Despite their frequent use in our daily life, the expansion and reduction of images are among the least studied research areas in the literature. That is why the present research paper undertakes the study of the different techniques used for reducing and enlarging images, whose combination will be a new protocol (REPro) for the transmission of medical images. The proposed approach consists in reducing the image when sent and enlarging it when received, which allows for consumption saving of the bandwidth allocated to the diagnostics service. To do so, the performance of our new protocol was tested for the transfer of scars images (color images) and ultrasound images (grayscale images) in terms of the resemblance between the original image, on one hand, and the reduced and then enlarged one, on the other hand.
International audienceClouds are more and more becoming a credible alternative to parallel dedicated resources. The pay-per-use pricing policy however highlights the real cost of computing applications. This new criterion, the cost, must then be assessed when scheduling an application in addition to more traditional ones as the completion time or the execution flow. In this paper, we tackle the problem of optimizing the cost of renting computing instances to execute an application on the cloud while maintaining a desired performance (throughput). The target application is a stream application based on a DAG pattern, i.e., composed of several tasks with dependencies, and instances of the same execution task graph are continuously executed on the instances. We provide some theoretical results on the problem of optimizing the renting cost for a given throughput then propose some heuristics to solve the more complex parts of the problem, and we compare them to optimal solutions found by linear programming
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