A Study to Optimize Heterogeneous Resources for Open IoT

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing and Networking (CANDAR2017), Nov. 201

Experiments of posture estimation on vehicles using wearable acceleration sensors

In this paper, we study methods to estimate drivers' posture in vehicles using acceleration data of wearable sensor and conduct a field test. Recently, sensor technologies have been progressed. Solutions of safety management to analyze vital data acquired from wearable sensor and judge work status are proposed. To prevent huge accidents, demands for safety management of bus and taxi are high. However, acceleration of vehicles is added to wearable sensor in vehicles, and there is no guarantee to estimate drivers' posture accurately. Therefore, in this paper, we study methods to estimate driving posture using acceleration data acquired from T-shirt type wearable sensor hitoe, conduct field tests and implement a sample application.Comment: 4 pages, 4 figures, The 3rd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2017), pp.14-17, Beijing, May 201

Security Camera Movie and ERP Data Matching System to Prevent Theft

In this paper, we propose a SaaS service which prevents shoplifting using image analysis and ERP. In Japan, total damage of shoplifting reaches 450 billion yen. Based on cloud and data analysis technology, we propose a shoplifting prevention service with image analysis of security camera and ERP data check for small shops. We evaluated movie analysis.Comment: 2 pages, 2 figures, IEEE Consumer Communications and Networking Conference (CCNC2017), pp.1021-1022, Jan. 201

A System Architecture for Real-time Anomaly Detection in Large-scale NFV Systems

Virtualization as a key IT technology has developed to a predominant model in data centers in recent years. The flexibility regarding scaling-out and migration of virtual machines for seamless maintenance has enabled a new level of continuous operation and changed service provisioning significantly. Meanwhile, services from domains striving for highest possible availability – e.g. from the telecommunications domain – are adopting this approach as well and are investing significant efforts into the development of Network Function Virtualization (NFV). However, the availability requirements for such infrastructures are much higher than typical for IT services built upon standard software with off-the-shelf hardware. They require sophisticated methods and mechanisms for fast detection and recovery of failures. This paper presents a set of methods and an implemented prototype for anomaly detection in cloud-based infrastructures with specific focus on the deployment of virtualized network functions. The framework is built upon OpenStack, which is the current de-facto standard of open-source cloud software and aims at increasing the availability and fault tolerance level by providing an extensive monitoring and analysis pipeline able to detect failures or degraded performance in real-time. The indicators for anomalies are created using supervised and non-supervised classification methods and preliminary experimental measurements showed a high percentage of correctly identified anomaly situations. After a successful failure detection, a set of pre-defined countermeasures is activated in order to mask or repair outages or situations with degraded performance

Cloud-efficient modelling and simulation of magnetic nano materials

Scientific simulations are rarely attempted in a cloud due to the substantial performance costs of virtualization. Considerable communication overheads, intolerable latencies, and inefficient hardware emulation are the main reasons why this emerging technology has not been fully exploited. On the other hand, the progress of computing infrastructure nowadays is strongly dependent on perspective storage medium development, where efficient micromagnetic simulations play a vital role in future memory design. This thesis addresses both these topics by merging micromagnetic simulations with the latest OpenStack cloud implementation while providing a time and costeffective alternative to expensive computing centers. However, many challenges have to be addressed before a high-performance cloud platform emerges as a solution for problems in micromagnetic research communities. First, the best solver candidate has to be selected and further improved, particularly in the parallelization and process communication domain. Second, a 3-level cloud communication hierarchy needs to be recognized and each segment adequately addressed. The required steps include breaking the VMisolation for the host’s shared memory activation, cloud network-stack tuning, optimization, and efficient communication hardware integration. The project work concludes with practical measurements and confirmation of successfully implemented simulation into an open-source cloud environment. It is achieved that the renewed Magpar solver runs for the first time in the OpenStack cloud by using ivshmem for shared memory communication. Also, extensive measurements proved the effectiveness of our solutions, yielding from sixty percent to over ten times better results than those achieved in the standard cloud.Aufgrund der erheblichen Leistungskosten der Virtualisierung werden wissenschaftliche Simulationen in einer Cloud selten versucht. Beträchtlicher Kommunikationsaufwand, erhebliche Latenzen und ineffiziente Hardwareemulation sind die Hauptgründe, warum diese aufkommende Technologie nicht vollständig genutzt wurde. Andererseits hängt der Fortschritt der Computertechnologie heutzutage stark von der Entwicklung perspektivischer Speichermedien ab, bei denen effiziente mikromagnetische Simulationen eine wichtige Rolle für die zukünftige Speichertechnologie spielen. Diese Arbeit befasst sich mit diesen beiden Themen, indem mikromagnetische Simulationen mit der neuesten OpenStack Cloud-Implementierung zusammengeführt werden, um eine zeit- und kostengünstige Alternative zu teuren Rechenzentren bereitzustellen. Viele Herausforderungen müssen jedoch angegangen werden, bevor eine leistungsstarke Cloud-Plattform als Lösung für Probleme in mikromagnetischen Forschungsgemeinschaften entsteht. Zunächst muss der beste Kandidat für die Lösung ausgewählt und weiter verbessert werden, insbesondere im Bereich der Parallelisierung und Prozesskommunikation. Zweitens muss eine 3-stufige CloudKommunikationshierarchie erkannt und jedes Segment angemessen adressiert werden. Die erforderlichen Schritte umfassen das Aufheben der VM-Isolation, um den gemeinsam genutzten Speicher zwischen Cloud-Instanzen zu aktivieren, die Optimierung des Cloud-Netzwerkstapels und die effiziente Integration von Kommunikationshardware. Die praktische Arbeit endet mit Messungen und der Bestätigung einer erfolgreich implementierten Simulation in einer Open-Source Cloud-Umgebung. Als Ergebnis haben wir erreicht, dass der neu erstellte Magpar-Solver zum ersten Mal in der OpenStack Cloud ausgeführt wird, indem ivshmem für die Shared-Memory Kommunikation verwendet wird. Umfangreiche Messungen haben auch die Wirksamkeit unserer Lösungen bewiesen und von sechzig Prozent bis zu zehnmal besseren Ergebnissen als in der Standard Cloud geführt

Proposal of Vital Data Analysis Platform using Wearable Sensor

In this paper, we propose a vital data analysis platform which resolves existing problems to utilize vital data for real-time actions. Recently, IoT technologies have been progressed but in the healthcare area, real-time actions based on analyzed vital data are not considered sufficiently yet. The causes are proper use of analyzing methods of stream / micro batch processing and network cost. To resolve existing problems, we propose our vital data analysis platform. Our platform collects vital data of Electrocardiograph and acceleration using an example of wearable vital sensor and analyzes them to extract posture, fatigue and relaxation in smart phones or cloud. Our platform can show analyzed dangerous posture or fatigue level change. We implemented the platform. And we are now preparing a field test.Comment: 6 pages, 2 figures, 5th IIAE International Conference on Industrial Application Engineering 2017 (ICIAE2017), pp.138-143, Mar. 201

Parallel processing area extraction and data transfer number reduction for automatic GPU offloading of IoT applications

For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU offloading technology as an elementary technology of Tacit Computing. However, it can improve limited applications because it only optimizes parallelizable loop statements extraction. Thus, in this paper, to improve performances of more applications automatically, we propose an improved method with reduction of data transfer between CPU and GPU. We evaluate our proposed offloading method by applying it to Darknet and find that it can process it 3 times as quickly as only using CPU.Comment: 6 pages, 4 figures, in Japanese, IEICE Technical Report, SC2018-3