Abstract
In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.
Highlights
Cloud computing provides ability to dynamically scale or shrink the provisioned resources as per the dynamic requirements [1]
We first introduce the performances of our bubble sort particle swarm optimization (BSPSO) algorithm for the design of cellular neural networks (CNNs) template parameters, including the ability to get the template solution and the comparison results with GA, PSO BSPSO, and sort algorithm
The performance of our VMMD model based on CNN algorithm is evaluated, such as the effects of VMMD model on migration lifecycle and the comparison results with other published existing algorithms
Summary
Cloud computing provides ability to dynamically scale or shrink the provisioned resources as per the dynamic requirements [1]. Complete state of running VM, which includes the permanent storage (i.e., the disk image), volatile storage (the memory), the state of connected devices (such as network interface card), and the internal state of the virtual CPU (VCPU), has to be transferred [2] In this case, VM locations are varied dynamically and the internal state of the VCPU and connected devices are a few kilobytes of data and can be sent to the VMM and the target PM. CNN has features with multidimensional array of neurons and realizable paradigm of parallel computation; the processing time is unrelated with the data dimension It can be implemented by very large scale integration (VLSI), which makes neural networks implemented [3].
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