On the Optimality of Task Offloading in Mobile Edge Computing Environments

Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose goal is to enhance the mobile devices' capabilities to face the requirements of new applications. Computation offloading faces the challenges of where and when to offload data to perform computing (analytics) tasks. In this paper, we tackle this problem by adopting the principles of Optimal Stopping Theory contributing with two time-optimized sequential decision making models. A performance evaluation is provided using real world data sets compared with baseline deterministic and stochastic models. The results show that our approach optimizes such decision in single user and competitive users scenarios.

[1]  Kelvin Lopes Dias,et al.  A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment , 2019, Future Gener. Comput. Syst..

[2]  Pan Hui,et al.  Future Networking Challenges: The Case of Mobile Augmented Reality , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[3]  Hamid Harroud,et al.  Mobile cloud computing for computation offloading: Issues and challenges , 2018 .

[4]  Ashwin Ashok,et al.  Vehicular Cloud Computing through Dynamic Computation Offloading , 2017, Comput. Commun..

[5]  Haopeng Chen,et al.  DMPO: Dynamic mobility-aware partial offloading in mobile edge computing , 2018, Future Gener. Comput. Syst..

[6]  Giancarlo Fortino,et al.  Autonomic computation offloading in mobile edge for IoT applications , 2019, Future Gener. Comput. Syst..

[7]  Kostas Kolomvatsos,et al.  Predictive intelligence to the edge through approximate collaborative context reasoning , 2017, Applied Intelligence.

[8]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[9]  Ke Zhang,et al.  Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading , 2017, IEEE Veh. Technol. Mag..

[10]  Zdenek Becvar,et al.  Path selection enabling user mobility and efficient distribution of data for computation at the edge of mobile network , 2016, Comput. Networks.

[11]  Erik Elmroth,et al.  Location-aware load prediction in Edge Data Centers , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[12]  Lorenzo Bracciale,et al.  CRAWDAD dataset roma/taxi (v.2014-07-17) , 2014 .

[13]  Prem Prakash Jayaraman,et al.  Opportunistic Computation Offloading in Mobile Edge Cloud Computing Environments , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[14]  Jakub Dolezal,et al.  Performance evaluation of computation offloading from mobile device to the edge of mobile network , 2016, 2016 IEEE Conference on Standards for Communications and Networking (CSCN).

[15]  Qi Zhang,et al.  Cooperative Computation Offloading for UAVs: A Joint Radio and Computing Resource Allocation Approach , 2018, 2018 IEEE International Conference on Edge Computing (EDGE).

[16]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[17]  José Alberto Hernández,et al.  Design and Analysis of 5G Scenarios with simmer: An R Package for Fast DES Prototyping , 2018, IEEE Communications Magazine.

[18]  Dimitrios P. Pezaros,et al.  Time-Optimized Task Offloading Decision Making in Mobile Edge Computing , 2019, 2019 Wireless Days (WD).

[19]  Sangheon Pack,et al.  Spatial and Temporal Computation Offloading Decision Algorithm in Edge Cloud-Enabled Heterogeneous Networks , 2018, IEEE Access.

[20]  Tinghuai Ma,et al.  Real time services for future cloud computing enabled vehicle networks , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[21]  A. Cox.  Optimal Stopping and Applications , 2009 .

[22]  Christos Anagnostopoulos,et al.  Predictive intelligence to the edge: impact on edge analytics , 2018, Evol. Syst..

[23]  Arturo Azcorra,et al.  simmer: Discrete-Event Simulation for R , 2017, Journal of Statistical Software.

[24]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal Stopping and Free-Boundary Problems , 2006 .

[25]  Christos Anagnostopoulos,et al.  Edge-Centric Efficient Regression Analytics , 2018, 2018 IEEE International Conference on Edge Computing (EDGE).