Improved Ant Colony Scheduling Algorithm for High-score Data Storage Model Based on Cloud Platform
- Peng Zhang
- Corresponding Author
- Peng Zhang
Available Online June 2017.
- https://doi.org/10.2991/ammee-17.2017.137How to use a DOI?
- Cloud Platform, Ant Colony, QoS.
- In order to find the optimal scheduling, to ensure the global optimization, and to adapt to the dynamic environment, the adaptive capacity remote sensing data cloud platform scheduling strategy is proposed based on the cloud platform high score data storage model. The main contents are as follows. The characteristics of the task of high score data in cloud environment are analyzed, and the applicability of ant colony algorithm in task scheduling of cloud computing platform is pointed out. The initial set of ant colony algorithm is optimized by combining the two parts. The quality of service (QoS) is used as the ant colony algorithm pheromone, and the ant colony algorithm scheduling strategy with QoS and double pheromone updating model are designed. It can be observed that in the process of dealing with large-scale data tasks, the traditional ACO algorithm needs to be further considered node load balancing problem. And the scheduling algorithm to node QoS is as a pheromone, the resource node reliability, response time and load rate are taken into account as it is in task scheduling, which makes the node with better performance easy to be selected. It can be concluded that this strategy can improve the traditional ant colony algorithm and get the local optimal solution. At the same time, the load balance of the resource node is improved to a certain extent.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Peng Zhang PY - 2017/06 DA - 2017/06 TI - Improved Ant Colony Scheduling Algorithm for High-score Data Storage Model Based on Cloud Platform BT - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017) PB - Atlantis Press UR - https://doi.org/10.2991/ammee-17.2017.137 DO - https://doi.org/10.2991/ammee-17.2017.137 ID - Zhang2017/06 ER -