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Transforming reactive auto-scaling into proactive auto-scaling
| Content Provider | ACM Digital Library |
|---|---|
| Author | Ellahi, Tariq Moore, Laura R. Bean, Kathryn |
| Abstract | Elasticity is a key characteristic of cloud platforms enabling resource to be acquired on-demand in response to time-varying workloads. We introduce a new elasticity management framework that takes as input commonly used reactive rule-based scaling strategies but offers in return proactive auto-scaling. The elasticity framework combines reactive and predictive auto-scaling techniques, and we discuss the specification and performance of these individual components. We present a case study, based on real datasets, to demonstrate that our framework is capable of making appropriate auto-scaling decisions that can improve resource utilization compared to that obtained from a purely reactive approach. |
| Starting Page | 7 |
| Ending Page | 12 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450320757 |
| DOI | 10.1145/2460756.2460758 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-04-14 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Cloud computing Auto-scaling Predictive Elasticity Platform-as-a-service |
| Content Type | Text |
| Resource Type | Article |
