Abstract
Virtualization is an act of creating virtual resources that are made accessible by the application of cloud computing. In case of failure of a virtual machine, it is imperative to shift the processes running on this machine to another. This activity is known as live virtual machine migration and is classified under the major issues of research. Xia (2015) proposed a mathematical model to analyze this problem in which the work has been done in two phases. It is showing a state transition model system in which the M/M/1/K model has been utilized in phase1 to find the rejection probability of the jobs in the phase2. Each virtual machine is considered to have a buffer to store the incoming buffer. The major issue being discussed in this paper is the relation of the rejection probability of jobs with the changing size of the buffer. It actually provides an exhaustive analysis of three different queueing models, i.e., M/M/1/∞, M/M/∞, and M/M/1/K. The simulations are carried out in MATLAB, and the results are analyzed based on the rejection probability of the jobs. It is observed that with an increase in the request arrival rate, the rejection probability of jobs increases. However, with an increase in execution rate, the rejection probability of jobs decreases. If we change the model to M/M/∞, actually, the formulas of request rejection probability and job rejection probability got changed that resulted in a continuous decrease in values of rejection rate lines as compared to the values of the author. Hence, we can say that changing the queueing model is beneficial.
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Sachdeva, S., Gupta, N. (2019). Effects of Different Queueing Models on Migration of Virtual Machines. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_24
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DOI: https://doi.org/10.1007/978-981-13-3393-4_24
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