Code-Partitioning Offloading Schemes in Mobile Edge Computing for Augmented Reality
Open Access
- 7 January 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 11222-11236
- https://doi.org/10.1109/access.2019.2891113
Abstract
Augmented reality (AR) is one of the emerging use cases relying on ultra-reliable and low latency communications (uRLLC). AR service is composed of multiple dependent computational-intensive components. Due to the limited capability of user equipment (UE), it is difficult to meet the stringent latency and reliability requirements of AR service merely by local processing. To solve the problem, it is viable to offload parts of the AR task to the network edge, i.e. mobile edge computing (MEC), which is expected to extend the computing capability of the UE. However, MEC also incurs extra communication latency and errors on wireless channel, therefore it is challenging to make an optimum offload decision. So far, little of state-of-the-art work has considered both latency and reliability of the MEC-enabled AR service. In this paper, we study the scenario multiple edge nodes (ENs) cooperate to complete the AR task. The dependency of task components is modeled by a directed acyclic graph through code partitioning. We aim to minimize the service failure probability (SFP) of the MEC-enabled AR service considering reliability and latency.We design an integer particle swarm optimization (IPSO) based algorithm. Although the solution of IPSO-based algorithm approaches the optimum of the problem, it is infeasible to use IPSO for real-time AR services in practice due to the relatively high computational complexity. Hence, we propose a heuristic algorithm, which achieves performance close to that of IPSO-based algorithm with much lower complexity. Compared with state-of-the-art work, heuristic algorithm can significantly improve the probability to fulfill the targeted SFP in various network conditions. Due to the generic characteristics, the proposed heuristic algorithm is applicable for AR services, as well as for many other use cases in uRLLC.Keywords
This publication has 29 references indexed in Scilit:
- Efficient Multi-User Computation Offloading for Mobile-Edge Cloud ComputingIEEE/ACM Transactions on Networking, 2015
- A Hierarchical Correlation Model for Evaluating Reliability, Performance, and Power Consumption of a Cloud ServiceIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015
- Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge ComputingIEEE Transactions on Signal and Information Processing over Networks, 2015
- Cloud-assisted collaborative execution for mobile applications with general task topologyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Mobile code offloading: from concept to practice and beyondIEEE Communications Magazine, 2015
- A Survey of Augmented RealityFoundations and Trends® in Human–Computer Interaction, 2015
- Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application OffloadingIEEE Transactions on Vehicular Technology, 2014
- A lightweight and accurate link abstraction model for the simulation of LTE networks in ns-3Published by Association for Computing Machinery (ACM) ,2012
- A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud ComputingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- A standard task graph set for fair evaluation of multiprocessor scheduling algorithmsJournal of Scheduling, 2002