发表机构
Virginia Tech; Shenzhen Graduate School, School of Electric and Computer Engineering, Peking University（弗吉尼亚理工大学; 北京大学深圳研究生院电气与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MAIDS调度算法，用于最小化RLNC编码多消息流的加权解码延迟，在特定条件下证明最优，并仿真验证其性能优于基线。
AI 中文摘要
本文研究了多个RLNC编码消息流在目的地竞争有限处理能力时的加权解码延迟最小化问题。数据包到达是外生的，而调度器仅决定目的地已可用数据包的处理顺序。一种基于轨迹条件的离线调度公式表明，即使使用单个处理单元，批量释放子类也是强NP难的。随后，开发了消息感知创新赤字调度（MAIDS），根据每个可服务消息的权重和剩余解码赤字对其进行优先级排序。对于单个处理单元，MAIDS在非阻塞渐进到达且权重相等的情况下，以及在共同激活且权重为任意正数的情况下，被证明是精确最优的，而无限制的加权在线问题则不存在通用的确定性$O(1)$竞争比。在流式和批量基准上的仿真结果表明，MAIDS相对于测试的基线持续降低了加权解码延迟，平均而言接近离线最优值，并恢复了预测的精确性能边界。
英文摘要
This paper studies weighted decoding-delay minimization for multiple RLNC-coded message streams that compete for finite processing capacity at a destination. Packet arrivals are exogenous, while the scheduler only determines the processing order of packets already available at the destination. A trace-conditioned offline scheduling formulation shows that a batch-release subclass is strongly NP-hard even with a single processing unit. Message-Aware Innovation-Deficit Scheduling (MAIDS) is then developed to prioritize each serviceable message according to its weight and remaining decoding deficit. For a single processing unit, MAIDS is shown to be exactly optimal under nonblocking progressive arrivals with equal weights and under common activation with arbitrary positive weights, while the unrestricted weighted online problem admits no universal deterministic $O(1)$ competitive ratio. Simulation results on streaming and batch benchmarks show that MAIDS consistently reduces weighted decoding delay relative to the tested baselines, remains close to the offline optimum on average, and recovers the predicted exact performance boundaries.
