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Computation offloading balance in small cell networks with mobile edge computing

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Abstract

As a promising way to offset the low computation capacity of user equipments (UEs), mobile edge computing (MEC) has attracted great attention of academy and industrial recently. By deploying MEC servers in small cell networks, the communication rate and computation speed could be enhanced efficiently. However, due to the different computation capability of various small cell base stations, micro base stations and UEs, the computation offloading balance becomes a challenging problem. Furthermore, the wireless channel condition and transmission delay also take important roles in the computation offload. In this paper, we jointly consider the spectrum allocation and computation offload selection to achieve desired balance in small cell networks with MEC. In this process, we formulate the spectrum allocation factor, computation offloading decision and UEs’ transmission power as an optimization problem with the objective of minimizing the energy consumption and the constraints of each UE’s latency. Then, we propose a two-step solution algorithm to solve the formulated problem. Finally, simulation results are presented to show the effectiveness of the proposed scheme in the performance of energy consumption, latency, computation offloading ratio and convergence.

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Notes

  1. Each UE can get the information of \(d_k\) and \(c_k\) by the method in [27]

  2. The maximum intra-tier interference temperature Q is not an optimization variable in the proposed framework. However, a suitable value of Q can be found via simulation in an off-line manner.

References

  1. Alcatel-Lucent. (2011). The declining profitability trend of mobile data: What can be done? Assessing network costs and planning for sustainable revenue growth. Market analysis (Technical Report).

  2. Saeid, A., Zohreh, S., Ejaz, A., Abdullah, G., & Rajkumar, B. (2014). Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open challenges. IEEE Communications Surveys & Tutorials, 16(1), 337–368.

    Article  Google Scholar 

  3. Fortes, S., Aguilar-Garcia, A., Barco, R., Barba, F., Fernandez-luque, J., & Fernandez-Duran, A. (2015). Management architecture for location-aware self-organizing LTE/LTE-a small cell networks. IEEE Communications Magazine, 53(1), 294–302.

    Article  Google Scholar 

  4. Hoadley, J., & Maveddat, P. (2012). Enabling small cell deployment with HetNet. IEEE Wireless Communications, 19(2), 4–5.

    Article  Google Scholar 

  5. Kumar, K., & Lu, Y. (2010). Cloud computing for mobile users: Can offloading computation save energy? Computer, 43(4), 51–56.

    Article  Google Scholar 

  6. Wang, Y., Sheng, M., Wang, X., Wang, L., et al. (2015). Energy-optimal partial computation offloading using dynamic voltage scaling. In Proceedings of IEEE international conference on communications workshop (ICC Workshop), pp. 2695–2700.

  7. Wang, Y., Sheng, M., Wang, X., Wang, L., & Li, J. (2016). Mobile-edge computing: Partial computation offloading using dynamic voltage scaling. IEEE Transactions on Communications, 64(10), 4268–4282.

    Google Scholar 

  8. Kamoun, M., Labidi, W., & Sarkiss, M. (2015). Joint resource allocation and offloading strategies in cloud enabled cellular networks. In IEEE international conference on communications (ICC), pp. 5529–5534.

  9. Olga, M., Antonio, P., & Joseph, V. (2015). Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading. IEEE Transactions on Vehicular Technology, 64(10), 4738–4755.

    Article  Google Scholar 

  10. Xiang, X., Lin, C., & Chen, X. (2014). Energy-efficient link selection and transmission scheduling in mobile cloud computing. IEEE Wireless Communications Letters, 3(2), 153–156.

    Article  Google Scholar 

  11. Barbarossa, S., Sardellitti, S., & Di Lorenzo, P. (2013). Joint allocation of computation and communication resources in multiuser mobile cloud computing. In Proceedings of workshop on signal processing advances in wireless communications (SPAWC), pp. 26–30.

  12. Sardellitti, S., Scutari, G., & Barbarossa, S. (2015). Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Transactions on Signal and Information Processing Over Networks, 1(2), 89–103.

    Article  MathSciNet  Google Scholar 

  13. Sardellitti, S., Barbarossa, S., & Scutari, G. (2014). Distributed mobile cloud computing: Joint optimization of radio and computational resources. In Proceedings of IEEE global communications conference workshops (Globecom Workshops), pp. 1505–1510.

  14. Cao, Y., Jiang, T., & Wang, C. (2014). Optimal radio resource allocation for mobile task offloading in cellular networks. IEEE Network, 28(5), 68–73.

    Article  Google Scholar 

  15. Chen, X. (2015). Decentralized computation offloading game for mobile cloud computing. IEEE Transactions on Parallel and Distributed Systems, 26(4), 974–983.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  17. Zhang, K., Mao, Y., Leng, S., Zhao, Q., et al. (2016). Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access, 4, 5896–5907.

    Article  Google Scholar 

  18. Wang, C., Yu, R. F., Liang, C., Chen, Q., & Tang, L. (2017). Joint computation offloading and interference management in wireless cellular networks with mobile edge computing. IEEE Transactions on Vehicular Technology, 66(8), 7432–7445.

    Article  Google Scholar 

  19. Wang, C., Liang, C., Yu, R. F., Chen, Q., & Tang, L. (2017). Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Transactions on Wireless Communications, 16(8), 4924–4938.

    Article  Google Scholar 

  20. Barbarossa, S., Sardellitti, S., & Di Lorenzo, P. (2014). Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Processing Magazine, 31(6), 45–55.

    Article  Google Scholar 

  21. Lin, X., Andrews, J. G., & Ghosh, A. (2013). Modeling, analysis and design for carrier aggregation in heterogeneous cellular networks. IEEE Transactions on Communications, 61(9), 4002–4015.

