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Recently convolutional neural networks (CNNs) have essentially achieved the state-of-the-art accuracies in image classification and recognition tasks. CNNs are usually deployed in the cloud to handle data collected from IoT devices, such as smartphones and unmanned systems. However, significant data transmission overhead and privacy issues have made it necessary to use CNNs directly in device side. Nevertheless, the trained model deployed on mobile devices cannot effectively handle the unknown data and objects in new environments, which could lead to low accuracy and poor user experience. Hence, it would be crucial to re-train a better model via future unknown data. However, with tremendous computing cost and memory usage, training a CNN on IoT devices with limited hardware resources is intolerable in practice. To solve this issue, using the power of cloud to assist mobile devices to train a deep neural network becomes a promising solution . Therefore, this paper proposes a cloud-assisted CNN framework, named FitCNN, with incremental learning and low data transmission, to reduce the overhead of updating CNNs deployed on devices. To reduce the data transmission during incremental learning, we propose a strategy, called Distiller, to selectively upload the data that is worth learning, and develop an extracting strategy, called Juicer, to choose light amount of weights from the new CNN model generated on the cloud to update the corresponding old ones on devices. Experimental results show that the Distiller strategy can reduce 39.4% data transmission of uploading based on a certain dataset, and the Juicer strategy reduces by more than 60% data transmission of updating with multiple CNNs and datasets.

Emerging applications produce massive files that show different properties in file size, lifetime, and read/write frequency. Existing hybrid storage systems place these files onto different storage mediums assuming that the access patterns of files are fixed. However, we find that the access patterns of files are changeable during their lifetime. The key to improve the file access performance is to adaptively place the files on the hybrid storage system using the run-time status and the properties of both files and the storage systems. In this paper, we propose a machine learning assisted data placement mechanism that adaptively places files onto the proper storage medium by predicting access patterns of files. We design a PMFS based tracer to collect file access features for prediction and show how this approach is adaptive to the changeable access pattern. Based on data access prediction results, we present a linear data placement algorithm to optimize the data access performance on the hybrid storage mediums. Extensive experimental results show that the proposed learning algorithm can achieve over 90% accuracy for predicting file access patterns. Meanwhile, this paper can achieve over 17% improvement of system performance for file accesses compared with the state-of-the-art linear-time data placement methods.

Fault tolerance techniques are of vital importance to promise data reliability for mobile distributed system. In mobile environments, nodes suffer from high failure probability and fluctuating bandwidth. Thus, traditional fault tolerance techniques are no longer suitable. In this paper, we present a replica prioritized hybrid fault tolerance strategy combining erasure codes and replicas for a mobile distributed system (MobileRE), to guarantee data reliability with dynamic network status. In MobileRE, we first formulate a reliability cost rate to indicate the cost of ensuring data reliability of the mobile system. MobileRE further adaptively applies the erasure codes and replicas algorithms based on real-time network bandwidth status to minimize system reliability cost rate. MobileRE also obtains the optimal reliability cost rate by customizing redundant configuration parameters. The numerical and simulation results verify the effectiveness of the proposed schemes, and show that compared with traditional designs that only adopt erasure codes or replicas, MobileRE can significantly reduce the system reliability cost rate.