摘要
针对遥感影像数据量大,多用户并发请求造成服务器负载加重,使遥感影像传输效率逐渐降低的问题,提出一种在多线服务器环境下分块调度遥感影像资源的策略。该策略采用改进的蚁群优化(IACO)算法,通过引入一个线路等待因子γ动态选择当前最优的线路进行传输,从而提高传输效率。对IACO、ACO、Max-min、Min-min和Random算法进行了对比实验,IACO算法在客户端的任务完成时间和服务器端的执行时间与其他算法相比均是最少的,且随着任务数目的增加,效果更明显;同时IACO算法的线路资源的利用率也更高。仿真结果表明:多线服务器分块调度策略与改进蚁群算法相结合,使遥感影像传输速度和线路资源利用率均有一定提高。
A block resource scheduling strategy for remote sensing images in multi-line server environment was proposed with the problems of huge amount of remote sensing data, heavy server load caused by multi-user concurrent requests which decreased the transmission efficiency of remote sensing images. To improve the transmission efficiency, an Improved Ant Colony Optimization（ IACO） algorithm was used, which introduced a line waiting factor γ to dynamically select the optimal transmission lines. Intercomparison experiments among IACO, Ant Colony Optimization（ ACO）, Max-min, Min-min, and Random algorithm were conducted and IACO algorithm finished the tasks in the client and executed in the server with the shortest time, and the larger the amount of tasks, the more obvious the effect. Besides, the line resource utilization of IACO was the highest. The simulation results show that： combining multi-line server block scheduling strategy with IACO algorithm can raise the speed of remote sensing image transmission and the utilization of line resource to some degree.
出处
《计算机应用》
CSCD
北大核心
2014年第11期3210-3213,共4页
journal of Computer Applications
基金
国家863计划项目(2012AA12A405)
国家自然科学基金资助项目(61172144)
关键词
遥感
资源调度
蚁群优化算法
多线服务器
资源利用率
remote sensing
resource scheduling
Ant Colony Optimization（ACO） algorithm
multi-line server
resource utilization
