https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Identifying linked incidents in large-scale online service systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39899 LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.]]> Wed 06 Jul 2022 09:15:10 AEST ]]> How to mitigate the incident? An effective troubleshooting guide recommendation technique for online service systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39871 Wed 06 Jul 2022 08:49:22 AEST ]]> How incidental are the incidents? Characterizing and prioritizing incidents for large-scale online service systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39861 incidental incidents. Our qualitative and quantitative analyses show that incidental incidents are significant in terms of both number and cost. Therefore, it is important to prioritize incidents by identifying incidental incidents in advance to optimize incident management efforts. In particular, we propose an approach, called DeepIP (Deep learning based Incident Prioritization), to prioritizing incidents based on a large amount of historical incident data. More specifically, we design an attention-based Convolutional Neural Network (CNN) to learn a prediction model to identify incidental incidents. We then prioritize all incidents by ranking the predicted probabilities of incidents being incidental. We evaluate the performance of DeepIP using real-world incident data. The experimental results show that DeepIP effectively prioritizes incidents by identifying incidental incidents and significantly outperforms all the compared approaches. For example, the AUC of DeepIP achieves 0.808, while that of the best compared approach is only 0.624 on average.]]> Wed 06 Jul 2022 08:36:19 AEST ]]> Towards Intelligent Incident Management: Why We Need It and How We Make It https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41877 Tue 16 Aug 2022 09:42:16 AEST ]]> Outage Prediction and Diagnosis for Cloud Service Systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:50076 Tue 09 Jul 2024 16:01:59 AEST ]]> Fighting the Fog of War: Automated Incident Detection for Cloud Systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:47921 Thu 09 Feb 2023 10:35:54 AEDT ]]> Stability analysis and stabilization of a class of cutting systems with chatter suppression https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:26946 Sat 24 Mar 2018 07:27:03 AEDT ]]> UniParser: A Unified Log Parser for Heterogeneous Log Data https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46971 Mon 12 Dec 2022 16:19:24 AEDT ]]> CONAN: Diagnosing Batch Failures for Cloud Systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53212 Fri 17 Nov 2023 12:05:41 AEDT ]]> How long will it take to mitigate this incident for online service systems? https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39739 Fri 17 Jun 2022 18:27:13 AEST ]]> SPINE: a scalable log parser with feedback guidance https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:54836 Fri 15 Mar 2024 11:52:33 AEDT ]]> Fast Outage Analysis of Large-Scale Production Clouds with Service Correlation Mining https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39690 Fri 02 Sep 2022 08:42:46 AEST ]]>