Abstract
Mobile edge computing (MEC) is becoming a promising paradigm of providing computing servers, like cloud computing, to Edge node. Compared to cloud servers, MECs are deployed closer to mobile devices (MDs) and can provide high quality-of-service (QoS; including high bandwidth, low latency, etc) for MDs with computation-intensive and delay-sensitive tasks. Faced with many MDs with high QoS requirements, MEC with limited computation capacity should consider how to allocate the computing resources to MDs to maximize the number of served MDs. Besides, for each MD, he/she wants to minimize the energy consumption within an acceptance delay range. To solve these issues, we propose a Game-based Computation Offloading (GCO) algorithm including a task offloading profile of MEC and the transmission power controlling of each MD. Specifically, we propose a Greedy-Pruning algorithm to determine the MDs that can offload the tasks to MEC. Meanwhile, each MD competes the computing resources by using his/her transmission power-controlling strategy. We illustrate the problem of task offloading for multi-MD as a non-cooperative game model, in which the information of each player (MDs) is incomplete for others and each player wishes to maximize his/her own benefit. We prove the existence of the Nash equilibrium solution of our proposed game model. Then, it is proved that the transmission power solution sequence obtained from GCO algorithm converges to the Nash equilibrium solution. Extensive simulated experiments are shown and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.
- N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie. 2018. Mobile edge computing: A survey. IEEE Internet of Things Journal 5, 1 (2018), 450--465.Google Scholar
Cross Ref
- M. Chen and Y. Hao. 2018. Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications 36, 3 (2018), 587--597.Google Scholar
- W. Chen, D. Wang, and K. Li. 2019. Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Transactions on Services Computing 12, 5 (2019), 726--738.Google Scholar
Cross Ref
- X. Chen, L. Jiao, W. Li, and X. Fu. 2016. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking 24, 5 (2016), 2795--2808.Google Scholar
Digital Library
- B. Du, R. Huang, Z. Xie, J. Ma, and W. Lv. 2018. KID model-driven things-edge-cloud computing paradigm for traffic data as a service. IEEE Network 32, 1 (2018), 34--41.Google Scholar
Cross Ref
- Q. Fan and N. Ansari. 2018. Application aware workload allocation for edge computing-based IoT. IEEE Internet of Things Journal 5, 3 (2018), 2146--2153.Google Scholar
Cross Ref
- Daniel Grosu and Anthony T. Chronopoulos. 2005. Noncooperative load balancing in distributed systems. Journal of Parallel and Distributed Computing 65, 9 (2005), 1022--1034.Google Scholar
Digital Library
- Hongzhi Guo and Jiajia Liu. 2018. Collaborative computation offloading for multiaccess edge computing over fiberĺcwireless networks. IEEE Transactions on Vehicular Technology 67, 5 (May 2018).Google Scholar
Cross Ref
- X. Hu, K. Wong, and K. Yang. 2018. Wireless powered cooperation-assisted mobile edge computing. IEEE Transactions on Wireless Communications 17, 4 (2018), 2375--2388.Google Scholar
Cross Ref
- S. M. R. Islam, N. Avazov, O. A. Dobre, and K. Kwak. 2017. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges. IEEE Communications Surveys and Tutorials 19, 2 (2017), 721--742.Google Scholar
Digital Library
- A. Kiani and N. Ansari. 2018. Edge computing aware NOMA for 5G networks. IEEE Internet of Things Journal 5, 2 (2018), 1299--1306.Google Scholar
Cross Ref
- K. Li. 2018. A game theoretic approach to computation offloading strategy optimization for non-cooperative users in mobile edge computing. IEEE Transactions on Sustainable Computing (Sept. 2018). DOI:https://doi.org/10.1109/TSUSC.2018.2868655Google Scholar
Cross Ref
- K. Li. 2019. Computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. IEEE Transactions on Sustainable Computing (March 2019). DOI:https://doi.org/10.1109/TSUSC.2019.2904680Google Scholar
Cross Ref
- K. Li, C. Liu, K. Li, and A. Y. Zomaya. 2016. A framework of price bidding configurations for resource usage in cloud computing. IEEE Transactions on Parallel and Distributed Systems 27, 8 (2016), 2168--2181.Google Scholar
Digital Library
- X. Li, D. Li, J. Wan, C. Liu, and M. Imran. 2018. Adaptive transmission optimization in SDN-based industrial Internet of Things with edge computing. IEEE Internet of Things Journal 5, 3 (2018), 1351--1360.Google Scholar
Cross Ref
- C. Liu, K. Li, J. Liang, and K. Li. 2019. COOPER-SCHED: A cooperative scheduling framework for mobile edge computing with expected deadline guarantee. IEEE Transactions on Parallel and Distributed Systems (2019), 1--1.Google Scholar
- C. Liu, K. Li, C. Xu, and K. Li. 2016. Strategy configurations of multiple users competition for cloud service reservation. IEEE Transactions on Parallel and Distributed Systems 27, 2 (2016), 508--520.Google Scholar
