Name
Abstract | ||
|---|---|---|
In this paper, we investigate optimal pricing models for profit maximization from the perspective of cloud providers in the presence of multiple classes of IaaS (Infrastructure as a Service) services. We propose an iterative model in which a cloud provider iteratively posts updated prices for the multiple classes of IaaS instances to users until reaching convergence that maximizes its profit. During this process, any interested user can determine the optimal class of IaaS instances and the optimal quantity to buy according to its own private utility function. In particular, we propose two algorithms to implement the iterative pricing process: a Genetic based near-optimal algorithm, and a hill climbing based cost-effective algorithm. The experimental results show that our iterative pricing algorithms can achieve advanced profitability in pricing multiclass IaaS instances in cloud environments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46295-0_39 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Pricing,IaaS,Cloud computing,Profit maximization | Convergence (routing),Data mining,Hill climbing,Mathematical optimization,Iterative and incremental development,Computer science,Simulation,Cloud provider,Profitability index,Profit maximization,Cloud computing | Conference |
Volume | ISSN | Citations |
9936 | 0302-9743 | 4 |
PageRank | References | Authors |
0.46 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo Zhang | 1 | 5 | 0.81 |
| Li Pan | 2 | 39 | 18.95 |
| Shijun Liu | 3 | 120 | 33.80 |
| Lei Wu | 4 | 73 | 17.47 |
| Li-zhen Cui | 5 | 282 | 71.41 |
| Dong Yuan | 6 | 768 | 48.06 |