An Adaptive Rescheduling Strategy for Grid Workflow Applications

Scheduling is the key to the performance of grid workflow applications. Various strategies are proposed, including static scheduling strategies which map jobs to resources before execution time, or dynamic alternatives which schedule individual job only when it is ready to execute. While sizable work supports the claim that the static scheduling performs better for workflow applications than the dynamic one, it is questioned how a static schedule works effectively in a grid environment which changes constantly. This paper proposes a novel adaptive rescheduling concept, which allows the workflow planner works collaboratively with the run time executor and reschedule in a proactive way had the grid environment changes significantly. An HEFT-based adaptive rescheduling algorithm is presented, evaluated and compared with traditional static and dynamic strategies respectively. The experiment results show that the proposed strategy not only outperforms the dynamic one but also improves over the traditional static one. Furthermore we observed that it performs more efficiently with data intensive application of higher degree of parallelism.

Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach

As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency. Pruning is one of the most popular network compression techniques. In this paper, we propose a novel unstructured pruning pipeline, Attention-based Simultaneous sparse structure and Weight Learning (ASWL). Unlike traditional channel-wise or weight-wise attention mechanism, ASWL proposed an efficient algorithm to calculate the pruning ratio through layer-wise attention for each layer, and both weights for the dense network and the sparse network are tracked so that the pruned structure is simultaneously learned from randomly initialized weights. Our experiments on MNIST, Cifar10, and ImageNet show that ASWL achieves superior pruning results in terms of accuracy, pruning ratio and operating efficiency when compared with state-of-the-art network pruning methods