignored the resource constraints of jobs and treated all machines as the same type, which caused low resource utilization or insufficient scheduling efficiency.To address the scheduling problems in online-offline colocated cloud environment, a framework named JobFusion was proposed.Firstly, an efficient resource partitioning scheme was built in the cloud computing platform supporting virtualization technology by integrating the hierarchical clustering method with connectivity constraints.Secondly, a graph convolutional neural network was utilized to embed the attributes of elastic dimension with various constraints and the jobs with various numbers, to capture the critical path information of workflow.Finally, existing high-performance reinforcement learning methods were integrated for scheduling jobs.According to the results of evaluation experiments, JobFusion improves the resource utilization by 39.86% and reduces the average job completion time by up to 64.36% compared with baselines.…”
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Distributed Broadcast with Minimum Latency in Asynchronous Wireless Sensor Networks under SINR-Based Interference by Shiliang Xiao, Lebing Pan, Jianpo Liu, Baoqing Li, Xiaobing Yuan

“…Existing works all assume strict time synchronization and provide centralized TDMA scheduling algorithms. However, WSNs in practice are more likely to be distributed asynchronous systems. …”
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EPO Extension of Dispatching Rules to Minimize Effects of Time Uncertainty in Production Scheduling by Radosław Puka, Iwona Skalna, Jerzy Duda, Tomasz Derlecki

“…., it has no negative impact on their most important advantage over other production scheduling algorithms. The results obtained from the real data of a large international offset printing company with various machine and job heterogeneities confirm the effectiveness of the proposed EPO extension in producing schedules that are indeed robust to changes in production time.…”
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Battery swapping scheduling for electric vehicles: a non-cooperative game approach by Yu Zhang, Tao Han, Wei He, Jianhua Xia, Lichao Cui, Zuofu Ma, Shiwei Liu

“…Therefore, it is crucial to develop efficient battery-swapping scheduling algorithms to optimize the operations of battery-swapping systems. …”
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A novel quality of service-aware task scheduling approach for cloud computing using modified Wombat Optimization Algorithm by Lijuan Yan

“…Most existing scheduling algorithms encounter difficulties in terms of premature convergence and suboptimal performance in multi-objective scenarios. …”
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Hi-Speed USB Based Middleware for Integration of Real-Time Systems with the Cloud by Jacek Augustyn, Paweł Maślanka, Grzegorz Hamuda

“…Variants including three embedded hardware implementations of the proposed architecture, two software realizations (single and multithread ones), and two process scheduling algorithms have been examined to evaluate their real-time performances. …”
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Social Media Management System for Educational Promotion by Yerik Afrianto Singgalen, Dorien Kartikawangi, Birgitta Narindri Rara Winayu

“…The resulting system integrates automated content workflows, scheduling algorithms, demographic-based audience targeting, and real-time performance analytics. …”
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MRR scheduling based cognitive LTE-R eNode B and type of service value priority queue management strategy by Hongyu DENG, Yiming WANG, Cheng WU

“…Cognitive radio technology was used to build cognitive radio networks in long-term evolution railway communication systems,and cognitive base station network architecture and MRR scheduling algorithms were proposed to improve the spectrum utilization of railway dedicated networks.In particular,the FIFO policy for cognitive LTE-R base stations in a single vehicle gateway causes excessive delay in real-time services of secondary users,and a cognitive LTE-R base station queue management strategy based on priority of service types was proposed.This strategy gave priority to sending packets with large service type values to the queue,so as to achieve the purpose of prioritizing real-time services.Simulation results show that under the premise of ensuring the normal communication of vehicles,the constructed cognitive base station can provide passengers with additional internet access links,and at the same time,improve the spectrum utilization rate of the LTE-R communication system.In addition,the cognitive base station queue management strategy based on service type value priority can effectively reduce the delay of passenger real-time services.…”
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Optimizing makespan and resource utilization in cloud computing environment via evolutionary scheduling approach. by Faten K Karim, Sara Ghorashi, Salem Alkhalaf, Saadia H A Hamza, Anis Ben Ishak, S Abdel-Khalek

“…Previous research found that metaheuristics can dramatically increase CC performance if deployed as scheduling algorithms. Therefore, this study develops an evolutionary algorithm-based scheduling approach for makespan optimization and resource utilization (EASA-MORU) technique in the cloud environment. …”
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Stair Scheduling for Data Collection in Wireless Sensor Networks by Jinbiao Chen, Yongcai Wang, Yuexuan Wang, Changjian Hu

