MLP-ANN-Based Execution Time Prediction Model and Assessment of Input Parameters Through Structural Modeling

[1]  Thomas Fahringer,et al.  Predicting Workflow Task Execution Time in the Cloud Using A Two-Stage Machine Learning Approach , 2020, IEEE Transactions on Cloud Computing.

[2]  Shishir Kumar,et al.  Fault tolerance based load balancing approach for web resources , 2019, Journal of the Chinese Institute of Engineers.

[3]  Dinesh Kumar Verma,et al.  RETRACTED ARTICLE: Exponential Relationship Based Approach for Predictions of Defect Density Using Optimal Module Sizes , 2016, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences.

[4]  Alaa Mohamed Riad,et al.  A machine learning model for improving healthcare services on cloud computing environment , 2018 .

[5]  Suresh Chandra Satapathy,et al.  Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications , 2018, Cluster Computing.

[6]  Saoussen Krichen,et al.  Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing , 2018, Computing.

[7]  Rawya Rizk,et al.  Honey Bee Based Load Balancing in Cloud Computing , 2017, KSII Trans. Internet Inf. Syst..

[8]  A. Senthil Kumar,et al.  Soft Computing in Remote Sensing Applications , 2017, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences.

[9]  Yu-Sung Wu,et al.  Application Execution Time Prediction for Effective CPU Provisioning in Virtualization Environment , 2017, IEEE Transactions on Parallel and Distributed Systems.

[10]  Shishir Kumar,et al.  Load Balancing Approaches for Web Servers: A Survey of Recent Trends , 2017 .

[11]  Julian Szymanski,et al.  MERPSYS: An environment for simulation of parallel application execution on large scale HPC systems , 2017, Simul. Model. Pract. Theory.

[12]  Rubén Ruiz,et al.  A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds , 2017, Future Gener. Comput. Syst..

[13]  Miriam Leeser,et al.  FIM: Performance Prediction for Parallel Computation in Iterative Data Processing Applications , 2017, 2017 IEEE 10th International Conference on Cloud Computing (CLOUD).

[14]  Eui-nam Huh,et al.  Energy efficiency for cloud computing system based on predictive optimization , 2017, J. Parallel Distributed Comput..

[15]  B. K. Murthy,et al.  Assessment of cloud application development attributes through interpretive structural modeling , 2017, International Journal of System Assurance Engineering and Management.

[16]  Marco Aurélio Stelmar Netto,et al.  Job placement advisor based on turnaround predictions for HPC hybrid clouds , 2016, Future Gener. Comput. Syst..

[17]  Xiaodong Liu,et al.  Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment , 2016, Sensors.

[18]  Ameet Talwalkar,et al.  MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..

[19]  Shishir Kumar,et al.  An improved optimized resource allocation mechanism for web server grid , 2016, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC).

[20]  Olaf Spinczyk,et al.  FederatedCloudSim: a SLA-aware federated cloud simulation framework , 2014, CCB '14.

[21]  F. Hussain,et al.  Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization , 2014, International Journal of Parallel Programming.

[22]  B. Chandra Mohan,et al.  A survey: Ant Colony Optimization based recent research and implementation on several engineering domain , 2012, Expert Syst. Appl..

[23]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[24]  Ruay-Shiung Chang,et al.  Selecting the most fitting resource for task execution , 2011, Future Gener. Comput. Syst..

[25]  Jun Cheng,et al.  A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[26]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[27]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.