Proposal of shoplifting prevention service using image analysis and ERP check

2017

J Netw Syst Manage

Self Cite

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.

Section: Related Workmentioning

confidence: 99%

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

2017

J Netw Syst Manage

Self Cite

“…Due to the progress in Internet of Things (IoT) technology (e.g. Industrie 4.0 and so on [13][14][15][16][17][18]), IoT devices are increasing rapidly. There are various application fields of IoT such as manufacturing, distribution, medical, and agriculture.…”

Section: Introductionmentioning

confidence: 99%

Study and evaluation of improved automatic GPU offloading method

International Journal of Parallel, Emergent and Distributed Sys

2021

Self Cite

With the slowing down of Moore's law, the use of hardware other than CPUs, such as graphics processing units (GPUs) or field-Programmable gate arrays (FPGAs), is increasing. However, when using heterogeneous hardware other than CPUs, barriers to technical skills, such for compute unified device architecture (CUDA) and open computing language (OpenCL), are high. Therefore, I previously proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code according to the hardware to be placed. As part of environment adaptive software, I also proposed a method to offload loop statements of applications to GPUs automatically. In this paper, I improved upon this automatic GPU offloading method to expand its applicability to more applications and enhance offloading performance. I implemented the improved method to evaluate its effectiveness for multiple applications.

“…Recently, IoT and cloud technologies have progressed. Manufacturing and maintenance are hot areas for IoT applications, and IoT platforms have also been created to develop and operate IoT applications effectively.…”

Section: Introductionmentioning

confidence: 99%

Maintenance of business machines with edge and cloud collaboration

IEEJ Transactions Elec Engng

Kumazaki

2018

Self Cite

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.