Name
Title | ||
|---|---|---|
Tuning Genetic Algorithms for Resource Provisioning and Scheduling in Uncertain Cloud Environments: Challenges and Findings |
Abstract | ||
|---|---|---|
Cloud computing allows users to devise cost-effective solutions for deploying their applications. Nevertheless, the decisions about resource provisioning are very challenging because workloads are seriously affected by the uncertainty of cloud performance and their characteristics vary. In this paper we address these issues by explicitly modeling workload and cloud uncertainty in the decision process. For this purpose, we adopt a probabilistic formulation of the optimization problem aimed at minimizing the expected cost for deploying a parallel application under a deadline constraint. To find a sub-optimal solution of the problem we apply a Genetic Algorithm. By tuning its parameters we are able to assess their role and their impact on the effectiveness and efficiency of the algorithm for provisioning and scheduling in uncertain cloud environments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMPDP.2019.8671564 | 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) |
Keywords | Field | DocType |
Task analysis,Genetic algorithms,Sociology,Statistics,Cloud computing,Optimal scheduling | Task analysis,Workload,Scheduling (computing),Computer science,Provisioning,Probabilistic logic,Optimization problem,Genetic algorithm,Cloud computing,Distributed computing | Conference |
ISSN | ISBN | Citations |
1066-6192 | 978-1-7281-1644-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Carla Calzarossa | 1 | 70 | 11.31 |
| Luisa Massari | 2 | 104 | 11.19 |
| Giuseppe Nebbione | 3 | 0 | 0.34 |
| Marco L. Della Vedova | 4 | 8 | 1.57 |
| Daniele Tessera | 5 | 123 | 14.97 |