    Article  Google Scholar 

  22. Kim, D., & Choi, S. (2012). Load balancing in open access femtocell based two-tier cellular networks. In Proceedings of IEEE global communications conference (Globecom), pp. 5123–5129.

  23. Li, Q. C., Hu, R. Q., Xu, Y., & Qian, Y. (2013). Optimal fractional frequency reuse and power control in the heterogeneous wireless networks. IEEE Transactions on Wireless Communications, 52(6), 2658–2668.

    Article  Google Scholar 

  24. Vasudevan, S., Pupala, R. N., & Sivanesan, K. (2013). Dynamic eICIC: A proactive strategy for improving spectral efficiencies of heterogeneous LTE cellular networks by leveraging user mobility and traffic dynamics. IEEE Transactions on Wireless Communications, 12(60), 4956–4969.

    Article  Google Scholar 

  25. Chen, Z., & Wu, D. (2012). Rate-distortion optimized cross-layer rate control in wireless video communication. IEEE Trans. Vircuits & Systems for Video Tech, 22(3), 352–365.

    Article  Google Scholar 

  26. Li, J., Li, X., Yang, B., & Sun, X. (2015). Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security, 10(3), 507–518.

    Article  Google Scholar 

  27. Yang, L., Cao, J., Tang, S., Li, T., & Chan, A. T. S. (2012). A framework for partitioning and execution of data stream applications in mobile cloud computing. In Proceedings of IEEE international conference on cloud computing, pp. 794–802.

  28. Iosifidis, G., Gao, L., Huang, J., & Tassiulas, L. (2013). An iterative double auction for mobile data offloading. In Proceedings of international symposium on modeling and optimization in mobile, ad hoc and wireless networks (WiOpt), pp. 154–161.

  29. Chen, X., Jiao, L., Li, W., & Fu, X. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5), 2795–2808.

    Article  Google Scholar 

  30. Huang, D., Wang, P., & Niyato, D. (2012). A dynamic offloading algorithm for mobile computing. IEEE Transactions on Wireless Communications, 11(6), 1991–1995.

    Article  Google Scholar 

  31. Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., et al. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716.

    Article  Google Scholar 

  32. Chen, L., Yu, R. F., Ji, H., Liu, G., & Leung, V. C. M. (2016). Distributed virtual resource allocation in small-cell networks with full-duplex self-backhauls and virtualization. IEEE Transactions on Vehicular Technology, 65(7), 5410–5423.

    Article  Google Scholar 

  33. Jong, Y. (2012). An efficient global optimization algorithm for nonlinear sum-of-ratios problem. http://www.optimizationonline.org.

  34. Derrick, W. K. N., Ernest, S. L., & Robert, S. (2012). Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas. IEEE Transactions on Wireless Communications, 11(9), 2392–3304.

    Google Scholar 

  35. Boyd, S., & Vandenberghe, L. (2009). A bi-level branch and bound method for economic dispatch with disjoint prohibited zones considering network losses. Cambridge: Cambridge University Press.

    Google Scholar 

  36. Liu, G., Yu, R. F., Ji, H., & Leung, V. C. M. (2014). Distributed resource allocation in full-duplex relaying networks with wireless virtualization. In Proceedings of IEEE global communications conference (Globecom), pp. 4959–4964.

  37. 3rd Generation Partnership Project (3GPP). (2010). Evolved universal terrestrial radio: Further advancements for E-UTRA physical layer aspects. TR: 36. 814 V9.0.0, http://www.qtc.jp/3GPP/Specs/36814-900.pdf.

  38. Ceselli, A., Premoli, M., & Secci, S. (2017). Mobile edge cloud network design optimization. IEEE/ACM Transactions on Networking, 25(3), 1818–1831.

    Article  Google Scholar 

  39. Cau, E., Corici, M., Bellavista, P., Foschini, L., Carella, G., Edmonds, A., & Bohnert, T. M. (2016). Efficient exploitation of mobile edge computing for virtualized 5G in EPC architectures. In 2016 4th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud), pp. 100–109.

  40. Bellavista, P., Foschini, L., Scotece, D. (2017). Converging mobile edge computing, fog computing, and IoT quality requirements. In 2017 IEEE 5th international conference on future internet of things and cloud (FiCloud), pp. 313–320.

  41. Bellavista, P., Chessa, S., Foschini, L., Gioia, L., & Girolami, M. (2018). Human-enabled edge computing: Exploiting the crowd as a dynamic extension of mobile edge computing. IEEE Communications Magazine, 56(1), 145–155.

    Article  Google Scholar 

  42. Zanni, A., Yu, S. Y., Bellavista, P., Langar, R., & Secci, S. (2017). Automated selection of offloadable tasks for mobile computation offloading in edge computing. In 2017 13th International conference on network and service management (CNSM), pp. 1–5.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China under Grant No. 61771070 and 61671088 and the National Science and Technology Major Project under Grant No. 2016ZX03001017.

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Authors and Affiliations

  1. Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecom, Beijing, People’s Republic of China

    Lei Chen, Xi Li & Hong Ji

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

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  1. Lei Chen
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  2. Xi Li
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  3. Hong Ji
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  4. Victor C. M. Leung
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Correspondence to Xi Li.

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Chen, L., Li, X., Ji, H. et al. Computation offloading balance in small cell networks with mobile edge computing. Wireless Netw 25, 4133–4145 (2019). https://doi.org/10.1007/s11276-018-1735-y

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