Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Sahu, Babuli; Swain, S. K.; Mangalampalli, Sudheer; Mishra, Satyasis

doi:10.1155/2023/4350615

Cited by 5 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An undertaking focused on multi-objective work process booking strategy was acquainted in [15] with precisely map approaching work processes into identical virtual assets. A better Coral Reef Improvement (ICRO) was created in [16] to address task booking issues and altogether lessen the makespan.…”

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

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

show abstract

“…To address these challenges, we propose a smart, real-time, secure strategy for allocating and scheduling resources based on fog computing and the multi-objective crow search algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

Security-Aware Resource Allocation in Fog Computing Environments

Kumar

2024

Preprint

View full text Add to dashboard Cite

Fog computing, an innovative approach building upon cloud technology, taps into local user resources to enhance services. It boasts cost-effectiveness, improved security, and reduced network delays, making it a popular choice for real-time applications. However, due to the heterogeneity of fog devices, resource allocation and scheduling pose challenges. This paper introduces a novel solution using a crow-inspired search mechanism and a multi-objective evolutionary approach for fog computing environments. The primary objectives are to optimize security and success rates simultaneously. A local search method enhances the performance of the crow search algorithm (CSA). The proposed method utilizes evolutionary techniques to allocate and schedule resources effectively. We compared our hybrid CSA with the original CSA and Genetic Algorithm (GA) across seven scenarios with varying parameters. Our hybrid CSA consistently outperformed existing methods, demonstrating its effectiveness in achieving the desired objectives.

show abstract

A heuristic task scheduling algorithm in cloud computing environment: an overall cost minimization approach

Boroumand,

Hosseini Shirvani,

Motameni

2024

Cluster Comput

View full text Add to dashboard Cite

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Cited by 5 publications

References 27 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

Security-Aware Resource Allocation in Fog Computing Environments

A heuristic task scheduling algorithm in cloud computing environment: an overall cost minimization approach

Contact Info

Product

Resources

About