Name
Abstract | ||
|---|---|---|
When deploying applications with dynamic and intensive memory footprint to big data systems on public clouds, one important yet challenging question to answer is how to select a specific instance type whose memory capacity is large enough to prevent out-of-memory errors while the cost is minimized without violating performance requirements. The state-of-the-practice solution is trial and error, causing both performance overhead and additional monetary cost. This article investigates two memory scaling mechanisms in public clouds: physical memory (good performance and high cost) and virtual memory (degraded performance and no additional cost). In order to analyze the trade-off between performance and cost of the two scaling options, a performance-cost model is developed that is driven by a lightweight analytic prediction approach through a compact representation of the memory footprint. In addition, for those scenarios when the footprint is unavailable, a meta-model-based prediction method is proposed using just-in-time migration mechanisms. The proposed techniques have been extensively evaluated with various benchmarks and real-world applications on Amazon Web Services: the performance-cost model is highly accurate and the proposed just-in-time migration approach reduces the monetary cost by up to 66 percent. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TBDATA.2020.3035522 | IEEE Transactions on Big Data |
Keywords | DocType | Volume |
Memory management,resource allocation,cloud computing | Journal | 8 |
Issue | ISSN | Citations |
5 | 2332-7790 | 0 |
PageRank | References | Authors |
0.34 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinying Wang | 1 | 0 | 0.34 |
| Cong Xu | 2 | 1154 | 48.25 |
| Ke Wang | 3 | 313 | 13.66 |
| Feng Yan | 4 | 163 | 18.78 |
| Dongfang Zhao | 5 | 362 | 26.49 |