Optimal Pricing for the Competitive and Evolutionary Cloud Market
Authors: Bolei Xu, Tao Qin, Guoping Qiu, Tie-Yan Liu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulations demonstrate the effectiveness of our proposed approach. We carry out a large set of numerical experiments and show that our proposed approach can help a provider to achieve much better profit against his/her reactive opponents than other simple pricing policies. |
| Researcher Affiliation | Collaboration | Bolei Xu The University of Nottingham Ningbo China Bolei.Xu@nottingham.edu.cn Tao Qin Microsoft Research taoqin@microsoft.com Guoping Qiu The University of Nottingham Ningbo China Guoping.Qiu@nottingham.edu.cn Tie-Yan Liu Microsoft Research tyliu@microsoft.com |
| Pseudocode | Yes | Algorithm 1 Q-learning for a stationary market; Algorithm 2 Backward induction for an evolutionary market |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a URL or an explicit statement of code release) for its source code. |
| Open Datasets | No | The paper describes numerical simulations with specific parameter settings but does not refer to a publicly available dataset or provide access information for any dataset used in training or evaluation. |
| Dataset Splits | No | The paper describes numerical simulations and parameter settings but does not mention specific training, validation, or test dataset splits, as it does not use pre-existing datasets in the traditional sense. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instance types) used for running its numerical simulations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers that would be required to replicate the experiments (e.g., specific programming languages, libraries, or solvers with their versions). |
| Experiment Setup | Yes | Parameter Settings For the simulations in this subsection, we consider a market with K = 3 providers. ... We set the threshold price ω = 0.1 in all the scenarios. ... For the simulations, we set the initial population number N0 = 100, the saturated population number is N = 10, 000, and the evolution rate κ = 0.07. ... We assume the users marginal value θj derived from cloud service follows a uniform distribution supported on [1, 5] and the users demand follows an exponential distribution and set the parameter Λ in Eq. (2) to 2. We set β = 0.01 and η = 0.02 for provider s marginal cost function in Eq. (4). For the Q-learning algorithm, we set learning rate α = 0.1 1+0.1j which is decreasing with respect to the iteration step j and the stop criteria δ = 1. |