Mobile Edge Computing(MEC) is a new computing approach in which computation tasks carried by Mobile Devices (MD) can be offloaded to MEC servers or computed locally. Since the mobile devices are always battery limited and computation tasks have strict deadlines, how to schedule the execution of each task energy effectively is important. Comparing with existing works, we consider a much more complexed scenario, in which multiple moving mobile devices sharing multiple heterogeneous MEC servers, and a problem named as Minimum Energy Consumption problem in Deadline-aware MEC system is formulated. Such problem is proved to be NP-Hard, and two approximation algorithms are proposed focusing on single and multiple MD scenarios, respectively. The performances of these algorithms are varied by theoretical analysis and simulations.

The past decade witnessed the dramatic evolution from Quality of Service (QoS) to Quality of Experience (QoE) in the design of wireless networks, especially on the aspect of link scheduling. In many applications, end users are concerned more about transmission quality of an individual task rather than the quality of a link, where a task may refer to a piece of music, video, etc. and may include many packets. This paper proposes a new network model aiming at improving user experience by pushing the scheduling problem to the task layer. A novel QoE requirement is designed to generalize the QoS requirements of a task, which is the ratio requirement. Following this design, a corresponding scheduling policy is proposed to capture it for each task and then reach an application-aware transmission allocation. We theoretically analyze the performance of the scheduling policy, and discuss the design of an optimal solution and the impact of the QoE requirements. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE.

Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top. Supplementary data are available at Bioinformatics online.