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In this article, a novel sensitivity minimization approach is proposed to optimize the performance of proportional‐integral (PI) dynamic average consensus (DAC) algorithms subject to measurement noises and model uncertainties. Network sensitivity and complementary sensitivity functions are defined to characterize the effects of the reference signals and measurement noises on the tracking error, respectively. Minimization of the H∞ norms of network sensitivity and complementary sensitivity functions, describing how large the PI DAC algorithm amplifies the reference and noise signals in the worst case, are conducted to optimize the tracking performance with respect to the reference signals and measurement noises without requiring prior knowledge. Further, by studying the weighted H∞ optimization of the network sensitivity functions, necessary and sufficient conditions are provided for the PI DAC algorithm to have a good tracking performance for low‐frequency reference signals and high‐frequency measurement noises. The proposed sensitivity minimization approach is shown to be a robust approach, by considering the PI DAC algorithm in the presence of norm‐bounded additive uncertainties. The robust weighted H∞ performance specification is formulated and a sufficient condition is provided, relying on the network topology, the bound of the uncertainties, and the parameters of the DAC algorithms. A tradeoff relationship between the tracking performance and robustness is unveiled. The special case of proportional DAC algorithms is similarly discussed. [ABSTRACT FROM AUTHOR]

Handing computing assets to cloud service providers (CSPs) to obtain cloud services is one of the important strategies for enterprises to embrace the digital era, and CSP selection is a crucial decision‐making process for cloud deployment. However, there are many criteria involved in selecting an optimal CSP, not all of which can be accurately quantified. Therefore, CSP selection is a typical hybrid‐information decision‐making problem, in which criterion evaluation values are expressed in various forms. Meanwhile, the psychological behavior of the CSP selection team also has a significant impact on the decision‐making result, which is poorly considered in the existing research results on CSP selection. Thus, in this paper, a new group decision‐making support framework incorporating regret theory is constructed to select CSPs with hybrid information. Initially, various forms of hybrid information are processed separately to avoid the distortion of heterogeneous information caused by traditional conversion methods. Then, considering the psychology of regret aversion, the respective regret–rejoice functions for hybrid information are defined. Subsequently, regret–rejoice values are introduced into the evaluation of mixed data method framework, and a decision‐making support procedure based on it is established, in which an expert weight determination method based on the maximizing consensus model is proposed, and the group best–worst method is used to calculate criteria weights. Afterwards, an illustrative example of CSP selection is given to clarify the implementation process of the proposed method. Finally, the effectiveness and superiority of the proposed decision‐making framework in selecting CSPs are explained through parameter analysis and comparison with existing methods. [ABSTRACT FROM AUTHOR]