| Title | An Accountability-Oriented Generation approach to Time-Varying Structure of Cloud Service |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Xiaojian, Chen, Jing, Jiang, Yiyi, Hu, Hangping, Yang, Haopeng |
| Conference Name | 2021 IEEE International Conference on Services Computing (SCC) |
| Date Published | sep |
| Keywords | Big Data, cloud service, composability, Conferences, convolutional neural networks, Event network, Metrics, network accountability, Neural networks, Prediction algorithms, pubcrawl, Resiliency, service computing, Structure generation, Uncertainty, uncertainty correlation |
| Abstract | In the current cloud service development, during the widely used of cloud service, it can self organize and respond on demand when the cloud service in phenomenon of failure or violation, but it may still cause violation. The first step in forecasting or accountability for this situation, is to generate a dynamic structure of cloud services in a timely manner. In this research, it has presented a method to generate the time-varying structure of cloud service. Firstly, dependencies between tasks and even instances within a job of cloud service are visualized to explore the time-varying characteristics contained in the cloud service structure. And then, those dependencies are discovered quantitatively using CNN (Convolutional Neural Networks). Finally, it structured into an event network of cloud service for tracing violation and other usages. A validation to this approach has been examined by an experiment based on Alibaba's dataset. A function integrity of this approach may up to 0.80, which is higher than Bai Y and others which is no more than 0.60. |
| DOI | 10.1109/SCC53864.2021.00059 |
| Citation Key | li_accountability-oriented_2021 |
An Accountability-Oriented Generation approach to Time-Varying Structure of Cloud Service