Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. Note to Practitioners—Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.

As an emerging computing paradigm, edge computing offers computational resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computational resources with different configurations to clients in turn. Clients independently choose which computational resources to rent and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without infringing on clients’ privacy. Experimental results show that the revenue of ECSP in Egret is only 1.29% lower than Oracle and 23.43% better than the state-of-the-art when the client arrives dynamically.

BACKGROUND: Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown. RESULTS: We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3' and 5' ends of genes are primary drivers of these differences between populations. CONCLUSION: Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups.

In modern seismic data acquisition, real-time data collection is a challenging task due to bandwidth limitations in wireless communications. In this letter, we propose a novel compressive data gathering scheme using generative adversarial networks, named GAN-CDG, to improve the efficiency of data gathering. Instead of collecting the originally acquired data, GAN-CDG gathers data projections in wireless geophone networks. Data compression and load-balanced relay transmission are utilized during the projection process. To speed up the formation of projections, the shortest path routing tree (SPRT) is constructed, which achieves the minimum end-to-end time delay. The sparse domain of seismic signals and its reconstruction mapping are learned by sparsity-constrained adversarial networks. The testing results demonstrate that projections with high compression ratios (e.g., 16) are gathered efficiently with the SPRT. Then, original seismic signals can be reconstructed accurately (over 30 dB) from the projections using the adversarial model, which outperforms the state-of-the-art method.