An emerging architecture for software-defined data centers and WANs is the network fabric, where complex applicationsensitive functions are factored out, leaving the network itself to provide a simple, robust high-performance data delivery abstraction. This requires performing route optimization, in real time and across a diverse choice of paths. A large variety of techniques have been proposed to provide path diversity for network fabrics. But, running up against the constraint of forwarding table size, these proposals are topology-dependent, complex, and still only provide limited path choice which (we show) can impact performance.We propose a simple approach to realize the vision of a flexible, high-performance fabric: the network should expose every possible path, allowing a controller or edge device maximum choice. To this end, we observe that source routing can be encoded and processed compactly into a single field, even in large networks, with OpenFlow 1.3. We show that, in addition to the expected decrease in required forwarding table size, source routing supports optimal throughput performance, in some cases significantly higher than some past proposals. We thus believe source routing offers a clean abstraction and efficient implementation for future network fabrics.
After more than two decades of evolution, TCP and its end host based modifications can still suffer from severely degraded performance under real-world challenging network conditions. The reason, as we observe, is due to TCP family's fundamental architectural deficiency, which hardwires packet-level events to control responses and ignores emprical performance. Jumping out of TCP lineage's architectural deficiency, we propose Performanceoriented Congestion Control (PCC), a new congestion control architecture in which each sender controls its sending strategy based on empirically observed performance metrics. We show through preliminary experimental results that PCC achieves consistently high performance under various challenging network conditions.
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