CERN Storage Systems for Large-Scale Wireless

The project aims at evaluating the use of CERN computing infrastructure for next generation sensor networks data analysis. The proposed system allows the simulation of a large-scale sensor array for traffic analysis, streaming data to CERN storage systems in an efficient way. The data are made available for offline and quasi-online analysis, enabling both long term planning and fast reaction on the environment

Distributed Network Anomaly Detection on an Event Processing Framework

Network Intrusion Detection Systems (NIDS) are an integral part of modern data centres to ensure high availability and compliance with Service Level Agreements (SLAs). Currently, NIDS are deployed on high-performance, high-cost middleboxes that are responsible for monitoring a limited section of the network. The fast increasing size and aggregate throughput of modern data centre networks have come to challenge the current approach to anomaly detection to satisfy the fast growing compute demand. In this paper, we propose a novel approach to distributed intrusion detection systems based on the architecture of recently proposed event processing frameworks. We have designed and implemented a prototype system using Apache Storm to show the benefits of the proposed approach as well as the architectural differences with traditional systems. Our system distributes modules across the available devices within the network fabric and uses a centralised controller for orchestration, management and correlation. Following the Software Defined Networking (SDN) paradigm, the controller maintains a complete view of the network but distributes the processing logic for quick event processing while performing complex event correlation centrally. We have evaluated the proposed system using publicly available data centre traces and demonstrated that the system can scale with the network topology while providing high performance and minimal impact on packet latency

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