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Data centers need low-latency fabrics. Several flow scheduling schemes have been proposed to minimize the Flow Completion Time (FCT) based on Shortest Job First (SJF) heuristic. However, to mimic SJF, previous proposals sacrifice the generality (e.g., pFabric requires special hardware) or sacrifice the performance to guarantee the generality (e.g., PIAS loses some of pFabric's performance). Especially, in multi-tenant data centers, traffic patterns from different applications are mixed together and vary over time, thereby creating even more challenges. In this paper, we investigated that the performance of information-agnostic scheme could be further improved by leveraging the unique characteristics of different traffic types. Based on this investigation, we present Traffic Prediction based Flow Scheduling (TPFS), aiming at achieving near-optimal performance and good generality in multi-tenant data centers with the mix-traffic pattern. To achieve near-optimal performance, we design a two-stage machine learning algorithm to first automatically cluster flows with the similar flow size distribution and then predict the priorities of flows based on the clustering results. Besides, we implement TPFS in virtual switches, which exerts fine-grained flow scheduling over the arbitrary network stacks of tenants. Testbed evaluation and simulations show that TPFS outperforms the previous information-agnostic flow scheduling scheme PIAS and greatly reduces the tail latency of the network.

Recently, Explicit Congestion Notification (ECN) has been leveraged by DCTCP and its successive protocols to reduce the latency in datacenters. However, we find that DCTCP may induce the queue underflow and high latency in multi-queue scenarios and even single-queue environments. Our analysis concludes that the problem is caused by improper ECN marking thresholds of DCTCP. In this paper, an Adaptive ECN (A-ECN) marking scheme is proposed to enhance the performance of the ECN marking scheme of DCTCP.A-ECN is designed to minimize queue length as well as keeping high link utilization. More importantly, A-ECN can adaptively adjust ECN marking thresholds in different scenarios to achieve good generality. Therefore, network operators can directly deploy A-ECN in various environments regardless of underlying bandwidth, packet schedulers and the number of concurrent flows. At last, the testbed evaluation results show that A-ECN can minimize queue length and maintain high throughput. The flow completion times (FCT) for short flows could be reduced by up to 40% compared to the ECN marking scheme of DCTCP.

With the rapid development of information technology, the name ambiguity problem has become one of the primary issues in the fields of information retrieval, data mining, and scientific measurement. Name disambiguation is used to promote computer technology and big data information, which maps virtual relational networks to real social networks to solve the problem that the same name points to multiple entities. At present many literature search platforms launched their respective scholar system, name ambiguity problem will inevitably affect the precision of other information calculations, reduce the credibility of the system, and affect the information quality and content quality. Most work deals with this issue by using graph theory and clustering. However, the name disambiguation problem is still not well resolved. In this paper, we propose a multi-level name disambiguation algorithm. This algorithm is mainly based on the unsupervised algorithm, which combines hierarchical agglomerative clustering (HAC) and graph theory for disambiguating. The experimental results show that the proposed solution achieves clearly better performance (+17 ~ 25% in terms of F1-Measure) than several methods, including HAC and Graph.

In named data networking (NDN), content store (CS) is proposed to provide on-path cache service. When user's request with content name is forwarded to NDN node, exact match in CS is carried out first. Under moderate cache hit ratio, most requests result in mismatch in CS searching process, which causes large overhead to the packet forwarding, and the overhead would rise as the scale of CS increases. In this paper, request filter of CS is studied and Compression Trie-based Bloom Filter (CT-BF) is proposed. CT-BF takes advantage of on-chip Bloom Filter to quickly filter out mismatch requests, and Compression Trie is adopted to accommodate the large CS name Trie into space-limited on-chip Bloom Filter. Optimal Compression Trie under space constraint is discussed for the first time and a heuristic approach Adapted Compression Trie based Bloom Filter (ACT-BF) is proposed for on-line operation. Simulation results show that ACT-BF can efficiently filter out mismatch requests with given on-chip space constraint and hence reduce average CS search delay.