An autonomous decentralized adaptive function for retaining control strength in large-scale and wide-area system

We have proposed an autonomous decentralized control using a local action rule for indirectly controlling the probability distribution of a system performance variable on the basis of markov chain monte carlo, while not measuring the variable. In this paper, we design an autonomous decentralized adaptive function for retaining the control strength of our control under a changing environment as an example of global controls appropriately reflecting information of external environment. We apply our control with the adaptive function to a virtual machine placement problem in a Data Center Network (DCN). Through simulation experiments, we confirm that the adaptive function effectively deals with several scenarios with a changing environment in a DCN.