Skip to main content
SpringerLink
Log in

Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In the paper, the distributed scheduling optimization model for resource-intensive mobile application is proposed. Lagrangian method is applied to achieve distributed scheduling optimization in hybrid cloud. By decomposing the Kuhn–Tucker conditions into different roles of mobile user, public cloud supplier and local cloud supplier, the scheduling optimization problem in hybrid cloud is converted into a distributed problem. The system design and example of distributed scheduling optimization for resource intensive mobile application is also given. The local or public cloud provider uses service-level agreement (SLA) in determining the share of resources to be allocated to the mobile user. The distributed scheduling optimization algorithm for resource intensive mobile application is proposed, which includes three parts: local cloud agent scheduling optimization, public cloud service scheduling and mobile application QoS optimization. The experiments study how data size, request arrival rate, number of mobile users and mobility have effect on the proposed algorithm and other related works.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Kovachev, D., Cao, Y., Klamma, R.: Building mobile multimedia services: a hybrid cloud computing approach. Multimed. Tools Appl. 70(2), 977–1005 (2014)

    Article  Google Scholar 

  2. Cole, Y., Zhang, H., Ge, L., et al.: ScanMe mobile: a local and cloud hybrid service for analyzing apks. Res. Adapt. Converg. Syst. RACS 2015, 268–273 (2015)

  3. Khalifa, A., Azab, M., Eltoweissy, M.: Resilient hybrid Mobile Ad-hoc cloud over collaborating heterogeneous nodes. In: 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom, pp. 134–143 (2014)

  4. Kazi, R., Deters, R.A.: Cloud-hosted hybrid framework for consuming Web Services on mobile devices. In: 2013 International Conference on Selected Topics in Mobile and Wireless Networking, MoWNeT, pp. 106–111 (2013)

  5. Skourletopoulos, G., Mavromoustakis, C.X., Mastorakis, G., et al.: An evaluation of cloud-based mobile services with limited capacity: a linear approach. Soft Comput. 21, 4523–4530 (2016)

  6. Bourdena, A., Mavromoustakis, C.X., Mastorakis, G., et al.: Using socio-spatial context in mobile cloud process offloading for energy conservation in wireless devices. IEEE Trans. Cloud Comput. 99, 1 (2015)

  7. Cerviño, J., Rodríguez, P., Trajkovska, I., et al.: A cost-effective methodology applied to videoconference services over hybrid clouds. Mob. Netw. Appl. 18(1), 103–109 (2013)

    Article  Google Scholar 

  8. Reiter, A., Zefferer, T.: Flexible and secure resource sharing for mobile augmentation systems. In: 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud, pp. 31–40 (2016)

  9. Farris, I., Militano, L., Nitti, M., et al.: Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments. In: 2nd IEEE World Forum on Internet of Things. WF-IoT, vol. 2015, pp. 591–596 (2015)

  10. Mavromoustakis, C.X., Andreou, A., Mastorakis, G., et al.: On the performance evaluation of a novel offloading-based energy conservation mechanism for wireless devices. In: 6th International ICST Conference on Mobile Networks and Management, MONAMI, pp. 179–191 (2014)

  11. Viswanathan, H., Lee, E.K., Rodero, I., et al.: Uncertainty-aware autonomic resource provisioning for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(8), 2363–2372 (2015)

    Article  Google Scholar 

  12. Viswanathan, H., Pandey, P., Pompili, D.: Maestro: orchestrating concurrent application workflows in mobile device clouds. In: 2016 13th IEEE International Conference on Autonomic Computing, ICAC, pp. 257–262 (2016)

  13. Shakkeera, L., Tamilselvan, L.: Energy-aware application scheduling and consolidation in mobile cloud computing with load balancing, emerging research in computing, information, communication and applications. Springer, Berlin, pp. 253–264 (2016)

  14. Rashidi, S., Sharifian, S.: A hybrid heuristic queue based algorithm for task assignment in mobile cloud. Future Gener. Comput. Syst. 68, 331–345 (2017)

  15. Care, R., Hassan, H.A.H., Suárez, L., et al.: Energy-efficient scheduling for cloud mobile gaming. IEEE Globecom Workshops. GC Wkshps 2014, 1198–1204 (2014)

  16. Barbarossa, S., Sardellitti, S., Di Lorenzo, P.: Joint allocation of computation and communication resources in multiuser mobile cloud computing. In: IEEE 14th Workshop on Signal Processing Advances in Wireless Communications. SPAWC 2013, 26–30 (2013)

  17. Miao, D., Zhu, W., Luo, C., et al.: Resource allocation for cloud-based free viewpoint video rendering for mobile phones. In: 19th ACM International Conference on Multimedia ACM Multimedia, MM’11, pp. 1237–1240 (2011)

  18. Choi, S.K., Chung, K.S., Yu, H.: Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Clust. Comput. 17(3), 911–926 (2014)

