Efficient resource scaling based on load fluctuation in edge-cloud computing environment
| Autor: | Chunlin Li, Youlong Luo, Jingpan Bai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
020203 distributed computing
business.industry Computer science Distributed computing Cloud computing 02 engineering and technology Stochastic programming Theoretical Computer Science Resource (project management) Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Enhanced Data Rates for GSM Evolution business Integer programming Scaling Software Edge computing Information Systems |
| Zdroj: | The Journal of Supercomputing. 76:6994-7025 |
| ISSN: | 1573-0484 0920-8542 |
| DOI: | 10.1007/s11227-019-03134-8 |
| Popis: | With the rapid development of information technology, edge computing has grown rapidly by pushing large amounts of computing to the edge of the network. However, due to the rapid growth of edge access devices and limited edge storage space, the edge cloud faces many challenges in addressing the workloads. In this paper, a cost-optimized resource scaling strategy is proposed based on load fluctuation. Firstly, the load prediction model is built based on DBN with supervised learning to predict the workloads of edge cloud. Then, a cost-optimized resource scaling strategy is presented, which comprehensively considers reservation planning and on-demand planning. In the reservation phase, the long-term resource reservation problem is planned as a two-stage stochastic programming problem, which is transformed into a deterministic integer programming problem. In the on-demand phase, the on-demand resource scaling problem planning is solved as an integer programming problem. Finally, extensive experiments are conducted to evaluate the performance of the proposed cost-optimized resource scaling strategy based on load fluctuation. |
| Databáze: | OpenAIRE |
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