Name
Abstract | ||
|---|---|---|
As heterogeneous multicore systems become a standard in computing devices, there is an increasing need for intelligent and adaptive resource allocation schemes to achieve a balance between performance and energy consumption. To support this growing need, researchers have explored a plethora of techniques to guide OS scheduling policies, including machine learning, statistical regression and custom heuristics. Such techniques have been enabled by the abundance of low-level performance counters, and have proven effective in characterizing applications as well as predicting power and performance. However, most works require and develop custom infrastructures. In this paper we present LUSH, a Lightweight Framework for User-level Scheduling in Heterogeneous Multicores that allows for users to develop their own customized scheduling policies, without requiring root privileges. LUSH contributes the following to the state-of-the-art: (1) a mechanism for monitoring application runtime behavior using performance counters, (2) a mechanism for exporting kernel data to user-level at a user-defined period; and (3) a parameterized and flexible interface for developing, deploying, and evaluating novel algorithms applied to OS scheduling policies. The framework presented in this paper serves as a foundation for exploring advanced and intelligent techniques for resource management in heterogeneous systems. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MCSoC51149.2021.00065 | 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) |
Keywords | DocType | ISBN |
Resource allocation,Optimization,Heterogeneous architectures | Conference | 978-1-7281-8752-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasco Miguel Liang Xu | 1 | 0 | 0.34 |
| Liam White McShane | 2 | 0 | 0.34 |
| Daniel Mossé | 3 | 2184 | 148.86 |