No abstract

Nowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets.Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edgeassisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework LSTM-C based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by 20%∼32% in cache hit rate. We also show that the training and predicting time of one iteration are 8.6 ms and 300 µs on average respectively, which are fast enough for online operations.INDEX TERMS Edge-assisted caching replacement, intelligent content caching, long short term memory.

Background: Familial renal glucosuria is a rare renal tubular disorder caused by SLC5A2 gene variants. Most of them are exonic variants and have been classified as missense variants. However, there is growing evidence that some of these variants can be detrimental by affecting the pre-mRNA splicing process. Therefore, we hypothesize that a certain proportion of SLC5A2 exonic variants can result in disease via interfering with the normal splicing process of the pre-mRNA. Methods: We used bioinformatics programs to analyze 77 previously described presumed SLC5A2 missense variants and identified candidate variants that may alter the splicing of pre-mRNA through minigene assays. Results: Our study indicated six of 7 candidate variants induced splicing alterations. Variants c.216C > A, c.294C > A, c.886G > C, c.932A > G and c.962A > G may disrupt splicing enhancer motifs and generate splicing silencer sequences resulting in the skipping of exon 3. Variants c.305C > T and c.1129G > A probably disturb splice sites leading to exon skipping. Conclusion: To our knowledge, we report, for the first time, SLC5A2 exonic variants that produce alterations in pre-mRNA. Our research reinforces the importance of assessing the consequences for putative point variants at the mRNA level. Additionally, we propose that minigenes function analysis may be valuable to evaluate the impact of SLC5A2 exonic variants on pre-mRNA splicing without patients' RNA samples.

Learning-based Adaptive Bit Rate (ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it is still suffering from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% -16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.