A mobile edge computing-based applications execution framework for Internet of Vehicles
Libing WU, Rui ZHANG, Qingan LI, Chao MA, Xiaochuan SHI
A mobile edge computing-based applications execution framework for Internet of Vehicles
Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput.
mobile edge computing / application partition / directed acyclic graph / offloading / Internet of Vehicles
[1] |
Ma X , Zhao Y , Zhang L , Wang H , Peng L . When mobile terminals meet the cloud: computation offloading as the bridge. IEEE Network, 2013, 27( 5): 28– 33
|
[2] |
Chen B , Wu L , Wang H , Zhou L , He D . A blockchain-based searchable public-key encryption with forward and backward privacy for cloud-assisted vehicular social networks. IEEE Transactions on Vehicular Technology, 2020, 69( 6): 5813– 5825
|
[3] |
Elazhary H , Aloraini S , Aljuraid R . Context-aware mobile application task offloading to the cloud. International Journal of Advanced Computer Science & Applications, 2017, 8( 5): 381– 390
|
[4] |
Marotta M A , Faganello L R , Schimuneck M A K , Granville L Z . Managing mobile cloud computing considering objective and subjective perspectives. Computer Networks, 2015, 93( P3): 531– 542
|
[5] |
Guo S , Liu J , Yang Y , Xiao B , Li Z . Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Transactions on Mobile Computing, 2019, 18( 2): 319– 333
|
[6] |
Yi S, Li C, Li Q. A survey of fog computing: concepts, applications, and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data (Mobidata '15). 2015, 1– 6
|
[7] |
Zeng D , Gu L , Pan S , Cai J , Guo S . Resource management at the network edge: A deep reinforcement learning approach. IEEE Network, 2019, 33( 3): 26– 33
|
[8] |
Jararweh Y, Doulat A, Darabseh A, Alsmirat M, Benkhelifa E. Sdmec: Software defined system for mobile edge computing. In: Proceedings of IEEE International Conference on Cloud Engineering Workshop. 2016, 88− 93
|
[9] |
Hu Y C , Patel M , Sabella D , Sprecher N , Young V . Mobile edge computing-a key technology towards 5G. ETSI white paper, 2015, 11( 11): 1– 16
|
[10] |
Gu L , Cai J , Zeng D , Zhang Y , Jin H , Dai W . Energy efficient task allocation and energy scheduling in green energy powered edge computing. Future Generation Computer Systems, 2019, 95( JUN.): 89– 99
|
[11] |
Chen B , Wu L , Li L , Choo K K R , He D . A parallel and forward private searchable public-key encryption for cloud-based data sharing. IEEE Access, 2020, 8( 99): 28009– 28020
|
[12] |
Satria D , Park D , Jo M . Recovery for overloaded mobile edge computing. Future Generation Computer Systems, 2017, 70
|
[13] |
Patel M , Naughton B , Chan C , Sprecher N , Abeta S , Neal A . Mobile-edge computing introductory technical white paper. White paper, Mobile-Edge Computing (MEC) Industry Initiative, 2014, 29
|
[14] |
Wang J , Wu L , Choo K K R , He D . Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure. IEEE Transactions on Industrial Informatics, 2020, 16( 3): 1984– 1992
|
[15] |
Chaudhary R , Kumar N , Zeadally S . Network service chaining in fog and cloud computing for the 5G environment: data management and security challenges. IEEE Communications Magazine, 2017, 55( 11): 114– 122
|
[16] |
Tong L, Li Y, Gao W. A hierarchical edge cloud architecture for mobile computing. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications. 2016, 1– 9
|
[17] |
Sun Y , Zhou S , Xu J . Emm: Energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE Journal on Selected Areas in Communications, 2017, 35( 11): 2637– 2646
|
[18] |
Xiao Y, Krunz M. Qoe and power efficiency tradeoff for fog computing networks with fog node cooperation. In: Proceedings of IEEE Conference on Computer Communications. 2017, 1– 9
|
[19] |
Khan M A . A survey of computation offloading strategies for performance improvement of applications running on mobile devices. Journal of Network and Computer Applications, 2015, 56
|
[20] |
Hassan M A, Xiao M, Wei Q, Chen S. Help your mobile applications with fog computing. In: Proceedings of IEEE International Conference on Sensing, Communication and Networking Workshops. 2015, 1– 6
|
[21] |
Takahashi N, Tanaka H, Kawamura R. Analysis of process assignment in multi-tier mobile cloud computing and application to edge accelerated Web browsing. In: Proceedings of IEEE International Conference on Mobile Cloud Computing, Services and Engineering. 2015, 233− 235
|
[22] |
Chen X , Jiao L , Li W , Fu X . Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 2016, 24( 5): 2795– 2808
|
[23] |
Zhang K, Mao Y, Leng S, Vinel A, Zhang Y. Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. In: Proceedings of International Workshop on Resilient Networks Design & Modeling. 2016, 288− 294
|
[24] |
Beck M T, Feld S, Fichtner A, Linnhoff-Popien C, Schimper T. Me-volte: Network functions for energy-efficient video transcoding at the mobile edge. In: Proceedings of the 18th International Conference on Intelligence in Next Generation Networks. 2015, 38− 44
|
[25] |
Jalali F , Hinton K , Ayre R , Alpcan T , Tucker R S . Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 2016, 34( 5): 1728– 1739
|
[26] |
Dai Y , Xu D , Maharjan S , Zhang Y . Joint load balancing and offloading in vehicular edge computing and networks. IEEE Internet of Things Journal, 2019, 6( 3): 4377– 4387
|
[27] |
Raza S , Liu W , Ahmed M , Anwar M R , Wang S . An efficient task offloading scheme in vehicular edge computing. Journal of Cloud Computing Advances Systems and Applications, 2020, 9( 1): 1– 14
|
[28] |
Guo H , Zhang J , Liu J . Fiwi-enhanced vehicular edge computing networks: Collaborative task offloading. IEEE Vehicular Technology Magazine, 2019, 14( 1): 45– 53
|
[29] |
Huang X , Xu K , Lai C , Chen Q , Zhang J . Energy-efficient offloading decision-making for mobile edge computing in vehicular networks. EURASIP Journal on Wireless Communications and Networking, 2020, 2020( 1): 1– 16
|
[30] |
Du J , Yu F R , Chu X , Feng J , Lu G . Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Transactions on Vehicular Technology, 2019, 68( 2): 1079– 1092
|
[31] |
Zhang K , Mao Y , Leng S , He Y , Zhang Y . Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading. IEEE Vehicular Technology Magazine, 2017, 12( 2): 36– 44
|
[32] |
Chen X , Shi Q , Yang L , Xu J . Thriftyedge: Resource-efficient edge computing for intelligent iot applications. IEEE Network, 2018, 32( 1): 61– 65
|
[33] |
Li H , Ota K , Dong M . Learning iot in edge: Deep learning for the internet of things with edge computing. IEEE Network, 2018, 32( 1): 96– 101
|
[34] |
Tobita T , Kasahara H . A standard task graph set for fair evaluation of multiprocessor scheduling algorithms. Journal of Scheduling, 2010, 5( 5): 379– 394
|
[35] |
Yang L , Cao J , Yuan Y , Li T , Han A , Chan A . A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Performance Evaluation Review, 2013, 40( 4): 23– 32
|
[36] |
Liu Y , Yu H , Xie S , Zhang Y . Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Transactions on Vehicular Technology, 2019, 68( 11): 11158– 11168
|
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