Simulation of big data balanced scheduling model in cloud computing environment

DOI

10.2991/icmemtc-16.2016.288How to use a DOI?

Keywords

cloud computing; big data; balanced scheduling

Abstract

in the process of the research on the modeling method of big data balanced scheduling, using the current algorithm to establish a big data balanced scheduling model, the data scheduling is easy to fall into local optimal solution, and there is a problem of big modeling error. To this end, a big data balanced scheduling modeling method based on cloud computing environment is proposed. In this way, the problem of big data balanced task scheduling in cloud computing environment is made formalized description. Through the formal derivation of the dynamic programming method, the heuristic priority allocation strategy of the earliest finish time is obtained. Based on this, using the improved genetic algorithm, the convergence rate of the optimal solution for large data equilibrium scheduling is accelerated. At the same time, with the dynamic heterogeneity of cloud computing environment, the fitness function is made optimization, and the search space of big data balanced scheduling is extended. Based on the optimal solution of the big data balanced scheduling, a big data balanced scheduling model is established. The simulation results show that the big data balanced modeling method based on cloud computing environment can effectively improve the efficiency of big data balanced scheduling and with strong robustness.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jinyi Zhou
PY  - 2016/04
DA  - 2016/04
TI  - Simulation of big data balanced scheduling model in cloud computing environment
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 1493
EP  - 1497
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.288
DO  - 10.2991/icmemtc-16.2016.288
ID  - Zhou2016/04
ER  -