H3CSA: A makespan aware task scheduling technique for cloud environments

Mishra, Ashutosh; Sahoo, Manmath Narayan; Satpathy, Anurag

doi:10.1002/ett.4277

Cited by 6 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization effects of various scheduling methods were analyzed and evaluated from multiple dimensions. The research results indicated that the research could make a certain contribution to scheduling problems based on field programmable gate arrays [7]. Ye W et al proposed a new unmanned aerial vehicle assisted edge computing system.…”

Section: Related Workmentioning

confidence: 93%

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Chen,

2024

IJACSA

View full text Add to dashboard Cite

Big data analysis can provide valuable insights not easily obtained from traditional data scales. However, addressing scheduling issues in big data can be challenging due to the vast amount and diverse nature of the data. To overcome this, a scheduling model based on Markov decision process is proposed. The deep Q-network algorithm is used for directed acyclic graph task scheduling. To improve this model further, the gradient strategy algorithm is introduced. From the results, when the dataset size was about 500, the hybrid algorithm achieved a recall rate of 0.96, outperforming the gradient strategy algorithm (0.83), deep Q-network algorithm (0.79), and estimated earliest completion time algorithm (0.63). Although the estimated earliest completion time algorithm had longer training times under different dataset sizes, the hybrid algorithm's training time was slightly longer than the gradient strategy algorithm and slightly shorter than the deep Q-network algorithm. Overall, the proposed algorithm exhibits superior performance and significant value in solving engineering problems.

show abstract

Section: Related Workmentioning

confidence: 93%

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

Chen,

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…A crow search-based meta-heuristic calculation was created in [17] to improve the makespan of undertaking planning. Nonetheless, the calculation didn't integrate dynamic virtual machine creation in light of past booking information.…”

Section: Related Workmentioning

confidence: 99%

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

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.

show abstract