DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

Mangalampalli, Sudheer; Karri, Ganesh Reddy; Kumar, Mohit; Khalaf, Osama Ibrahim; Romero, Carlos Andres Tavera; Sahib, GhaidaMuttashar Abdul

doi:10.1007/s11042-023-16008-2

Cited by 30 publications

(15 citation statements)

References 33 publications

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“…In this part, we execute trial appraisals of the proposed MGRNFL procedure close by existing techniques, DRLBTSA [1] and MTD-DHJS [2]. To accomplish asset effective undertaking planning, we use the Individual Cloud Datasets acquired from http://cloudspaces.eu/results/datasets.…”

Section: Methodsmentioning

confidence: 99%

“…This section assessment of the MGRNFL technique with existing methods, DRLBTSA [1] and MTD-DHJS [2] are presented in terms of task scheduling efficiency, makespan, and throughput. The performances of these parameters are analyzed using table and graphical representation.…”

Section: Comparative Performance Analysismentioning

confidence: 99%

“…A few undertaking booking techniques have been created in cloud conditions. A Profound Support Learning Based Task-Scheduling Calculation (DRLBTSA) was created in [1] to address the unique idea of errand booking and ideal asset distribution. Nonetheless, the calculation didn't improvement in that frame of mind of undertaking planning.…”

Section: Introductionmentioning

confidence: 99%

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Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

George,

B K,

P Vijayan

2024

E3S Web of Conf.

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Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

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Section: Methodsmentioning

confidence: 99%

Section: Comparative Performance Analysismentioning

confidence: 99%

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Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

George,

B K,

P Vijayan

2024

E3S Web of Conf.

View full text Add to dashboard Cite

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“…To put this another way, the signal generating method that is used in the GFDM system has a negative impact on the performance of the classical SLM scheme [ 36 ]. The cumulative symbol optimization process that was devised for the purpose of avoiding the PAPR-increasing consequence of the symbol addition operation in was therefore implemented into the PTS scheme [ 37 ]. As a result of these actions, a fresh method for lowering PAPR was devised and given the name CBBO.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel partial sequence technique based Chaotic biogeography optimization for PAPR reduction in generalized frequency division multiplexing waveform

Xue,

Palanisamy,

et al. 2023

Heliyon

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“…Traditional scheduling algorithms usually handle these problems via greedy or rulebased strategies, but they are difficult to solve complex problems with multiple constraints. Compared to this, deep reinforcement learning is a more flexible and intelligent method that can optimize task scheduling decisions via continuous learning while maximizing production efficiency while satisfying multiple constraints, such as the adaptive operator selection paradigm based on dual deep Q-network (DDQN) proposed in reference [13]. The scheduling decision of production tasks usually occurs in dynamic and uncertain environments, and many traditional scheduling algorithms are difficult to handle in this situation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Pu,

Li,

Rahimifard

2024

Sustainability

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In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

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DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

Cited by 30 publications

References 33 publications

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

A Novel partial sequence technique based Chaotic biogeography optimization for PAPR reduction in generalized frequency division multiplexing waveform

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

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