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ServeNet: A Deep Neural Network for Web Service Classification

Automated service classification plays a crucial role in service management such as service discovery, selection, and composition. In recent years, machine learning techniques have been used for service classification. However, they can only predict around 10 to 20 service categories due to the quality of feature engineering and the imbalance problem of service dataset. In this project, we present a deep neural network ServeNet with a novel dataset splitting algorithm to deal with these issues. ServeNet can automatically abstract low-level representation to high-level features, and then predict service classification based on the service datasets produced by the proposed splitting algorithm. To demonstrate the effectiveness of our approach, we conducted a comprehensive experimental study on 10,000 real-world services in 50 categories. The result shows that ServeNet can achieve higher accuracy than other machine learning methods.

Please cite our paper as follows:

DataSet in Release:

  • RAW data
  • CSV
  • hdf5 or h5
  • Tensor in pickle

Reuseable Models in Release:

  • BILSTM.hdf5
  • CLSTM.hdf5
  • CNN.hdf5
  • LSTM.hdf5
  • RCNN.hdf5
  • ServeNet.hdf5
  • ServeNet-ICWS20.hdf5

Development

(Online) Google Colab

Directly open .ipynb in Google Colab and prepare data

(Local) Start jupyter lab with docker

CPU

  • docker build . -t servenet:cpu -f Dockerfile-CPU
  • docker run -itd --rm --name servenet-cpu -p 8888:8888 -v /yourpath:/data servenet:cpu

Find URL in log to open jupyter lab

  • docker logs servenet-cpu

GPU

  • docker build . -t servenet:gpu -f Dockerfile-GPU
  • docker run -itd --rm --runtime=nvidia --name servenet-gpu -p 8888:8888 -v /yourpath:/data servenet:gpu

Find URL in log to open jupyter lab

  • docker logs servenet-gpu