Efficient implementation of the first-fit strategy for dynamic storage allocation
ACM Transactions on Programming Languages and Systems (TOPLAS)
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Real-time task scheduling by multiobjective genetic algorithm
Journal of Systems and Software
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Genetic algorithms for task scheduling problem
Journal of Parallel and Distributed Computing
List scheduling with duplication for heterogeneous computing systems
Journal of Parallel and Distributed Computing
Reliability-aware scheduling strategy for heterogeneous distributed computing systems
Journal of Parallel and Distributed Computing
On cluster resource allocation for multiple parallel task graphs
Journal of Parallel and Distributed Computing
Feedback Dynamic Algorithms for Preemptable Job Scheduling in Cloud Systems
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Research on the resource monitoring model under cloud computing environment
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Future Generation Computer Systems
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
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In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem.In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio.At first, the HSGA algorithm makes tasks prioritization in complex graph considering their impact on others, based on graph topology. This technique is efficient to reduction of completion time of application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment.Finally, the proposed algorithm presents the better results with increasing number of tasks in application graph in contrast with other studied algorithms.