Instance Expansion Algorithm for Micro-service with Prediction

DOI

10.2991/jiaet-18.2018.59How to use a DOI?

Keywords

Micro-service; Instance expansion; Prediction; Maximum correntropy criterion

Abstract

Instances expansion of micro-service consumes time for constructing new instances, it can’t satisfy the requirement of low latency service, such as scientific calculation workflows. In order to reduces the instance expansion time, an instance expansion algorithm for micro-service with prediction is proposed in this letter, which sets correntry as cost function and uses MCC (Maximum Correntropy Criterion) to filter the burst service requirements to improve the accuracy of prediction, and use stochastic gradient descent algorithm to train the data set to predict the required micro-service instances in the next time. The performance of the proposed algorithm are analysed in real experiment telecom office with the compared ones using A/B test, and the experiment results show that the proposed algorithm has 70% less time of instance expansion than the compared ones, and the accuracy of the proposed algorithm is 20% more than the compared one which uses LMS as the cost function.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qing Lin
AU  - Jinhuan Wang
AU  - Jing Wang
PY  - 2018/03
DA  - 2018/03
TI  - Instance Expansion Algorithm for Micro-service with Prediction
BT  - Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)
PB  - Atlantis Press
SP  - 333
EP  - 337
SN  - 2352-5401
UR  - https://doi.org/10.2991/jiaet-18.2018.59
DO  - 10.2991/jiaet-18.2018.59
ID  - Lin2018/03
ER  -