Availability optimization of consistency and availability-based micro-service systems through elastic scheduling of container resources

Availability optimization of consistency and availability-based micro-service systems through elastic scheduling of container resources

Qinghui Ren Shenglin Li  Bo Song  Chen Chen 

Department of Information Engineering, Army Logistics University of PLA, Chongqing 401331, China

Corresponding Author Email: 
31 December 2018
| Citation



The availability of consistency (C) and availability (A)-based micro-service systems is low when both consistency and partition tolerance (P) are satisfied. Considering the low resource occupation and fast supply of containers, this paper puts forward an approach to optimize the availability of CP micro-service systems based on the elastic scheduling of container resources, and sets up a prediction model of response time using the cascade queuing system. Then, the author determined whether to relax, restrict or maintain the container resource in light of the conformity of the response time. Finally, the proposed optimization approach was verified through experiments. The results show that a 2~3s-long adaptation period is needed for the approach under abrupt load changes, and the response time can be accurately predicted to ensure the system availability in the other cases.


consistency (C), availability (A), partition tolerance (P), micro-service system, container, prediction model, elastic scheduling.

1. Introduction
2. Approach overview
3. System availability optimization
4. Experimental simulation and results analysis
5. Conclusions

Boettiger C. (2015). An introduction to Docker for reproducible research. Acm Sigops Operating Systems Review, Vol. 49, No. 1, pp. 71-79. https://doi.org/10.1145/2723872.2723882

Cai J. F., Chan R. H., Nikolova M. (2012). Two-phase approach for deblurring images corrupted by impulse plus Gaussian noise. Inverse Problems & Imaging, Vol. 2, No. 2, pp. 187-204.

Chen B., Liu X., Zhao H., Principe J. C. (2017). Maximum correntropy Kalman filter. Automatica, Vol. 76, pp. 70-77. http://dx.doi.org/10.1016/j.automatica.2016.10.004

Cherkasova L., Phaal P. (2002). Session-based admission control: A mechanism for peak load management of commercial web sites. IEEE Transactions on Computers, Vol. 51, No. 6, pp. 669-685. https://doi.org/10.1109/TC.2002.1009151

Decandia G., Hastorun D., Jampani M., Kakulapati G., Lakshman A., Pilchin A., Sivasubramanian S., Vosshall P., Vogels W. (2007). Dynamo: amazon's highly available key-value store. ACM Sigops Sym-posium on Operating Systems Principles. ACM, pp. 205-220. https://doi.org/10.1145/1323293.1294281

Degue K. H., Ny J. L. (2018). On differentially private Kalman filtering. IEEE Global Conference on Signal and Information Processing. IEEE, pp. 487-491. https://doi.org/10.1109/GlobalSIP.2017.8308690

Hao T. Y., Wu H., Wu G. Q., Zhang W. B. (2017). Elastic resource provisioning approach for container in micro-service architecture. Journal of Computer Research and Development, Vol. 54, No. 3, pp. 597-608. Http://dx.chinadoi.cn/10.7544/issn1000-1239.2017.20151043

Jiang W. Y., Bin L. I., Ling L. (2012). Research on data consistency and concurrency optimization of distributed system. Computer Engineering, Vol. 38, No. 4, pp. 260-262. http://www.ecice06.com/EN/10.3969/j.issn.1000-3428.2012.04.085

Karlsson M., Karamanolis C., Zhu X. (2009). Triage: Performance isolation and differentiation for storage systems. Twelfth IEEE International Workshop on Quality of Service. IEEE, pp. 67-74. https://doi.org/10.1109/IWQOS.2004.1309358

Newman S. (2015). Building microservices (First Edition). USA: O’Reilly Media, Inc, pp. 2-3.

Raja J. K., Prabhu V. (2017). An integrated software system for enterprise performance management. International Journal of Management & Decision Making, Vol. 8, No. 1, pp. 89-113.

Schafer D. R., Weiss A., Tariq M. A., Andrikopoulos V., Säez S., Krawczyk L., Rothermel K. (2016). HAWKS: A system for highly available executions of workflows. IEEE International Conference on Services Computing. IEEE, pp. 130-137. https://doi.org/10.1109/SCC.2016.24

Souri A., Pashazadeh S., Navin A. H. (2014). Consistency of data replication protocols in database systems: A review. International Journal on Information Theory, Vol. 3, No. 4, pp. 19-32.

Thönes J. (2015). Microservices. IEEE Software, Vol. 32, No. 1, pp. 116-116.

Wei H., Huang Y., Lu J. (2017). Probabilistically-atomic 2-atomicity: enabling almost strong consistency in distributed storage systems. IEEE Transactions on Computers, Vol. 66, No. 3, pp. 502-514. http://dx.doi.org/10.1109/TC.2016.2601322

You J., Zhang L., Wang H., Sun Y. (2015). JMeter-based aging simulation of computing system. International Conference on Computer, Mechatronics, Control and Electronic Engineering. IEEE, pp. 282-285. http://dx.doi.org/10.1109/CMCE.2010.5609969