Digital Library
- J. Liu, Y. Mao, J. Zhang, and K. B. Letaief. 2016. Delay-optimal computation task scheduling for mobile-edge computing systems. In 2016 IEEE International Symposium on Information Theory (ISIT). 1451--1455.Google Scholar
- L. Ma, X. Liu, Q. Pei, and Y. Xiang. 2019. Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing. IEEE Transactions on Services Computing 12, 5 (2019), 786--799.Google Scholar
Cross Ref
- Y. Mao, J. Zhang, and K. B. Letaief. 2017. Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In 2017 IEEE Wireless Communications and Networking Conference (WCNC). 1--6.Google Scholar
- M. R. Musku, A. T. Chronopoulos, D. C. Popescu, and A. Stefanescu. 2010. A game-theoretic approach to joint rate and power control for uplink CDMA communications. IEEE Transactions on Communications 58, 3 (2010), 923--932.Google Scholar
Digital Library
- John F. Nash. 1950. Equilibrium points in n-person games. Proceedings of the National Academy of Sciences 36, 1 (1950), 48--49. DOI:https://doi.org/10.1073/pnas.36.1.48 arXiv:https://www.pnas.org/content/36/1/48.full.pdfGoogle Scholar
Cross Ref
- J. L. D. Neto, S. Yu, D. F. Macedo, J. M. S. Nogueira, R. Langar, and S. Secci. 2018. ULOOF: A user level online offloading framework for mobile edge computing. IEEE Transactions on Mobile Computing 17, 11 (2018), 2660--2674.Google Scholar
Digital Library
- Z. Ning, X. Wang, and J. Huang. 2019. Mobile edge computing-enabled 5g vehicular networks: Toward the integration of communication and computing. IEEE Vehicular Technology Magazine 14, 1 (2019), 54--61.Google Scholar
Cross Ref
- Satish Penmatsa and Anthony T. Chronopoulos. 2011. Game-theoretic static load balancing for distributed systems. Journal of Parallel and Distributed Computing 71, 4 (2011), 537--555.Google Scholar
Digital Library
- S. Ranadheera, S. Maghsudi, and E. Hossain. 2018. Computation offloading and activation of mobile edge computing servers: A minority game. IEEE Wireless Communications Letters 7, 5 (2018), 688--691.Google Scholar
Cross Ref
- T. G. Rodrigues, K. Suto, H. Nishiyama, N. Kato, and K. Temma. 2018. Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Transactions on Computers 67, 9 (2018), 1287--1300.Google Scholar
Cross Ref
- G. Scutari, D. P. Palomar, F. Facchinei, and J. Pang. 2010. Convex optimization, game theory, and variational inequality theory. IEEE Signal Processing Magazine 27, 3 (2010), 35--49.Google Scholar
Cross Ref
- X. Sun and N. Ansari. 2017. Latency aware workload offloading in the cloudlet network. IEEE Communications Letters 21, 7 (2017), 1481--1484.Google Scholar
Cross Ref
- X. Tao, K. Ota, M. Dong, H. Qi, and K. Li. 2017. Performance guaranteed computation offloading for mobile-edge cloud computing. IEEE Wireless Communications Letters 6, 6 (2017), 774--777.Google Scholar
Cross Ref
- E. E. Tsiropoulou, G. K. Katsinis, and S. Papavassiliou. 2012. Distributed uplink power control in multiservice wireless networks via a game theoretic approach with convex pricing. IEEE Transactions on Parallel and Distributed Systems 23, 1 (2012), 61--68.Google Scholar
Digital Library
- E. E. Tsiropoulou, P. Vamvakas, and S. Papavassiliou. 2012. Energy efficient uplink joint resource allocation non-cooperative game with pricing. In 2012 IEEE Wireless Communications and Networking Conference (WCNC). 2352--2356.Google Scholar
- F. Wang, J. Xu, X. Wang, and S. Cui. 2018. Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Transactions on Wireless Communications 17, 3 (2018), 1784--1797.Google Scholar
Cross Ref
- K. Wang, H. Yin, W. Quan, and G. Min. 2018. Enabling collaborative edge computing for software defined vehicular networks. IEEE Network 32, 5 (2018), 112--117.Google Scholar
Cross Ref
- L. Yang, H. Zhang, M. Li, J. Guo, and H. Ji. 2018. Mobile edge computing empowered energy efficient task offloading in 5G. IEEE Transactions on Vehicular Technology 67, 7 (2018), 6398--6409.Google Scholar
Cross Ref
- J. Zhang, X. Hu, Z. Ning, E. C. Ngai, L. Zhou, J. Wei, J. Cheng, and B. Hu. 2018. Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks. IEEE Internet of Things Journal 5, 4 (2018), 2633--2645.Google Scholar
Cross Ref
- K. Zhang, S. Leng, Y. He, S. Maharjan, and Y. Zhang. 2018. Cooperative content caching in 5G networks with mobile edge computing. IEEE Wireless Communications 25, 3 (2018), 80--87.Google Scholar
Cross Ref
- Z. Zhao, G. Min, W. Gao, Y. Wu, H. Duan, and Q. Ni. 2018. Deploying edge computing nodes for large-scale IoT: A diversity aware approach. IEEE Internet of Things Journal 5, 5 (2018), 3606--3614.Google Scholar
Cross Ref
Index Terms
Game-Based Task Offloading of Multiple Mobile Devices with QoS in Mobile Edge Computing Systems of Limited Computation Capacity
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