2014, Vol.19, No.2

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Cloud computing provides flexible and efficient mass data analysis platform. In this paper, wedefine a new resource provisioning architecture in the public cloud, named PCIA. In addition, weprovide a service model of PCIA and its new naming scheme. Our model selects the proper numberof physical or virtual resources based on the requirements of clients. By the analysis ofperformance variation in the PCIA, we evaluate the relationship between performance variationand resource provisioning, and we present key standards for cloud system constructions, which canbe an important resource provisioning criteria for both cloud service providers and clients.

In this paper, we build server consolidate virtualization focusing on NT server dilapidated andthrough analysis of the effect of this, has been presented the following results. First, it waspossible to obtain an increase in the constant temperature constant humidity chamber efficiency bysecuring the upper surface space for computing data center mounted virtualization integrationserver servers multiple physical. Second, by the deployment of virtualization of a large serverdilapidated, the introduction of new resources and the cost of maintaining computerized datacenter and reduce power consumption, the reduction of hardware introduction of additional due to the new server deployment it was possible to derive a cost reduction effect. We obtain thereduction of 89% and 79% compared with conventional consumption of cooling and power savingsof the top surface, respectively. Integration of NT servers are virtualized can be expected to changein the Green-IT to contribute to providing the maintenance costs of computing centers in the futureas well as current. Building a virtualization server integration applied in this paper can besimilarly applied to the UNIX system and to integrate the server group other.

The use of induction motors has been recently increasing in a variety of industrial sites, andthey play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damagecaused by their faults. To early identify induction motor faults, this paper effectively estimatesspectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC)analysis technique and an expectation maximization (EM) algorithm. Moreover, this paperclassifies induction motor faults into their corresponding categories by calculating Mahalanobisdistance using the estimated spectral envelopes and finding the minimum distance. Experimentalresults show that the proposed approach yields higher classification accuracies than thestate-of-the-art conventional approach for both noiseless and noisy environments for identifyingthe induction motor faults.