Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

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.

Section: Maintenance Platform Using Lambda Architecturementioning

confidence: 99%

Maintenance of business machines with edge and cloud collaboration

IEEJ Transactions Elec Engng

Kumazaki

2018

Self Cite

“…Fatigue level is calculated using RRI change (e.g., [23]) and relaxation level is calculated with cardiac vagal index (CVI) [24]. When machine learning technologies are used, GPU or baremetal power can be used to analyze [25] [26]. Based on analyzed results of cloud, actions of substitute drivers assignments or emergent alerts are conducted using Web services [27]- [31] or service coordination technologies [32]- [37].…”

Section: Sample Applicationmentioning

confidence: 99%

Experiments of Posture Estimation on Vehicles Using Wearable Acceleration Sensors

2017 IEEE 3rd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High P

2017

Self Cite

Abstract-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.

“…We previously proposed an automatic verification function for cloud user environments [42][43][44][45][46][47]. It replicates virtual environments, extracts regression test cases corresponding to the installed OS, middleware, and applications, and executes them by using the Jenkins tool to verify software patches automatically.…”

Section: Automatic Performance Verificationmentioning

confidence: 99%

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

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.