Abstract
To satisfy quality of service requirements in a cost-efficient manner, cloud service providers would benefit from providing a means for quantifying the level of operational uncertainty within their systems. This uncertainty arises due to the dynamic nature of the cloud. Since tasks requiring various amounts of resources may enter and leave the system at any time, systems plagued by high volatility are challenging in preemptive resource provisioning. In this paper, we present a general method based on Dempster-Shafer theory that enables quantifying the level of operational uncertainty in an entire cloud system or parts thereof. In addition to the standard quality metrics, we propose monitoring of system calls to capture historical behavior of virtual machines as an input to the general method. Knowing the level of operational uncertainty enables greater accuracy in online resource provisioning by quantifying the volatility of the deployed system.
| Original language | Undefined/Unknown |
|---|---|
| Title of host publication | Database and Expert Systems Applications (DEXA), 2017 28th International Workshop on |
| Editors | Haithem Mezni, Sabeur Aridhi, Allel Hadjali |
| Publisher | IEEE |
| Pages | 127–131 |
| ISBN (Electronic) | 978-1-5386-1051-0 |
| ISBN (Print) | 978-1-5386-2207-0 |
| DOIs | |
| Publication status | Published - 2017 |
| MoE publication type | A4 Article in a conference publication |
| Event | Database and Expert Systems Applications (DEXA) - 28th International Workshop on Database and Expert Systems Applications (DEXA) Duration: 28 Aug 2017 → 31 Aug 2017 |
Conference
| Conference | Database and Expert Systems Applications (DEXA) |
|---|---|
| Period | 28/08/17 → 31/08/17 |
Keywords
- Cloud uncertainty
- Kernel methods
- Virtual machine provisioning