Computing aware scheduling in mobile edge computing system

  • Ke WangEmail author
  • XiaoYi Yu
  • WenLiang Lin
  • ZhongLiang Deng
  • Xin Liu


Mobile edge computing (MEC) is an emerging technology recognized as an effective solution to guarantee the Quality of Service for computation-intensive and latency-critical traffics. In MEC system, the mobile computing, network control and storage functions are deployed by the servers at the network edges (e.g., base station and access points). One of the key issue in designing the MEC system is how to allocate finite computational resources to multi-users. In contrast with previous works, in this paper we solve this issue by combining the real-time traffic classification and CPU scheduling. Specifically, a support vector machine based multi-class classifier is adopted, the parameter tunning and cross-validation are designed in the first place. Since the traffic of same class has similar delay budget and characteristics (e.g. inter-arrival time, packet length), the CPU scheduler could efficiently scheduling the traffic based on the classification results. In the second place, with the consideration of both traffic delay budget and signal baseband processing cost, a preemptive earliest deadline first (EDF) algorithm is deployed for the CPU scheduling. Furthermore, an admission control algorithm that could get rid off the domino effect of the EDF is also given. The simulation results show that, by our proposed scheduling algorithm, the classification accuracy for specific traffic class could be over 82 percent, meanwhile the throughput is much higher than the existing scheduling algorithms.


MEC SVM EDF Scheduling Admission control 



This work is jointly supported by Project 61501052 and 61602538 of the National Natural Science Foundation of China, and Project D010109.


  1. 1.
    Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358.CrossRefGoogle Scholar
  2. 2.
    Medium access control (MAC) protocol specification, 3GPP Std. TS 36.321, Sep. 15.3.0 (2018).Google Scholar
  3. 3.
    Policy and charging control architecutre, 3GPP Std. TS 23.203, Sep. 15.4.0 (2018).Google Scholar
  4. 4.
    Capozzi, F., Piro, G., Grieco, L. A., Boggia, G., & Camarda, P. (2013). Downlink packet scheduling in lte cellular networks: Key design issues and a survey. IEEE Communications Surveys & Tutorials, 15(2), 678–700.CrossRefGoogle Scholar
  5. 5.
    Wanstedt, S. (2007). Mixed traffic hsdpa scheduling-impact on voip capacity. In Vehicular Technology Conference (2007). VTC2007-Spring. IEEE 65th. IEEE (pp. 1282–1286).Google Scholar
  6. 6.
    Shakkottai, S., & Stolyar, A . L. (2002). Scheduling for multiple flows sharing a time-varying channel: The exponential rule. Translations of the American Mathematical Society-Series 2, 207, 185–202.MathSciNetzbMATHGoogle Scholar
  7. 7.
    Wang, K., Yang, K., Chen, H. H., & Zhang, L. (2017). Computation diversity in emerging networking paradigms. IEEE Wireless Communications, 24(1), 88–94.CrossRefGoogle Scholar
  8. 8.
    Nikaein, N. (2015). Processing radio access network functions in the cloud: Critical issues and modeling. In Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services. ACM (pp. 36–43).Google Scholar
  9. 9.
    Valenti, M. C., Talarico, S., & Rost, P. (2014). The role of computational outage in dense cloud-based centralized radio access networks. In 2014 IEEE Global Communications Conference. IEEE (pp. 1466–1472).Google Scholar
  10. 10.
    Rost, P., Maeder, A., Valenti, M. C., & Talarico, S. (2015 ). Computationally aware sum-rate optimal scheduling for centralized radio access networks. In 2015 IEEE Global Communications Conference (GLOBECOM). IEEE (pp. 1–6).Google Scholar
  11. 11.
    Guo, K., & Sheng, M. (2016). Cooperative transmission meets computation provisioning in downlink c-ran. In 2016 IEEE International Conference on Communications (ICC). IEEE (pp. 1–6).Google Scholar
  12. 12.
    Ha, V. N., & Le, L. B. (2016). Computation capacity constrained joint transmission design for c-rans. In 2016 IEEE Wireless Communications and Networking Conference. IEEE (pp. 1–6).Google Scholar
  13. 13.
    Liao, Y., Song, L., Li, Y., & Zhang, Y. A. (2016). Radio resource management for cloud-ran networks with computing capability constraints. In 2016 IEEE International Conference on Communications (ICC). IEEE (pp. 1–6).Google Scholar
  14. 14.
    Molina Pena, M., Muñoz Medina, O., Pascual Iserte, A., & Vidal Manzano, J. (2014). Joint scheduling of communication and computation resources in multiuser wireless application offloading. In Proceedings PIMRC 2014. Institute of Electrical and Electronics Engineers (IEEE) (pp. 1093–1098).Google Scholar
  15. 15.
    Yu, Y., Zhang, J., & Letaief, K. B. (2016). Joint subcarrier and cpu time allocation for mobile edge computing. In Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE (pp. 1–6).Google Scholar
  16. 16.
    Yang, L., Cao, J., Cheng, H., & Ji, Y. (2015). Multi-user computation partitioning for latency sensitive mobile cloud applications. IEEE Transactions on Computers, 64(8), 2253–2266.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Jing, N., Yang, M., Cheng, S., Dong, Q., & Xiong, H. (2011). An efficient svm-based method for multi-class network traffic classification. In Performance Computing and Communications Conference (IPCCC) (2011). IEEE 30th International. IEEE (pp. 1–8).Google Scholar
  18. 18.
    Hao, S., Hu, J., Liu, S., Song, T., Guo, J., & Liu, S. (2015). Improved svm method for internet traffic classification based on feature weight learning. In 2015 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE (pp. 102–106).Google Scholar
  19. 19.
    Yamansavascilar, B., Guvensan, M. A., Yavuz, A. G., & Karsligil, M. E. (2017). Application identification via network traffic classification. In 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE (pp. 843–848).Google Scholar
  20. 20.
    Li, Z., Yuan, R. , & Guan, X. (2007). Accurate classification of the internet traffic based on the svm method. In IEEE International Conference on Communications, ICC’07. IEEE (pp. 1373–1378).Google Scholar
  21. 21.
    Bhaumik, S., Chandrabose, S. P., Jataprolu, M. K., Kumar, G., Muralidhar, A., Polakos, P., Srinivasan, V., & Woo, T. (2012). Cloudiq: A framework for processing base stations in a data center. In Proceedings of the 18th annual international conference on Mobile computing and networking. ACM (pp. 125–136).Google Scholar
  22. 22.
    Liu, C. L., & Layland, J. W. (1973). Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM, 20(1), 46–61.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Bastoni, A., Brandenburg, B. B., & Anderson, J. H. (2010). An empirical comparison of global, partitioned, and clustered multiprocessor edf schedulers. In Real-Time Systems Symposium (RTSS) (2010). IEEE 31st. IEEE (pp. 14–24).Google Scholar
  24. 24.
    Sesia, S., Toufik, I., & Baker, M. (2009). LTE, the UMTS long term evolution: From theory to practice. New York: Wiley.CrossRefGoogle Scholar
  25. 25.
    Wang, K., & Cen, Y. (2017). Real-time partitioned scheduling in cloud-ran with hard deadline constraint. In 2017 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6).Google Scholar
  26. 26.
     Physical layer procedures (fdd), 3GPP Std. TS 36.213, Sep. 15.3.0 (2018).  Google Scholar
  27. 27.
    Nguyen, T. T. T., & Armitage, G. (2009). A survey of techniques for internet traffic classification using machine learning. IEEE, 10(4), 56–76.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China

Personalised recommendations