Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A taxonomy and survey of grid resource management systems for distributed computing
Software—Practice & Experience
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms with Small Populations
Proceedings of the 5th International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Stork: Making Data Placement a First Class Citizen in the Grid
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Pegasus: A framework for mapping complex scientific workflows onto distributed systems
Scientific Programming
A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Workflow task clustering for best effort systems with Pegasus
Proceedings of the 15th ACM Mardi Gras conference: From lightweight mash-ups to lambda grids: Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities
Falkon: a Fast and Light-weight tasK executiON framework
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Modeling the Latency on Production Grids with Respect to the Execution Context
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
A data placement service for petascale applications
PDSW '07 Proceedings of the 2nd international workshop on Petascale data storage: held in conjunction with Supercomputing '07
Specification and runtime workflow support in the ASKALON Grid environment
Scientific Programming - Dynamic Computational Workflows: Discovery, Optimization and Scheduling
Accelerating large-scale data exploration through data diffusion
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
SimGrid: A Generic Framework for Large-Scale Distributed Experiments
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Workflows and e-Science: An overview of workflow system features and capabilities
Future Generation Computer Systems
Overhead Analysis of Grid Workflow Applications
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
A stopping criterion based on Kalman estimation techniques with several progress indicators
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
NSGA-II Based Grid Task Scheduling with Multi-QoS Constraint
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
A multi-dimensional classification model for scientific workflow characteristics
Proceedings of the 1st International Workshop on Workflow Approaches to New Data-centric Science
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on computational tasks and workflow makespan. However, as workflow-based analysis becomes ever more data intensive, data optimization is becoming a prime concern. Moreover, scientific workflows can scale along several dimensions: (i) number of computational tasks, (ii) heterogeneity of computational resources, and the (iii) size and type (static versus streamed) of data involved. Adapting workflow structure in response to these scalability challenges remains an important research objective. Understanding how a workflow graph can be restructured in an automated manner (through task merge, for instance), to address constraints of a particular execution environment is explored in this work, using a multi-objective evolutionary approach. Our approach attempts to adapt the workflow structure to achieve both compute and data optimization. The question of when to terminate the evolutionary search in order to conserve computations is tackled with a novel termination criterion. The results presented in this article demonstrate the feasibility of the termination criterion and demonstrate that significant optimization can be achieved with a multi-objective approach.