    Article  Google Scholar 

  19. Al-Sharif, Z.A., Jararweh, Y., Al-Dahoud, A., et al.: ACCRS: autonomic based cloud computing resource scaling. Clust. Comput. 20(3), 2479–2488 (2017)

  20. Wang, K., Yang, K., Magurawalage, C.: Joint energy minimization and resource allocation in C-RAN with mobile cloud. IEEE Trans. Cloud Comput. 99, 1 (2016)

  21. Chakroun, O., Cherkaoui, S.: Resource allocation for delay sensitive applications in mobile cloud computing. In: 41st IEEE Conference on Local Computer Networks, LCN, pp. 615–618 (2016)

  22. Raei, H., Yazdani, N.: Analytical performance models for resource allocation schemes of cloudlet in mobile cloud computing. J. Supercomput. 73(3), 1274–1305 (2017)

    Article  Google Scholar 

  23. Mohammed, M.H., Baothman, F.: Intelligent workload management of computing resource allocation for mobile cloud computing. Int. J. Comput. Org. Trends 19(1), 8–19 (2015)

    Google Scholar 

  24. Pompili, D., Hajisami, A., Viswanathan, H.: Dynamic provisioning and allocation in Cloud Radio Access Networks (C-RANs). Ad Hoc Netw. 30, 128–143 (2015)

    Article  Google Scholar 

  25. Bohez, S., Verbelen, T., Simoens, P., et al.: Allocation algorithms for autonomous management of collaborative cloudlets. In: 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 1–9 (2014)

  26. Deng, R., Lu, R., Lai, C., et al.: Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)

    Google Scholar 

  27. Chang, Z., Gong, J., Zhou, Z., et al.: Resource allocation and data offloading for energy efficiency in wireless power transfer enabled collaborative mobile clouds. In: 2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS, pp. 336–341 (2015)

  28. Aral, A.: Modeling and optimization of resource allocation in distributed clouds. In: 2016 IEEE International Conference on Cloud Engineering Workshops, IC2EW, pp. 210–212 (2016)

  29. Meng, S., Wang, Y., Miao, Z., et al.: Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment. Peer-to-Peer Netw. Appl. (2017). doi:10.1007/s12083-017-0544-x

  30. Nishio, T., Shinkuma, R., Takahashi, T., et al.: Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud. In: 2013 1st International Workshop on Mobile Cloud Computing and Networking, MobileCloud, pp. 19–26 (2013)

  31. Chunlin, Li, Xin, Y., LaYuan, Li: Flexible service provisioning based on context constraint for enhancing user experience in service oriented mobile cloud. J. Netw. Comput. Appl. 66, 250–261 (2016)

    Article  Google Scholar 

  32. Chunlin, L., Layuan, L.: Cost and energy aware service provisioning for mobile client in cloud computing environment. J. Supercomput. 71(4), 1196–1223 (2015)

    Article  Google Scholar 

  33. Chunlin, L., Layuan, L.: Exploiting composition of mobile devices for maximizing user QoS under energy constraints in mobile grid. Inf. Sci. 279(20), 654–670 (2014)

    Article  Google Scholar 

  34. Kelly, F., Maulloo, A., Tan, D.: Rate control for communication networks: shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49(3), 237–252 (1998)

    Article  MATH  Google Scholar 

  35. Everett, H.: Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Oper. Res. 11(3), 399–417 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  36. Luh, P.B., Hoitomt, D.J.: Scheduling of manufacturing systems using the Lagrangian relaxation technique. IEEE Trans. Autom. Control 38(7), 1066–1079 (1993)

    Article  MathSciNet  Google Scholar 

  37. Kuhn, H.W., Tucker, A.W.: “Nonlinear programming”. In: Proceedings of 2nd Berkeley Symposium. University of California Press, Berkeley, pp. 481–492 (1951)

  38. Amazon EC2 instances, http://aws.amazon.com/ec2/instance-types/

  39. Youku. Available on http://www.youku.com/, September 2016

Download references

Acknowledgements

The authors thank the editors and the anonymous reviewers for their helpful comments and suggestions. The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61472294), the Fundamental Research Funds for the Central Universities (WUT No. 2017-YB-029), Wuhan University of Technology and Program for the High-end Talents of Hubei Province. Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Author information

Authors and Affiliations

  1. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Li Chunlin & Tang Jianhang

  2. Hebei Engineering Technology Research Center for IOT Data acquisition & Processing, North China Institute of Science and Technology, Hebei, 065201, People’s Republic of China

    Li Chunlin

  3. School of Management, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Luo Youlong

Authors
  1. Li Chunlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tang Jianhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luo Youlong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Chunlin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chunlin, L., Jianhang, T. & Youlong, L. Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud. Cluster Comput 21, 1331–1348 (2018). https://doi.org/10.1007/s10586-017-1171-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1171-2

Keywords

Search

Navigation