Analysis and review of effectiveness of metaheuristics in task scheduling process with delineating machine learning as suitable alternative

Author:

Lohi Shantanu A.,Tiwari Nandita,Namdeo Varsha,Lohi Snehal A.

Publisher

IEEE

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization;Concurrency and Computation: Practice and Experience;2023-11-20

2. Design of a Crop Disease Detection Model using Multi-parametric Bio-inspired Feature Representation and Ensemble Classification;2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT);2023-02-11

3. An Energy-Efficient Task Scheduling Method for CPU-GPU Heterogeneous Cloud;2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2022-12

4. Enhanced adaptive creation of visualisation network by detection of leaves;Materials Today: Proceedings;2021-02

5. A Machine Learning Inspired Task Scheduling Process Improved Novel Algorithm (TspINA) for Reduction in Overall System Requirements to Perform Multi-functional Tasks by Boosting Efficiency;Advances in Intelligent Systems and Computing;2021

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