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In a large-scale data center, it is vital to precisely recognize the termination statuses of applications at an early stage. In recent years, many machine learning techniques have been applied to this issue, which is beneficial for optimizing the scheduling policy and improving the efficiency of resource utilization. However, if the application's dynamic information is insufficient at the early stage, the generalization performance of the machine learning model will be lessened, and the prediction accuracy could be low. To overcome this problem, a novel failure prediction method that is based on the association relationships between similar jobs is proposed in this paper to jointly predict task's termination statuses at an earlier stage. The similar jobs whose tasks have similar changing modes of consumed resources, an inherent structural correlation may exist, and the correlation information is significant for improving the prediction model's generalization performance. First, a job clustering algorithm is proposed for identifying the jobs with higher similarity from jobs that have various numbers of tasks. Second, based on the job clustering results, the robust multi-task learning algorithm is introduced to effectively utilize the domain information among jobs (i.e. interactional relationship among jobs on the termination statuses of task). Experiments are conducted on a Google cluster workload traces dataset. The results show that the proposed method can realize higher prediction accuracy, lower misjudgment rate, and higher predictive stability than several state-of-the-art methods at 1/3 the running time of the tasks. [ABSTRACT FROM AUTHOR]

As a novel learning algorithm for single-hidden-layer feedforward neural networks, extreme learning machines (ELMs) have been a promising tool for regression and classification applications. However, it is not trivial for ELMs to find the proper number of hidden neurons due to the nonoptimal input weights and hidden biases. In this paper, a new model selection method of ELM based on multi-objective optimization is proposed to obtain compact networks with good generalization ability. First, a new leave-one-out (LOO) error bound of ELM is derived, and it can be calculated with negligible computational cost once the ELM training is finished. Furthermore, the hidden nodes are added to the network one-by-one, and at each step, a multi-objective optimization algorithm is used to select optimal input weights by minimizing this LOO bound and the norm of output weight simultaneously in order to avoid over-fitting. Experiments on five UCI regression data sets are conducted, demonstrating that the proposed algorithm can generally obtain better generalization performance with more compact network than the conventional gradient-based back-propagation method, original ELM and evolutionary ELM. [ABSTRACT FROM AUTHOR]

Most traditional approaches to load identification usually perform not very well because the inverse identification problem in most practical cases is ill-posed. In this paper, a new approach to the identification of dynamic loads based on the solution path algorithm of support vector regression is proposed. A general framework of dynamic load identification using support vector regression is derived and the corresponding identification model using solution path algorithm is established. The results of numerical and experimental tests on cylinder vibration system show that the proposed approach greatly outperforms existing methods, i.e., direct inverse identification method, ℇ-SVR and LS-SVM, in terms of identification accuracy and numerical stability. As a conclusion, the proposed approach is applicable to a wide variety of mechanical cases. [ABSTRACT FROM AUTHOR]

Abstract: In the conventional solution path algorithm of support vector regression, the ε-insensitive error of every training sample is equally penalized, which means every sample affects the generalization ability equally. However, in some cases, e.g. time series prediction or noisy function regression, the ε-insensitive error of the sample which could provide more important information should be penalized more heavily. Therefore, the weighted solution path algorithm of support vector regression is proposed in this paper. Error penalty parameter of each training sample is weighted differently, and the whole solution path is modified correspondingly. More importantly, by choosing Arc Tangent function as the prototype to generate weights with various characteristics, a heuristic weight-setting optimization algorithm is proposed to compute the optimal weights using particle swarm optimization (PSO). This method is applicable to different applications. Experiments on time series prediction and noisy function regression are conducted, demonstrating comparable results of the proposed weighted solution path algorithm and encouraging performance of the heuristic weight-setting optimization. [Copyright &y& Elsevier]