No abstract

No abstract

No abstract

No abstract

No abstract

No abstract

Nowadays, scientists are dealing with large-scale scientific workflows that need a high processing capacity platform to facilitate on-time completion. Cloud computing is the ideal platform to overcome this problem as it has several resources that scientists may choose from depending on the size of their applications. However, using cloud computing requires some monetary charges. Recently cloud computing providers started a new pricing schema that offers to their users a set of resources with specific combinations of CPU frequency configurations settings and price. The selected configurations settings reflect energy consumption. Besides, the configuration selection to meet users' satisfaction (minimum cost) and providers' satisfaction (energy saving) is crucial. Therefore, a multiobjective (cost and energy) efficient mechanism is essential. In this article, we address an important novel problem concerning multiobjective deadline constrained workflow scheduling in the cloud. We first study the relationship between cost minimization and minimization of the energy consumption in a cloud environment, and then discuss, develop, and propose an algorithm with two variants to help the system satisfy both sides (users and providers) at the same time during the selection of the configuration. The proposed heuristic is evaluated using specified real-world applications. The observed results indicate that our heuristic can reduce significantly the energy consumption and the cost at the same time. K E Y W O R D Scloud computing, cost-efficient workflow scheduling, deadline constrained workflow scheduling, energy efficient workflow scheduling, multicriteria workflow scheduling, multiobjective workflow scheduling, weighted sum for workflow scheduling INTRODUCTIONLarge-scale workflow-based applications appear in most of every scientific experiment. They are presented with many interdependent tasks, computing dependencies, and data flows. Workflow scheduling is the process of mapping workflow tasks to the available computing resources for the satisfaction of some constraints. Among distributed systems, Cloud computing has been witnessed to be the applicable environment to handle the computation requirements of large-scale workflow-based applications. Cloud computing is the lasted generation of the distributed system like grid computing, cluster computing, and parallel computing, which offers to its user a resizable computing resource, storage, and bandwidth on a pay

In this paper, we propose a novel semi-supervised method based on two branch autoencoder (TBAE) for hyperspectral images. A branch is classification function, and another branch is decoder function. Both of functions promote each other in training process. The encoder is fit for both classifier and decoder. The encoder and the decoder guide feature extraction from unlabeled samples. The encoder and the classifier guide classification for all labeled samples. We can get more information from unlabeled samples for classification. Finally, we use convolutional autoencoder (CAE) to extend TBAE and obtain TBCAE. Compared with ANN and CNN, TBAE and TBCAE have a better performance in the case of a small number of labeled samples. Use different datasets to verify these methods and print visualization of hidden layer and the reconstructed data. The results demonstrate that the proposed framework obtains credible results with a small number of labeled samples.