Entanglement routing plays a vital role in supporting various applications in quantum networks. Existing works on entanglement routing either ignored the Entangled Photon Source (EPS) placement issue or simply assumed a pool of EPSes at a centralized location that can provision entanglement over arbitrary quantum links. In this paper, we propose LIGHTER and fidelity-aware LIGHTER (named F-LIGHTER) to solve the joint EPS placement and entanglement routing problem based on the assumption that EPSes are co-located with quantum nodes and each EPS can send one entangled photon at a time to one of its adjacent nodes only. The salient features of LIGHTER and F-LIGHTER include (i) LIGHTER and F-LIGHTER use a demand-agnostic EPS placement scheme to maximize network throughput and fairness for all feasible Entanglement Connection EC) establishment demands, and (ii) most requested ECs can be established over Entanglement Paths (EPs) determined offline, and only a small percentage of them will be established over online calculated EPs, resulting in fast and efficient entanglement routing. Extensive simulations show that compared with schemes without proper EPS placement or entanglement routing, LIGHTER can improve the network throughput by up to 175.6% and 37.0%, respectively. When the fidelity is considered, the network throughput improvement achieved by F-LIGHTER will be up to 135.0% and 21.5%, respectively.

Remote Direct Memory Access (RDMA) has been widely deployed in datacenters for its high performance. Large-scale high performance cloud services built on geographically distributed datacenters require long-range RDMA for performance requirements. However, existing RDMA solutions can hardly satisfy the stringent requirements of the emerging large-scale high-performance cloud services built on geo-distributed datacenters in terms of throughput and delay. On the one hand, lossless RDMA suffers from a deep buffer and potential suboptimal throughput for inter-datacenter traffic due to delayed response to Priority Flow Control (PFC) messages. On the other hand, lossy RDMA with selective retransmissions suffers from poor performance when multiple flows with different round-trip times (RTTs) coexist in cross-datacenter scenarios. This article proposesSwing, which expands the high-performance lossless RDMA to long-distance links through PFC-Relay.Swingensures the throughput of long-distance links while minimizing the buffer requirement for long-range RDMA. It enables long-range RDMA without making any modifications to existing in-datacenter networks. The evaluation shows thatSwingcan reduce the average flow completion time (FCT) by 14%-66% in a variety of traffic scenarios.

Campus networks are widely deployed for organizations like universities and large companies. Applications and network-based services require campus networks to guarantee short queue and provide low latency and large bandwidth. However, the widely adopted packet-loss-based congestion control mechanism in client hosts builds up long queues in the switch buffer, which is prone to packet loss in burst scenarios, resulting in great network delay. Therefore, a scheme for efficiently controlling queue length of shallow buffer switches in campus networks is urgently needed. Explicit Congestion Notification(ECN) as an explicit feedback mechanism is widely adopted in data center networks to build lossless networks. In this paper, we propose Clean, an efficient queue length control scheme based on transparent ECN-proxy for campus networks. Clean is able to exert fine-grained control over arbitrary client TCP stacks by enforcing per-flow congestion control in the access point(AP). It allows the campus network switches to maintain a low queue length, resulting in high throughput, low latency and zero packet loss. Evaluation results demonstrate that Clean reduces the maximum queue length of the switch by 86% and reduces the 99th percentile latency by 85%. Clean also achieves zero packet loss in burst scenarios.

Machine slot-time spent on data transmission has direct impact on average job completion time (JCT). In this paper, we propose Macroflow, a networking abstraction that can capture the primitive scheduling granularity of machine slot-time. We demonstrate that minimizing machine slot-time is equivalent to minimizing the average macroflow completion time (MCT). We prove that minimizing MCT to be strongly NP-hard and focus on developing effective heuristics. We propose the Smallest-Macroflow-First (SMF) and Smallest-Average-Macroflow-First (SAMF) heuristics that greedily schedule macroflows based on their network footprint. To work with existing commodity switches, priority discretization is performed to classify macroflows into a small number of priority queues.