AlertInsight:Mining Multiple Correlation For Alert Reduction

Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools,which generate massive numbers of alerts and events that are not actionable.These alerts usually carry isolated messages that are missing service contexts.Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems.Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages.One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations.Based on these guidelines,AlertInsight,a framework for alert event reduction,is proposed in this paper.In AlertInsight,the correlations among event sources are found by mining a sequence of historical events.Then,event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream.Finally,the correlated events are aggregated into a single high-level event for alert reduction.Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments,an innovative approach for multiple correlation mining,which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism,is proposed in this paper.In addition,a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed.Experiments are conducted to test the effectiveness of AlertInsight.The experimental results(precision=0.92,recall=0.93,and F1-score=0.93)demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction.

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.