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| Content Provider | ACM Digital Library |
|---|---|
| Author | Singh, Rahul Shenoy, Prashant Sharma, Upendra Cecchet, Emmanuel |
| Abstract | Online Internet applications see dynamic workloads that fluctuate over multiple time scales. This paper argues that the non-stationarity in Internet application workloads, which causes the request mix to change over time, can have a significant impact on the overall processing demands imposed on data center servers. We propose a novel mix-aware dynamic provisioning technique that handles both the non-stationarity in the workload as well as changes in request volumes when allocating server capacity in Internet data centers. Our technique employs the k-means clustering algorithm to automatically determine the workload mix and a queuing model to predict the server capacity for a given workload mix. We implement a prototype provisioning system that incorporates our technique and experimentally evaluate its efficacy on a laboratory Linux data center running the TPC-W web benchmark. Our results show that our k-means clustering technique accurately captures workload mix changes in Internet applications. We also demonstrate that mix-aware dynamic provisioning eliminates SLA violations due to under-provisioning with non-stationary web workloads, and that it offers a better resource usage by reducing over-provisioning when compared to a baseline provisioning approach that only reacts to workload volume changes. We also present a case study of our provisioning approach on Amazon's EC2 cloud platform. |
| Starting Page | 21 |
| Ending Page | 30 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450300742 |
| DOI | 10.1145/1809049.1809053 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-06-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Availability Performance Modeling |
| Content Type | Text |
| Resource Type | Article |
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