Abstract
Microservices based architecture has recently gained traction among the cloud service providers in quest for a more scalable and reliable modular architecture. In parallel with this architectural choice, cloud providers are also facing the market demand for fine grained usage based prices. Both the management of the microservices complex dependencies, as well as the fine grained metering require the providers to track and log detailed monitoring data from their deployed cloud setups. Hence, on one hand, the providers need to record all such performance changes and events, while on the other hand, they are concerned with the additional cost associated with the resources required to store and process this ever increasing amount of collected data. In this paper, we analyze the design of the monitoring subsystem provided by open source cloud solutions, such as OpenStack. Specifically, we analyze how the monitoring data is collected by OpenStack and assess the characteristics of the data it collects, aiming to pinpoint the limitations of the current approach and suggest alternate solutions. Our preliminary evaluation of the proposed solutions reveals that it is possible to reduce the monitored data size by up to 80% and missed anomaly detection rate from 3% to as low as 0.05% to 0.1%.
| Original language | English (US) |
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| Title of host publication | 6th Asia-Pacific Systems Workshop, APSys 2015 |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9781450335546 |
| DOIs | |
| State | Published - Jul 27 2015 |
| Externally published | Yes |
| Event | 6th Asia-Pacific Systems Workshop, APSys 2015 - Tokyo, Japan Duration: Jul 27 2015 → Jul 28 2015 |
Publication series
| Name | 6th Asia-Pacific Systems Workshop, APSys 2015 |
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Conference
| Conference | 6th Asia-Pacific Systems Workshop, APSys 2015 |
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| Country/Territory | Japan |
| City | Tokyo |
| Period | 7/27/15 → 7/28/15 |
Bibliographical note
Funding Information:Acknowledgments Thanks to the anonymous reviewers and our shepherd, Jia Wang, for their valuable feedback. This work was sponsored in part by the NSF under CNS-1405697 and CNS- 1422788 grants.
Publisher Copyright:
© 2015 ACM.