Even though conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. In this paper, we first carry out large-scale measurement analysis by using the real-world service data from Huawei Cloud, a representative cloud-edge provider in China. We observe that the massive number of channels has proposed great burdens to the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we proposeAggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea ofAggCastis to aggregate some viewers that are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by integrating the useful insights obtained from our large-scale measurement,AggCastcan not only ensure that quality of experience (QoS) does not suffer degradation, but also satisfy the systematic requirements of CLS services.AggCasthas been A/B tested and fully deployed. The online and trace-driven experiments show that, compared to the most prevalent method,AggCastsaves over 16.3%back-to-source(BTS) bandwidth costs while significantly improving QoS (startup latency, stall frequency and stall time are reduced over 12.3%, 4.57% and 3.91%, respectively).

Conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. We observe that the massive number of channels has greatly burdened the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers who are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by leveraging the insights obtained from our large-scale measurement, AggCast can not only ensure quality of experience (QoS), but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed in a top cloud-edge provider in China for over eight months. The online and trace-driven experiments show that, compared to the common practice, AggCast can save over 15% back-to-source (BTS) bandwidth costs while having no negative impacts on QoS.

The discovery of gene-disease associations is important for the prevention, diagnosis and treatment of diseases. The studies on gene-disease associations have produced diverse data, which can facilitate the gene-disease association prediction. Integrating diverse information is critical for developing high-accuracy prediction models. In this paper, we propose a heterogeneous network-based method that enhances gene-disease association prediction by using graph embedding and ensemble learning, abbreviated as “HNEEM”. A heterogeneous network is constructed based on gene-disease associations, gene-chemical associations and disease-chemical associations, to combine diverse information. The network uses genes, diseases and chemicals as nodes, and uses their associations as edges. The graph embedding methods are utilized to extract representation vectors of nodes in the heterogeneous network, and the feature vectors of genes and diseases are merged to represent gene-disease pairs, and the random forest is employed to build the prediction model based on gene-disease pairs. We consider six types of graph embedding methods, and take the individual graph embedding method-generated features to build prediction models and use them as base predictors, and then combine base predictors to develop the ensemble learning model HNEEM. We comprehensively compare different graph embedding methods, and results demonstrate that the graph embedding methods produce satisfying results in the gene-disease association prediction, and integrating different graph embedding methods can make further improvements. In computational experiments, HNEEM produces better results compared to the state-of-the-art gene-disease perdition methods, and HNEEM is robust to the data richness as well. Moreover, the usefulness of the proposed method HNEEM is validated by the case studies. In conclusion, HNEEM is a promising method for predicting gene-disease associations.