面向跨数据中心网络的节点约束存储转发调度方法

借助海量数据中心存储,通过存储转发(store-and-forward,SnF)调度大数据传输,已被证明能有效解决跨数据中心间大数据传输难题.然而,多数现有调度方法将数据途经的所有网络节点(例如数据中心)均纳入SnF调度决策,导致其计算复杂度过高,难以为大规模网络提供实时调度服务.针对跨数据中心光网络场景,给出SnF模型,量化分析存储节点数量对调度问题性能与复杂度的影响.研究表明:在一定条件下,无需将所有节点都纳入调度决策也可获得良好的调度性能.由此,提出了节点约束SnF调度方法.该方法的特点在于:1)仅将部分数据途经节点纳入调度决策,降低调度问题求解难度;2)引入拓扑抽象,将被选节点间链路状态压缩,缩小调度问题规模、提高算法求解效率.仿真结果表明:在阻塞率和算法计算时间方面,该方法优于现有调度方法.

Node-Constraint Store-and-Forward Scheduling Method for Inter-Datacenter Networks

Performing store-and-forward(SnF)using abundant storage resources inside datacenters has been proven to be effective in overcoming the challenges faced by inter-datacenter bulk transfers.Most prior studies attempt to fully leverage the network infrastructure and maximize the flexibility of the SnF scheme.Their proposed scheduling methods hence aim at a full storage placement where all network nodes(e.g.,datacenters)are SnF-enabled and every node is taken into account in the scheduling process.However,the computational complexity of the prior methods exponentially increases with the network scale.As a result,the prior methods may become too complicated to implement for large-scale networks and online scheduling.In this work,based on the inter-datacenter optical network,SnF models are presented to quantify the impact of the number of SnF-enabled nodes on the performance and the complexity of the SnF scheduling problem.Our key findings show that taking a few SnF-enabled nodes into account in the scheduling process can provide high performance while maintaining low complexity under certain circumstances.It is unnecessary to take every node into account in the scheduling process.Therefore,a node-constraint SnF scheduling method is proposed,whose features are twofold:1)by taking a portion of nodes into account,it reduces the complexity of the SnF scheduling problem;2)by introducing a topology abstraction,it condenses the link states between the considered nodes and hence reduces the problem size,which improves its efficiency in solving the SnF scheduling problem.Simulations demonstrate that the proposed method outperforms the prior method in terms of blocking probability and computation time.