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Cite us

  • If you see my code and data useful and use it, please cite us as follows

    • Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.

    • Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.

  • If you want to know more about code, or want a pdf of both above paper, contact me: nguyenthieu2102@gmail.com

  • Take a look at this repos, the simplify code using python (numpy) for all algorithms above. (without neural networks)

How to read my repository

  1. data: include raw and formatted data
  2. envs: include conda environment and how to install conda environment
  3. utils: Helped functions such as IO, Draw, Math, Settings (for all model and parameters), Preprocessing...
  4. paper: include 2 main folders:
    • results: forecasting results of all models (3 folders inside)
      • final: final forecasting results (runs on server)
      • test: testing forecasting results (runs on personal computer, just for test)
      • temp: nothing (just for copy of test folder)
    • scaling: scaling results
  5. model: (4 folders)
    • root: (want to understand the code, read this classes first)
      • root_base.py: root for all models (traditional, hybrid and variants...)
      • root_algo.py: root for all optimization algorithms
      • traditional: root for all traditional models (inherit: root_base)
      • hybrid: root for all hybrid models (inherit: root_base)
    • optimizer: (this classes inherit: root_algo.py)
      • evolutionary: include algorithms related to evolution algorithm such as GA, DE,..
      • swarm: include algorithms related to swarm optimization such as PSO, CSO, BFO, ...
    • main: (final models)
      • this classes will use those optimizer above and those root (traditional, hybrid) above
      • the running files (outside with the orginial folder: prediction_flnn) will call this classes
      • the traditional models will use single file such as: traditional_ffnn, traditional_flnn,...
      • the hybrid models will use 2 files, example: hybrid_flnn.py and GA.py (optimizer files)
  6. special files
    • vms_real_used_CPU_RAM.csv (the real amount of CPU and RAM used in cloud): calculated by get_real_Vms_usages.py file
    • _scipt.py: running files (: represent model) such as flgann_script.py => FLNN + GA

Model

  1. ANN (1 HL) => mlnn1hl_script.py
  2. FLNN => flnn_script.py
  3. FL-GANN => flgann_script.py
  4. FL-DENN => fldenn_script.py
  5. FL-PSONN => flpsonn_script.py
  6. FL-ABCNN => flabcnn_script.py
  7. FL-BFONN => flbfonn_script.py
  8. FL-ABFOLSNN => flabfonn_script.py
  9. FL-CSONN => flcsonn_script.py

Notes

  1. To improve the speed of Pycharm when opening (because Pycharm will indexing when opening), you should right click to paper and data folder => Mark Directory As => Excluded

  2. When runs models, you should copy the running files to the original folder (prediction_flnn folder)

  3. Make sure you active the environment before run the running files

  • For terminal on linux
    source activate environment_name 
    python running_file.py
  1. In paper/results/final model includes folder's name represent the data such as
cpu: input model would be cpu, output model would be cpu 
ram: same as cpu
multi_cpu : input model would be cpu and ram, output model would be cpu 
multi_ram : input model would be cpu and ram, output model would be ram
multi : input model would be cpu and ram, output model would be cpu and ram