Context-aware content-provision service for shopping malls based on ubiquitous service-oriented network framework and authentication and access control agent framework

IEEJ Transactions Elec Engng

Kumazaki

2018

Recently, internet of things (IoT) technologies have progressed, but IoT maintenance applications are not widespread in Japan yet because of insufficient analyses of real-time situations and the high costs of configuring failure detection rules and collecting sensing data. In this paper, using lambda architecture concept, we propose a maintenance platform on which edge nodes analyze sensing data, detect anomalies and extract a new detection rule in real time, and a cloud orders maintenance automatically.

Section: Maintenance Platform Using Lambda Architecturementioning

confidence: 99%

Maintenance of business machines with edge and cloud collaboration

IEEJ Transactions Elec Engng

Kumazaki

2018

“…Machine learning models and other configuration setting of Jubatus are distributed from the cloud shoplifting prevention application to small computers in each shop. When we update configuration setting including models, we will use service coordination technologies such as in Refs [26][27][28][29][30]. And when we conduct regression tests after configuration updates, we will use automatic verification methods such as in Refs [31,33].…”

Section: Confirmation Of Security Camera Movie Stream Analysis By Jubmentioning

confidence: 99%

Proposal of shoplifting prevention service using image analysis and ERP check

IEEJ Transactions Elec Engng

Fukumoto

Kumazaki

2017

In this paper, we propose a software‐as‐a‐service application that prevents shoplifting using image analysis and enterprise resource planning (ERP). In Japan, the total damage due to shoplifting has reached ¥450 billion per year, and each year more than 1000 small book stores give up their business because of shoplifting damage. Based on the recent advancement in cloud technology and data analysis technology, we propose a shoplifting prevention service with the image analysis of security camera movie and ERP data check for small shops. We also confirm the precision ratio of security camera stream movie analysis using the online machine learning framework Jubatus. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

“…Cloud providers can reduce cost because cloud providers can enhance application performances much by few number of GPU/FPGA servers. Users also can coordinate outer systems using service coordination technologies such as [61][62][63][64][65][66]. We also consider to use GPU/ FPGA servers to IoT applications [67][68][69][70][71][72][73][74].…”

Section: Related Workmentioning

confidence: 99%

Server Selection, Configuration and Reconfiguration Technology for IaaS Cloud with Multiple Server Types

2017

J Netw Syst Manage

We propose a server selection, configuration, reconfiguration and automatic performance verification technology to meet user functional and performance requirements on various types of cloud compute servers. Various servers mean there are not only virtual machines on normal CPU servers but also container or baremetal servers on strong graphic processing unit (GPU) servers or field programmable gate arrays (FPGAs) with a configuration that accelerates specified computation. Early cloud systems are composed of many PC-like servers, and virtual machines on these severs use distributed processing technology to achieve high computational performance. However, recent cloud systems change to make the best use of advances in hardware power. It is well known that baremetal and container performances are better than virtual machines performances. And dedicated processing servers, such as strong GPU servers for graphics processing, and FPGA servers for specified computation, have increased. Our objective for this study was to enable cloud providers to provision compute resources on appropriate hardware based on user requirements, so that users can benefit from high performance of their applications easily. Our proposed technology select appropriate servers for user compute resources from various types of hardware, such as GPUs and FPGAs, or set appropriate configurations or reconfigurations of FPGAs to use hardware power. Furthermore, our technology automatically verifies the performances of provisioned systems. We measured provisioning and automatic performance verification times to show the effectiveness of our technology.