Map ve reduce ile mobil ağlarda uçtan uca internet hız analizi

Mobil veri iletişimi, mobil cihazların kullanımın ve veri yoğunluklu uygulamaların artmasıyla hızlı bir şekilde artmaktadır. Artan veri kullanımından dolayı veri iletişimi trafiğinin izlenmesi ve analiz edilmesi, mobil servis sağlayıcıları için ağ yönetimi ve optimizasyonu bakımından önemli hale getirmiştir. Paket tabanlı mobil ağlarda kullanıcı deneyimini ölçmek önemlidir fakat şuanki sistemlerde kullanıcı deneyimini yakalamak bir hayli zordur. Çünkü trafik analizi uçtan uca alınarak yapılamamaktadır. Çalışmamız kapsamında geliştirdiğimiz sistem, mobil ağlarda, test sistemleri gerçek kullanım senaryoları ile hız testi koşup, iletişim paketlerini uçtan uca analiz ederek saniye başına indirilen ve yüklenen veri miktarlarını hesaplamaktadır. Büyük veri kümesi halinde alınan test sonuçları paralel ve dağıtık sistemlerde MapReduce programlama modelleri kullanılarak Hadoop ortamında analiz etmektedir. Analiz edilen büyük test sonuçları ile internet hız karakteristik haritası çıkartmaktadır. Sonuç olarak sistem, uçtan uca ağ analizi yaparak, ağ hız kalitesini ölçmekte ayrıca büyük veri analizi ile birlikte kullanıcı deneyimleri ve mobil ağda oluşan problemleri kullanıcı şikâyeti üremeden tespit etmektedir.Packet-based mobile networks are increasingly carrying internet data traffic for data intensive applications. Because of increased data usage, data traffic analysis have been a very essential way in network management and optimization for the mobile service providers. It is an important issue to measure user experiences in packet network, but the current systems cannot correctly capture user experiences. This is simply because; data traffic analysis is not end-to-end traffic trace. In the context of this study, the analysis system has been developed for mobile network. The test system runs speed tests with actual usage scenarios, trace communication packets and analysis download and upload. Test results as large data sets are processed and analyzed in parallel and distributed systems on cluster of computers using MapReduce programming model on Hadoop environment. Internet speed characteristics map is rendered with regional analyzed big test results. Additionally, the system reviews end-to-end network analysis and consequently, measure network speed quality too, with big data analysis, detects user experiences and problems without user complaints

Big Data Meets Telcos: A Proactive Caching Perspective

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.Comment: 8 pages, 5 figure

Game Theoretic Approaches to Massive Data Processing in Wireless Networks

Wireless communication networks are becoming highly virtualized with two-layer hierarchies, in which controllers at the upper layer with tasks to achieve can ask a large number of agents at the lower layer to help realize computation, storage, and transmission functions. Through offloading data processing to the agents, the controllers can accomplish otherwise prohibitive big data processing. Incentive mechanisms are needed for the agents to perform the controllers' tasks in order to satisfy the corresponding objectives of controllers and agents. In this article, a hierarchical game framework with fast convergence and scalability is proposed to meet the demand for real-time processing for such situations. Possible future research directions in this emerging area are also discussed

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