Towards decomposition based multi-objective workflow scheduling for big data processing in clouds
- 24 November 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Cluster Computing
- Vol. 24 (1), 115-139
- https://doi.org/10.1007/s10586-020-03208-w
Abstract
No abstract availableKeywords
Funding Information
- Natural Science Foundation of Fujian Province (2018J01107)
- National Natural Science Foundation of China (61672439)
This publication has 27 references indexed in Scilit:
- A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computingFuture Generation Computer Systems, 2018
- Cost optimization for deadline-aware scheduling of big-data processing jobs on cloudsFuture Generation Computer Systems, 2018
- An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in CloudsDistributed and Parallel Databases, 2017
- Cost Optimization for Scheduling Scientific Workflows on Clouds under Deadline ConstraintsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Cost-Efficient CPU Provisioning for Scientific Workflows on CloudsLecture Notes in Computer Science, 2016
- Interactive multiobjective optimization with NIMBUS for decision making under uncertaintyOR Spectrum, 2013
- Characterizing and profiling scientific workflowsFuture Generation Computer Systems, 2013
- MOEA/D: A Multiobjective Evolutionary Algorithm Based on DecompositionIEEE Transactions on Evolutionary Computation, 2007
- Synchronous approach in interactive multiobjective optimizationEuropean Journal of Operational Research, 2006
- Performance-effective and low-complexity task scheduling for heterogeneous computingIEEE Transactions on Parallel and Distributed Systems, 2002