Nowadays, optical circuit switching is becoming an increasingly favored technology in scaling data center networks for its definitive advantages in data rate, power consumption, and device cost. Concurrently, reducing coflow completion time (CCT) is of great significance for improving application-level performance. However, minimizing CCT in circuit switched networks is totally different from that in traditional packet switched networks due to port constraints and circuit reconfiguration delays. To address this issue, this article proposes Grouped Optimization-based Scheduling (GOS), a CCT minimization algorithm for circuit switched networks integrating circuit and coflow scheduling. We first formalize the CCT minimization problem into a 0-1 programming problem, then relax and solve the problem in 2 steps to obtain the coflow order and flow grouping decisions on each circuit. Thus intra-group reconfiguration delays are saved, and small coflows can be prioritized at the group level. Theoretical analysis proves GOS is a 4-approximation algorithm in average CCT. To reduce computing overheads, we further propose a heuristic approximation algorithm. Extensive simulations show that the heuristic algorithm has satisfactory CCT performance (0.12× Varys, 0.36× Sunflow) as well as high throughput (16.74× Varys, 1.32× Sunflow), and well adapts to a wide range of reconfiguration delays and algorithm decision time.

中文翻译：

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最小化光电路交换网络中的 Coflow 完成时间

如今，由于其在数据速率、功耗和设备成本方面的决定性优势，光电路交换正成为扩展数据中心网络的越来越受欢迎的技术。同时，减少协流完成时间（CCT）对于提高应用程序级性能具有重要意义。然而，由于端口限制和电路重新配置延迟，在电路交换网络中最小化 CCT 与在传统分组交换网络中的 CCT 完全不同。为了解决这个问题，本文提出了基于分组优化的调度（GOS），这是一种用于集成电路和协流调度的电路交换网络的 CCT 最小化算法。我们首先将 CCT 最小化问题形式化为 0-1 规划问题，然后放松并分 2 步解决问题，以获得每个电路上的 coflow 顺序和流分组决策。因此，节省了组内重新配置延迟，并且可以在组级别优先考虑小协流。理论分析证明GOS是平均CCT中的4-近似算法。为了减少计算开销，我们进一步提出了一种启发式逼近算法。大量仿真表明，启发式算法具有令人满意的CCT性能（0.12×Varys，0.36×Sunflow）和高吞吐量（16.74×Varys，1.32×Sunflow），并且很好地适应了广泛的重构延迟和算法决策时间。理论分析证明GOS是平均CCT中的4-近似算法。为了减少计算开销，我们进一步提出了一种启发式逼近算法。大量仿真表明，启发式算法具有令人满意的CCT性能（0.12×Varys，0.36×Sunflow）和高吞吐量（16.74×Varys，1.32×Sunflow），并且很好地适应了广泛的重构延迟和算法决策时间。理论分析证明GOS是平均CCT中的4-近似算法。为了减少计算开销，我们进一步提出了一种启发式逼近算法。大量仿真表明，启发式算法具有令人满意的CCT性能（0.12×Varys，0.36×Sunflow）和高吞吐量（16.74×Varys，1.32×Sunflow），并且很好地适应了广泛的重构延迟和算法决策时间。