Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement Learning

Shresthamali, Shaswot; Kondo, Masaaki; Nakamura, Hiroshi

doi:10.3390/jlpea12040053

Cited by 4 publications

(4 citation statements)

References 63 publications

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“…MC involves randomness in its estimations [79][80][81], while RL continuously improves effectiveness through experimentation and learning [42,82]. Larger sets of proposed felling trees provide more samples for training and enhancing experimental results in MC and RL [38,81,83,84]. PSO is more suitable for optimizing a relatively small number of trees chosen based on QVM or VMM, helping avoid local optimization [85,86].…”

Section: Influence Of Proposed Selective Felling Tree Determination M...mentioning

confidence: 99%

“…Larger sets of proposed felling trees inherently offer more training samples, thus potentially enhancing the experimental outcomes in both MC and RL [38,81,83]. On the other hand, PSO is better suited for optimizing a relatively modest number of trees chosen using QVM or VMM, which aids in avoiding issues related to local optimization [89,90].…”

Section: Influence Of Proposed Selective Felling Tree Determination M...mentioning

confidence: 99%

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Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Xuan,

Wang,

Chen

2023

Forests

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Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

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Section: Influence Of Proposed Selective Felling Tree Determination M...mentioning

confidence: 99%

Section: Influence Of Proposed Selective Felling Tree Determination M...mentioning

confidence: 99%

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Xuan,

Wang,

Chen

2023

Forests

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“…In order to evaluate the efficacy of the proposed framework, Shresthamali, S. et al [32] focused on designs to simulate both single-task and dual-task systems. The results show that their Multi-Objective Reinforcement Learning (MORL) algorithms can learn superior policies while incurring lower learning costs and effectively balancing competing goals during execution.…”

Section: Background Studymentioning

confidence: 99%

“…14 Shresthamali, S. et al [32] This paper focuses on simulating both single-task and dual-task systems in order to assess the effectiveness of the proposed framework. The outcomes prove that their Multi-Objective Reinforcement Learning (MORL) algorithms are able to learn superior policies with reduced learning costs and effectively balance competing goals during execution.…”

Section: Background Studymentioning

confidence: 99%

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

et al. 2023

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One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.

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Dynamic multi-objective service composition based on improved social learning optimization algorithm

Hai,

Xu,

Liu

2024

Applied Soft Computing

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Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement Learning

Cited by 4 publications

References 63 publications

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

Dynamic multi-objective service composition based on improved social learning optimization algorithm

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