Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Multicriteria aspects of Grid resource management
Grid resource management
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Cost-Based Scheduling of Scientific Workflow Application on Utility Grids
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Scheduling of a meta-task with QoS requirements in heterogeneous computing systems
Journal of Parallel and Distributed Computing
Towards a general model of the multi-criteria workflow scheduling on the grid
Future Generation Computer Systems
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Multi-Criteria Scheduling of Pipeline Workflows (and Application To the JPEG Encoder)
International Journal of High Performance Computing Applications
Multiobjective differential evolution for scheduling workflow applications on global Grids
Concurrency and Computation: Practice & Experience - Special Issue: Advanced Strategies in Grid Environments
Energy-Aware Scheduling of Flow Applications on Master-Worker Platforms
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Multi-criteria Scheduling of Precedence Task Graphs on Heterogeneous Platforms
The Computer Journal
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Using a new event-based simulation framework for investigating resource provisioning in Clouds
Scientific Programming - Science-Driven Cloud Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Executing large-scale applications in distributed computing infrastructures (DCI), for example modern Cloud environments, involves optimization of several conflicting objectives such as makespan, reliability, energy, or economic cost. Despite this trend, scheduling in heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization problem. In this paper, we propose a generic multi-objective optimization framework supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The algorithm approximates the optimal solution by considering user-specified constraints on objectives in a dual strategy: maximizing the distance to the user's constraints for dominant solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-objective case study comprising makespan, economic cost, energy consumption, and reliability as optimization goals. We implemented our method as part of the ASKALON environment (Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real and synthetic simulation experiments that our algorithm outperforms related bi-criteria heuristics while meeting the user constraints most of the time.