Supporting Dynamic Relocation of Video Data in Disk-Array-Based Video Servers

Abstract

Although modification operations on multimedia data such as insertion and/or deletion of video clips are rare, they may cause significant degradation of disk I/O performance in disk-array-based video servers. This is because many existing disk-array-based video servers consider only video playback at normal speed and retrieval-oriented VCR-like operations such as fast-forward and rewind without considering relocation of video data. Most conventional video servers provide optimal performance using static placement of video data on multiple disks. In this paper, we propose a method called 'SRM (Segment Relocation Method)' which provides uniform loadbalance of disks regardless of the dynamic modification of stored video objects. For modification operations, the segments belonging to the corresponding 'round' of disk placement are relocated so that the system guarantees uniform load-balance of multiple disks in all rounds. To justify the significance of the relocation scheme, we evaluate and compare the cost of retrieval in terms of the required buffer size and the number of admitted users.

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

“…genres of movies) to be added based on the new state-of-the-art model BERT4REC, which improves the sequential recommending task. More recently, graph embedding techniques were employed to profile the rich interactions between users and items [36,42].…”

“…Recently, different neural networks – that is, RNN, convolutional neural network (CNN) and self-attention network (SAN) – have been successfully applied to sequential recommendation. RNN and its variants – LSTM and gated recurrent unit (GRU) – are most widely used in sequential recommendation with their capability of capturing the sequential dependencies [34–36]. Wei et al [34] proposed a sequential recommender system called LANCR, which combines LSTM to capture users’ long-term interests and self-attention mechanism to profile the user’s short-term intentions for user next-click recommendation.…”