Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions for analyzing and processing such Big Data by distributing the computations among a number of compute nodes. In this work, performance of distributed computing environments on the basis of Hadoop and Spark frameworks is estimated for real and virtual versions of clusters. As a test task, we chose the classic use case of word counting in texts of various sizes. It was found that the running times grow very fast with the dataset size and faster than a power function even. As to the real and virtual versions of cluster implementations, this tendency is the similar for both Hadoop and Spark frameworks. Moreover, speedup values decrease significantly with the growth of dataset size, especially for virtual version of cluster configuration. The problem of growing data generated by IoT and multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye tracking, etc.) interaction channels is presented. In the context of this problem, the current observations as to the running times and speedup on Hadoop and Spark frameworks in real and virtual cluster configurations can be very useful for the proper scaling-up and efficient job management, especially for machine learning and Deep Learning applications, where Big Data are widely present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine

Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service

An increasing number of Analytics-as-a-Service solutions has recently seen the light, in the landscape of cloud-based services. These services allow flexible composition of compute and storage components, that create powerful data ingestion and processing pipelines. This work is a first attempt at an experimental evaluation of analytic application performance executed using a wide range of storage service configurations. We present an intuitive notion of data locality, that we use as a proxy to rank different service compositions in terms of expected performance. Through an empirical analysis, we dissect the performance achieved by analytic workloads and unveil problems due to the impedance mismatch that arise in some configurations. Our work paves the way to a better understanding of modern cloud-based analytic services and their performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1