论文标题

部分可观测时空混沌系统的无模型预测

A Modeling of TSRCG and Resource Optimization for Multi-task Delivery Guarantee Algorithm Based on CGR Strategy in LEO Satellite Network

论文作者

Sun, Xue, Li, Changhao, Yan, Lei, Cao, Suzhi

论文摘要

随着卫星成本的降低和加工能力的增强,低地球轨道(LEO)卫星星座可以独立地建立卫星间网络,而无需依赖受地理位置分布的传统地面站的限制,并且可以建立卫星间链接(ISLS)(ISLS)和完整的计算和整个计算和电流。频繁开关ISL的特征,卫星网络的高度动态网络拓扑使其面临着路由策略设计的挑战,即延迟/中断耐受性网络(DTN)。作为确定性的动态路由算法,触点图路由(CGR)使用联系计划来计算路径和正向数据,但它仍然存在诸如高计算开销,低预测准确性诸如忽略队列延迟引起的低预测准确性以及由有限缓存引起的过多预订的问题。因此,我们首先从时空资源触点图(TSRCG)开始,以准确表征卫星网络和多任务下的网络资源参数的时间变化和可预测的特征。然后,我们优化了路由列表计算和动态路线计算过程,以确保任务交付并减少各种资源的消耗,例如联系人容量,计算资源和存储资源。以及基于CGR(RMDG-CGR)策略的多任务交付保证算法的资源优化与ION 4.0.1中的标准CGR进行了比较。最后,仿真结果表明,RMDG-CGR可以提前实现更高的任务交付,并成功完成任务交付率,节省联系量占用率,计算和存储资源,并且上述效果更加突出,尤其是在临界捆绑包的任务情况下。

With the reduction of satellite costs and the enhancement of processing capabilities, low earth orbit (LEO) satellite constellations can independently build inter-satellite networks without relying on traditional ground stations restricted by geographical distribution and can establish inter-satellite links (ISLs) and complete computing and routing on-board. The characteristics of frequent on-off ISLs, the highly dynamic network topology of satellite networks make it face the challenges of routing strategy design as a delay/interruption tolerant network (DTN). As a deterministic dynamic routing algorithm, contact graph routing (CGR) uses a contact plan to calculate the path and forward data, but it still has problems such as high computational overhead, low prediction accuracy caused by ignoring queue delay, and overbooked problem caused by limited cache. Therefore, we first start with the time-space resource contact graph (TSRCG) to accurately characterize the time-varying and predictable characteristics of the satellite network and the network resource parameters under multi-tasks. Then, we optimize the route-list computation and dynamic route computation process to ensure task delivery and reduce the consumption of various resources, such as contact capacity, computing resources, and storage resources. And the resource optimization for the multi-task delivery guarantee algorithm based on CGR (RMDG-CGR) strategy we propose is compared with standard CGR in ION 4.0.1. Finally, the simulation results show that the RMDG-CGR can achieve higher task delivery in advance and successful task delivery rate, save contact volume occupancy rate, computing and storage resource, and the above effects are more prominent, especially in the task scenario with critical bundles.

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