Name
Abstract | ||
|---|---|---|
In light of rapid increase of electricity cost, many business organizations have to find new ways to cut the electricity bill. This paper studies how to reduce the electricity cost in geographical inter-domain virtual network embedding, which embeds virtual networks requested by users to multiple geographically distributed substrate networks run by an infrastructure provider. Previous researches have primarily focused on finding embedding methods to increase revenues by accommodating more virtual network requests, with little attention to reducing the electricity cost. To bridge this gap, we formulate an electricity cost model and design an efficient cost-aware virtual network embedding algorithm by exploiting the location-varying and time-varying diversities of the electricity price and optimizing the energy consumption. Through extensive simulations, we show that our algorithm can significantly reduce the electricity cost by up to 21% over the existing cost-oblivious algorithm, while maintaining nearly the same revenues for the infrastructure provider. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/GLOCOM.2012.6503510 | GLOBECOM |
Keywords | Field | DocType |
business organizations,location-varying diversities,geographical inter-domain virtual network embedding,time-varying diversities,electricity price,electricity cost,organisational aspects,electricity bill,computer networks,energy consumption,pricing,telecommunication power supplies,cost-aware virtual network embedding | Electricity market,Revenue,Virtual network,Embedding,Electricity cost,Electricity,Computer science,Computer network,Virtual network embedding,Energy consumption | Conference |
Volume | Issue | ISSN |
null | null | 1930-529X E-ISBN : 978-1-4673-0919-6 |
ISBN | Citations | PageRank |
978-1-4673-0919-6 | 8 | 0.44 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongbao Zhang | 1 | 404 | 27.60 |
| Sen Su | 2 | 666 | 65.68 |
| Xinli Niu | 3 | 8 | 0.44 |
| Jiao Ma | 4 | 42 | 3.78 |
| Xiang Cheng | 5 | 410 | 28.18 |
| Kai Shuang | 6 | 330 | 28.68 |