Deep learning based mobile data offloading in mobile edge computing systems

Abstract Mobile Edge Computing (MEC) has been regarded as a key technology of the future communication systems in the industry due to its capability to satisfy a wide range of requirements of the emerging wireless terminals (virtual reality devices, augmented reality, and Intelligent Vehicles), such as high data rate, low latency, and huge computation. Besides, difficulties in the lack of resources in the licensed band have prompted researches on mobile data offloading. Owing to the cheap and effective characteristics of WiFi AP, it is utilized to offload some devices from small base stations (SBS) in this paper. Furthermore, a multi-Long Short Term Memory (LSTM) based deep-learning model is constructed to predict the real-time traffic of SBS, which may help us perform the offloading process accurately. According to the prediction results, an mobile data offloading strategy based on cross entropy (CE) method has been proposed. The presented results based on actual dataset provide strong proofs of the applicability of the prediction and offloading scheme we proposed.

[1]  Zhi Chen,et al.  Data Allocation for Hybrid Memory With Genetic Algorithm , 2015, IEEE Transactions on Emerging Topics in Computing.

[2]  Yongmin Zhang,et al.  Optimal Cooperative Wireless Communication for Mobile User Data Offloading , 2018, IEEE Access.

[3]  Jack Edmonds,et al.  Matroids and the greedy algorithm , 1971, Math. Program..

[4]  Xing Zhang,et al.  A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.

[5]  Injong Rhee,et al.  Mobile data offloading: how much can WiFi deliver? , 2013, TNET.

[6]  Yuguang Fang,et al.  Motivating Human-Enabled Mobile Participation for Data Offloading , 2018, IEEE Transactions on Mobile Computing.

[7]  Mehdi Bennis,et al.  Toward Interconnected Virtual Reality: Opportunities, Challenges, and Enablers , 2016, IEEE Communications Magazine.

[8]  Meikang Qiu,et al.  Resource allocation robustness in multi-core embedded systems with inaccurate information , 2011, J. Syst. Archit..

[9]  Lih-Yuan Deng,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning , 2006, Technometrics.

[10]  Dario Pompili,et al.  Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges , 2016, IEEE Communications Magazine.

[11]  Shuguang Cui,et al.  Joint offloading and computing optimization in wireless powered mobile-edge computing systems , 2017, 2017 IEEE International Conference on Communications (ICC).

[12]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[13]  Lyes Khoukhi,et al.  Prediction-Based Mobile Data Offloading in Mobile Cloud Computing , 2018, IEEE Transactions on Wireless Communications.

[14]  Yonggang Wen,et al.  Public Cloud Storage-Assisted Mobile Social Video Sharing: A Supermodular Game Approach , 2017, IEEE Journal on Selected Areas in Communications.

[15]  Ellis Horowitz,et al.  Computing Partitions with Applications to the Knapsack Problem , 1974, JACM.

[16]  Victor C. M. Leung,et al.  A Survey on Mobile Data Offloading Technologies , 2018, IEEE Access.

[17]  Huan Zhou,et al.  V2V Data Offloading for Cellular Network Based on the Software Defined Network (SDN) Inside Mobile Edge Computing (MEC) Architecture , 2018, IEEE Access.

[18]  Jianwei Huang,et al.  Optimal Resource Allocations for Mobile Data Offloading via Dual-Connectivity , 2018, IEEE Transactions on Mobile Computing.

[19]  Hamid Aghvami,et al.  A survey on mobile data offloading: technical and business perspectives , 2013, IEEE Wireless Communications.

[20]  Yong Zhao,et al.  Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff , 2018, IEEE Access.