云环境下蚁群优化算法的视频点播视频流任务调度策略

Video-on-demand video stream scheduling policy based on ant colony optimization algorithm under cloud environment

针对云环境下大规模并发视频流调度过程中资源利用率低和负载不均的问题,提出一种基于蚁群优化(ACO)算法的视频点播(VOD)集群视频流任务调度策略VodAco。在分析视频流期望性能与服务器空闲性能的相关性、定义综合性能匹配度的基础上,建立数学模型,并采用蚁群优化思路进行最佳调度方案搜索。通过云仿真软件CloudSim实验表明,与轮询(RR)、贪婪(Greedy)算法相比,所提算法在任务完成时间、平台资源占有率、各节点性能负载均衡指标上具有较为明显的优势。

Concerning the large-scale concurrent video stream scheduling problem of low resource utilization and load imbalance under cloud environment, a Video-on-Demand（ VOD） scheduling policy based on Ant Colony Optimization（ ACO）algorithm named VodAco was proposed. The correlation of video stream expected performance and server idle performance was analyzed, and a mathematical model was built based on the definition of comprehensive matching degree, then ACO method was adopted to hunt the best scheduling schemes. The contrast experiments with Round Robin（ RR） and greedy schemes were tested on CloudSim. The experimental results show that the proposed policy has more obvious advantages in task completion time, platform resources occupancy and node load balancing performance.