Packet-level load balancing has shown its massive potential for long in utilizing super high bisection bandwidth of data center network (DCN). This kind of potential, however, has still not been completely transformed into huge performance enhancement of data transmission. The fundamental reason is that packet-level load balancing can fully utilize the parallel paths of underlying physical network, but suffer from the problem of packet disordering transmission, which greatly impairs the flow-level transmission performance of DCN. This paper explores the root cause of performance impairment generated by packet disordering transmission, and proposes$R^{3}$, a solution focusing on “recognizably releasing redundant acknowledgements” as a building block for data center packet-level load balancer. In$R^{3}$'s heart, the source leaf switch perceives the global packet loss information and selectively intercepts the redundant acknowledgement packets, thus avoiding the TCP-driven end-host from experiencing frequent window reductions and unnecessary packet retransmissions. Experimental results of numerous simulation tests and real implementations show that, after integrating$R^{3}$into the representative data center packet-level load balancing schemes, the transmission performances of both delay-sensitive and throughput-oriented data center flows are significantly improved. Furthermore,$R^{3}$is merely implemented by switch, leaving the end hosts and the deployed load balancing scheme totally unchanged.

Modern data center network often possesses multiple end-to-end parallel paths, which undertake the crucial task of transmitting vast heterogeneous data traffic generated by a wide variety of applications. To fully utilize the offered super high bisection network bandwidth thus benefiting application performance, many data center load balancing schemes are proposed to improve path utilization for avoiding network congestion hot-spot. However, these schemes are naturally agnostic to data center traffic pattern and the diverse requirements on flow-transmission, leading to the sub-optimal network transmission performance. To address this issue, this paper presents a new data center load balancing scheme, called PCLB, which selectively generates Clone Packets by considering both flow-transmission phases and path states, thereby helping different types of flows choose more appropriate paths for speeding up their data transmission. Experimental results of numerous NS2 simulations show that PCLB significantly reduces the average and tail flow completion time for delay-sensitive flows, while the performance of throughput-oriented flows can be always maintained at high level.

With the rapid growth of the internet and multimedia technology, there is an exponential growth of copy video, which causes some certain impact on video retrieval and copyright protection. Therefore, it becomes increasingly important to find copy videos and locate subsequent clips in a large-scale video database. In this paper, an efficient method is proposed to solve the current problem of video copy detection when the test video contains both copy video clips and non-copy video clips. This paper presents a method of judging the video copy sub-sequence based on the distance between the test video keyframe and the reference video keyframe. First, AlexNet is used to extract the features of the keyframe. Second, it judges whether each test video keyframe is a copy frame according to the distance. Then, the location of the copy sub-sequence is determined by finding the continuous copy frame, and then the video location of the copy sub-sequence is determined. Experimental results show that the proposed method can achieve 86.29% in recall and 95.71% in precision.

Anchorperson shot detection is an effective way of news video structuring. According to some features of anchorperson shots, such as visually similar with each other, long duration, and large span, etc., an anchorperson shot detection method based on clustering called AnchorClu is proposed. After fast clustering of shots’ key frames, some clusters including the anchorperson cluster can be got. Then by some proper rules, the anchorperson cluster is selected out, and the anchorperson shots can also be extracted to help the analysis of news video structuring. Experiments prove the effectiveness of the method, which is fast and gets the average recall of 93% and the average precision of 98%.