“…The results show that the performances of stair scheduling are much better than that of random scheduling algorithms.…”
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Edge Cloud Resource Scheduling with Deep Reinforcement Learning by Y. Feng, M. Li, J. Li, Y. Yu

“…Designing optimal scheduling algorithms for task allocation in edge cloud clusters presents significant challenges due to the constantly changing workloads and service requests in edge cloud data center environments. …”
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Cost-aware cloud workflow scheduling using DRL and simulated annealing by Yan Gu, Feng Cheng, Lijie Yang, Junhui Xu, Xiaomin Chen, Long Cheng

“…While several scheduling algorithms have been proposed in recent years, they are mainly designed to handle batch tasks and not well-suited for real-time workloads. …”
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Limited-Preemption EDF Scheduling for Multi-Phase Secure Tasks by Standaert, Benjamin, Raadia, Fatima, Sudvarg, Marion, Baruah, Sanjoy, Chantem, Thidapat, Fisher, Nathan, Gill, Christopher

“…We develop corresponding limited-preemption EDF scheduling algorithms and associated pseudo-polynomial schedulability tests for constrained-deadline MPS tasks. …”
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Task Scheduling in Cloud Environment–Techniques, Applications, and Tools: A Systematic Literature Review by Olanrewaju L. Abraham, Md Asri Bin Ngadi, Johan Bin Mohamad Sharif, Mohd Kufaisal Mohd Sidik

“…This taxonomy categorizes metaheuristic scheduling techniques based on scheduling algorithms, problem nature, task types, primary scheduling objectives, task-resource mapping, scheduling constraints, and testing environments. …”
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An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems by A. Al-Wattar, S. Areibi, G. Grewal

“…An efficient reconfigurable platform is implemented along with novel placement/scheduling algorithms. The proposed algorithms tend to reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software and hardware to efficiently utilize resources, and reduce computation time. …”
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Resource Scheduling Method for Integration of TT＆C and Observation Based on Multi-Agent Deep Reinforcement Learning by Siyue CHENG, Haoran LI, Weigang BAI, Di ZHOU, Yan ZHU

“…With the development of satellite communication technology and the continuous expansion of the constellation scale, the integration of TT＆amp;C and observation technology has become the mainstream trend.The large constellation scale, many scheduling objects and complex operation joint control bring great challenges to the integrated resource scheduling of satellite network TT＆amp;C and observation.Subject to the low solution effi ciency and complex constraints of scheduling algorithms, the traditional TT＆amp;C resource scheduling technology adopts the advance injection TT＆amp;C instructions to perform tasks according to the fi xed deployment, which is diffi cult to meet the scheduling needs of emergencies and emergency tasks.Therefore, a kind of resource scheduling method based on multi-agent actor-Agent Actor-Critic Deterministic Policy Gradient Algorithms (MADDPG) was presented.With centralized training and distributed execution, the multi-agent model of integrated task of TT＆amp;C and observation was established.By analyzed the scheduling strategy of neighbor agent, the response speed of local information was improved.According to the model and constraints in the integrated resource scheduling problem of TT＆amp;C and observation, selected signifi cant and interpretable constraints, then established the multi-agent resource scheduling reinforcement learning model, and carried on the simulation test.The simulation results showed that the task benefi t of this method was 22% higher than the traditional method.…”
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Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization by Mohammad Babar, Muhammad Sohail Khan, Ahmad Din, Farman Ali, Usman Habib, Kyung Sup Kwak

“…The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. …”
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Confidential Intelligent Traffic Light Control System: Prevention of Unauthorized Traceability by Ahmad Audat, Maram Bani Younes, Marah Yahia, Said Ghoul

“…Many research studies have designed intelligent traffic light scheduling algorithms. Some researchers rely on specialized sensors and hardware to gather real-time traffic data at signalized road intersections. …”
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Joint Antenna Selection and Proportional Fairness User Scheduling for Multi-User Massive MIMO Systems by Boqing Chen, Lijun Yang, Meng Wu

“…Furthermore, to tackle the issue of fairness, which is often overlooked by traditional user scheduling algorithms, a proportional fairness user scheduling (PFUS) approach is proposed. …”
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From Simulation to Implementation: A Systems Model for Electric Bus Fleet Deployment in Metropolitan Areas by Ludger Heide, Shuyao Guo, Dietmar Göhlich

“…The framework employs empirical energy models, graph-based scheduling algorithms, and integer linear programming for depot assignment and smart charging. …”
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