Name
Abstract | ||
|---|---|---|
We study cloud resource control in the global-local distributed cloud infrastructure. We firstly model and formulate the problem while capturing the multiple challenges such as the inter-dependency between resources and the uncertainty in the inputs. We then propose a novel online algorithm which, via the regularization technique, decouples the original problem into a series of subproblems for individual time slots and solves both the subproblems and the original problem over every prediction time window to jointly make resource allocation decisions. Compared against the offline optimum with accurate inputs, our approach maintains a provable parameterized worst-case performance gap with only inaccurate inputs under certain conditions. Finally, we conduct evaluations with large-scale, real-world data traces and show that our solution outperforms existing methods and works efficiently with near-optimal cost in practice. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IWQoS.2018.8624119 | 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) |
Keywords | Field | DocType |
edge resources,cloud resource control,inter-dependency,regularization technique,resource allocation decisions,distributed cloud infrastructure,worst-case performance,online algorithm,optimal cost | Online algorithm,Mathematical optimization,Parameterized complexity,Computer science,Real-time computing,Regularization (mathematics),Resource allocation,Performance gap,Cloud computing | Conference |
ISSN | ISBN | Citations |
1548-615X | 978-1-5386-2543-9 | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Jiao | 1 | 732 | 54.48 |
| Antonia Maria Tulino | 2 | 325 | 33.17 |
| Jaime Llorca | 3 | 407 | 37.47 |
| Yue Jin | 4 | 10 | 2.96 |
| Alessandra Sala | 5 | 21 | 4.29 |
| Jun Li | 6 | 204 | 34.80 |