Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan and improving the utilization of cloud computing resources. INDEX TERMS Cloud computing, task duplication, workflow scheduling, resource provision, heuristic mechanism.

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Facial expression recognition computer technology can obtain the emotional information of the person through the expression of the person to judge the state and intention of the person. The article proposes a hybrid model that combines a convolutional neural network (CNN) and dense SIFT features. This model is used for facial expression recognition. First, the article builds a CNN model and learns the local features of the eyes, eyebrows, and mouth. Then, the article features are sent to the support vector machine (SVM) multiclassifier to obtain the posterior probabilities of various features. Finally, the output result of the model is decided and fused to obtain the final recognition result. The experimental results show that the improved convolutional neural network structure ER2013 and CK+ data sets’ facial expression recognition rate increases by 0.06% and 2.25%, respectively.