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Virtual machine placement is one of the main sub-problems in the virtual machine consolidation process which faces different challenges. Burst-aware placement plays a key role in improving energy efficiency and reducing the SLA violations in cloud computing systems and hence requires special attention and investigation. Therefore, in this study, we will present burst-aware algorithms in order to decrease the resource wastage and reduce SLA violations. By presenting these algorithms, we aim to minimize the negative effects of workload bursts on the process of making decisions about the placement of virtual machines. We use random and realworld datasets and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding energy efficiency and performance, compared to the benchmark methods.

PurposeThe purpose of this paper is to present an economic–statistical design (ESD) for the Bayesian X‾ control chart based on predictive distribution with two types of informative and noninformative prior distributions.Design/methodology/approachThe design used in this study is based on determining the control chart of the predictive distribution and then its ESD. The new proposed cost model is presented by considering the conjugate and Jeffrey's prior distribution in calculating the expected total cycle time and expected cost per cycle, and finally, the optimal design parameters and related costs are compared with the fixed ratio sampling (FRS) mode.FindingsNumerical results show decreases in costs in this Bayesian approach with both Jeffrey's and conjugate prior distribution compared to the FRS mode. This result shows that the Bayesian approach which is based on predictive density works better than the classical approach. Also, for the Bayesian approach, however, there is no significant difference between the results of using Jeffrey's and conjugate prior distributions. Using sensitivity analysis, the effect of cost parameters and shock model parameters and deviation from the mean on the optimal values of design parameters and related costs have been investigated and discussed.Practical implicationsThis research adds to the body of knowledge related to quality control of process monitoring systems. This paper may be of particular interest to quality system practitioners for whom the effect of the prior distribution of parameters on the quality characteristic distribution is important.Originality/valueeconomic statistical design (ESD) of Bayesian control charts based on predictive distribution is presented for the first time.

PurposeThe purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.Design/methodology/approachThe design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.FindingsNumerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.Practical implicationsThis research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.Originality/valueThe cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

One of the essential phases during the Virtual Machine (VM) consolidation is to detect the source (underloaded and overloaded) hosts. Bursts with a sudden and transient increase in the workload have an adverse effect on this process. Also, burst-aware methods for detecting the overloaded and underloaded hosts play a key role in achieving performance and energy efficiency tradeoffs in the cloud computing systems. In this study, we deal with these methods as an open issue in the resource management process. We present a dynamic burst-aware method for detecting the overloaded and underloaded hosts. The principal idea in our proposed method is to keep the weighted average load of the hosts within a dynamic range. The thresholds and weights (impact factors) are obtained from the analysis of the historical data related to hosts. Furthermore, we have used the CloudSim simulator and PlanetLab real-world dataset in order to measure the performance of our proposed method. The simulation results verify the superiority of the proposed technique in achieving performance and energy efficiency tradeoffs, compared to the benchmark methods.