Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin
Abstract With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
|
| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00579-4 |
| _version_ | 1827381936863051776 |
|---|---|
| author | Zicong Miao Weize Li Xiaodong Pan |
| author_facet | Zicong Miao Weize Li Xiaodong Pan |
| author_sort | Zicong Miao |
| collection | DOAJ |
| description | Abstract With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it is still a challenge to guarantee the correlation between data when compressing the MTS. This paper proposes an MTS Collaborative Compression (MTSCC) method based on the two-step compression scheme. First, shape-based clustering is implemented to group the MTS. Afterward, the compressed sensing is optimized to achieve collaborative compression of grouped data. Based on a real-world MTS dataset, the experimental results show that the proposed MTSCC can effectively preserve the complex temporal correlation between indicators while achieving efficient data compression, and the root mean squared error of correlation between the reconstructed and original data is only 0.0489 in the case of 30% compression ratio. Besides, it is verified that using the reconstructed data in the production environment has almost the same performance as using the original data. |
| first_indexed | 2024-03-08T14:12:10Z |
| format | Article |
| id | doaj.art-6e71af2994c74379936e286f228bc799 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-08T14:12:10Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-6e71af2994c74379936e286f228bc7992024-01-14T12:36:39ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-01-0113111510.1186/s13677-023-00579-4Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twinZicong Miao0Weize Li1Xiaodong Pan2China Telecom Cloud Computing CorporationChina Telecom Cloud Computing CorporationChina Telecom Cloud Computing CorporationAbstract With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it is still a challenge to guarantee the correlation between data when compressing the MTS. This paper proposes an MTS Collaborative Compression (MTSCC) method based on the two-step compression scheme. First, shape-based clustering is implemented to group the MTS. Afterward, the compressed sensing is optimized to achieve collaborative compression of grouped data. Based on a real-world MTS dataset, the experimental results show that the proposed MTSCC can effectively preserve the complex temporal correlation between indicators while achieving efficient data compression, and the root mean squared error of correlation between the reconstructed and original data is only 0.0489 in the case of 30% compression ratio. Besides, it is verified that using the reconstructed data in the production environment has almost the same performance as using the original data.https://doi.org/10.1186/s13677-023-00579-4Cloud monitoringMTSShape-based clusteringCompressed sensingCollaborative compression |
| spellingShingle | Zicong Miao Weize Li Xiaodong Pan Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin Journal of Cloud Computing: Advances, Systems and Applications Cloud monitoring MTS Shape-based clustering Compressed sensing Collaborative compression |
| title | Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin |
| title_full | Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin |
| title_fullStr | Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin |
| title_full_unstemmed | Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin |
| title_short | Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin |
| title_sort | multivariate time series collaborative compression for monitoring systems in securing cloud based digital twin |
| topic | Cloud monitoring MTS Shape-based clustering Compressed sensing Collaborative compression |
| url | https://doi.org/10.1186/s13677-023-00579-4 |
| work_keys_str_mv | AT zicongmiao multivariatetimeseriescollaborativecompressionformonitoringsystemsinsecuringcloudbaseddigitaltwin AT weizeli multivariatetimeseriescollaborativecompressionformonitoringsystemsinsecuringcloudbaseddigitaltwin AT xiaodongpan multivariatetimeseriescollaborativecompressionformonitoringsystemsinsecuringcloudbaseddigitaltwin |