Caliper
| Autor: | Jeongseob Ahn, Jason Mars, Lingjia Tang, Michael A. Laurenzano, Ram Srivatsa Kannan |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ACM Transactions on Architecture and Code Optimization. 16:1-25 |
| ISSN: | 1544-3973 1544-3566 |
| DOI: | 10.1145/3323090 |
| Popis: | We introduce Caliper , a technique for accurately estimating performance interference occurring in shared servers. Caliper overcomes the limitations of prior approaches by leveraging a micro-experiment-based technique. In contrast to state-of-the-art approaches that focus on periodically pausing co-running applications to estimate slowdown, Caliper utilizes a strategic phase-triggered technique to capture interference due to co-location. This enables Caliper to orchestrate an accurate and low-overhead interference estimation technique that can be readily deployed in existing production systems. We evaluate Caliper for a broad spectrum of workload scenarios, demonstrating its ability to seamlessly support up to 16 applications running simultaneously and outperform the state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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