2014, Vol.19, No.11

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

In this paper, we propose a virtual machine(VM) remapping scheme that reduces VM relocation time ona cloud cluster. The proposed scheme finds VMs that should be migrated in sequence from a given VMmap, and exchanges destinations of some VMs among them to reduce the VM relocation time. The VMs,the destinations of which will be exchanged, are chosen based on the amount of physical machine'savailable resources and migration completion time. The exchange of destinations is repeated until the VMrelocation time cannot be shortened any further. Through a simulation, we show that the proposed schemereduces VM relocation time by 42.7% in maximum.

In this paper, We propose a block reallocation algorithm for reducing network traffic in Hadoop cluster. The scheduler of Hadoop cluster receives a job from users. And the job is divided into multiple tasksassigned to nodes. At this time, the scheduler allocates the task to the node that satisfied data locality. Ifa task is assigned to the node that does not have the data(block) to be processed, the task is processedafter the data transmission from another node. There is difference of workload among nodes becauseblocks in cluster have different access frequency. Therefore, the proposed algorithm relocates blocks according to the task allocation pattern of Hadoop scheduler. Eventually, workload of nodes are leveled,and the case of the task processing in a node that does not have the block to be processing is reduced. Thus, the network traffic of the cluster is also reduced. We evaluate the proposed block reallocationalgorithm by a simulation. The simulation result shows maximum 23.3% reduction of network consumptionthan default delay scheduling for jobs processing.

This paper proposes a fault detection method for low-speed rolling element bearings of an inductionmotor using acoustic emission signals and histogram modeling. The proposed method performs envelopmodeling of the histogram of normalized fault signals. It then extracts and selects significant features ofeach fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally,using the extracted features as inputs, the support vector regression (SVR) classifies bearing’s inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed methodwith varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and thenumber of features from 2 to 150. Experimental results show that the proposed fault identification methodusing 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performanceand outperforms conventional fault diagnosis methods as well.