Learning Resource Allocation and Pricing for Cloud Profit Maximization
Authors: Bingqian Du, Chuan Wu, Zhiyi Huang7570-7577
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation based on real-world datasets shows that our DRL approach outperforms basic DRL algorithms and state-of-the-art white-box online cloud resource allocation/pricing algorithms significantly, in terms of both profit and the number of accepted users. |
| Researcher Affiliation | Academia | Bingqian Du, Chuan Wu, Zhiyi Huang The University of Hong Kong |
| Pseudocode | Yes | Algorithm 1: DRL Algorithm for VM Placement and Pricing, LAPP |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We make use of two sets of public traces: (i) Microsoft Azure dataset (Cortez et al. 2017), ... (ii) Google cluster-usage dataset (Reiss, Wilkes, and Hellerstein 2011)... |
| Dataset Splits | Yes | We extract one week s workload of a subscription from the Azure dataset for training our DRL model. |
| Hardware Specification | Yes | We implement LAPP using Tensor Flow on a server equipped with one Nvidia GTX 1080 GPU, Intel Xeon E5-1620 CPU with 4 cores, and 32GB memory. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify a version number for it or any other key software dependencies. |
| Experiment Setup | Yes | The actor NN we use has 300 and 400 neurons in the two fully-connected layers, respectively, and the output from the LSTM is a vector of 256 units (Ming et al. 2017); the activation function is softmax for outputting vi1 and rectifier for outputting vi2. The critic NN has 400 neurons in each fully-connected layer and the output of the LSTM layer has a size of 256; the activation function is rectifier for its output layer. The learning rates in the actor network and the critic network are 10 4 and 10 4, respectively. We set Batch Size = 32, γ = 0.99, and L = 4. |