藉由佈置計算單元於邊緣網路，行動邊緣運算(Mobile Edge Computing, MEC)是
一個具有潛力滿足第五世代行動網路中服務(Service)的超低延遲需求的框架。然
而，在此架構中服務的延遲會因為終端使用者的移動性而受到嚴重的影響，其中服
務的延遲當在終端使用者為兩個時又會較單一使用者更受使用者們的移動性而影
響。在本篇論文中，我們假設兩個具有移動性的使用者共同使用一個服務，其中，
一個使用者始終跟隨著另一個使用者。我們稱這兩個使用者為一對(a pair)，並且假設系統中有多對使用者。在一對使用者中，後方的使用者會向前方的使用者及相關的物聯網裝置要求其所需的資訊，前方的使用者及相關的物聯網裝置則會上傳其收集的資料至邊緣伺服器(MEC server)，邊緣伺服器會處理該資料成為資訊後下傳給後方使用者。為了保證滿足服務延遲的需求，根據兩個使用者的移動性做出適當
的服務遷移(Service migration)是必須的。在本篇論文中，我們把服務的遷移規劃為馬可夫決策過程(Markov Decision Process)。我們由馬可夫決策過程的解算得到了具有已知及預測路徑之一對使用者的最佳解。對於多對使用者的情境，我們考慮了邊緣伺服器的計算資源是有限的，所以我們提出了邊緣伺服器負載平衡演算法，其能有效解決當系統有多對使用者時易產生的邊緣伺服器超載問題。我們藉由模擬來評價演算法的成果，結果顯示：一、在一對使用者的系統中，我們提出的方法相較於傳統的方法節省了最多60.19%的總系統花費。二、在多對使用者的系統中，我們利用城市車輛移動性模擬器，SUMO，模擬了城市─巴爾的摩(Baltimore)的車量移動性，並以公開的巴爾的摩犯罪監視器座標資料作為物聯網裝置的假設依據。在模擬的結果中，我們提出的邊緣伺服器負載平衡演算法相較於基值最多能減少41.06%的花費去平衡超載的邊緣伺服器。

One of the major challenges in Mobile Edge Computing (MEC) for 5G networks is maintaining the quality of service considering the mobility of end users. The challenge becomes more severe when it comes to double mobile applications in which both information sources and decision makers are in mobility. A double mobile application involves a pair of mobile users, one of which always follows the others physically and requests information from it with latency requirements, such as drone-assisted police car chasing. In order to guarantee the latency requirement, service migration according to the mobility of both users is necessary. In this paper, we formulate the service migration decision as a Markov Decision Process (MDP). The optimal solution that minimizes the service cost while satisfying the QoS requirements of the target service given the predicted route and mobility pattern is derived in single pair scenario. For the multi-pair scenario, we consider the maximum loading of MEC servers and proposed load balancing algorithm to derive the feasible solutions with desired performance in an acceptable computation complexity. The performance is evaluated through simulations. The results show that we can achieve 60.19% reduction of overall system cost over traditional methods in single-pair system. For multi-pair scenario, simulations based on Baltimore street map is performed. Our simulation results show that we can achieve 41.06% reduction of cost of balancing the overloaded MEC server over the baseline method, with much higher chance to find the feasible solutions.

[1] Simulation of Urban MObility: SUMO. [Online]. Available: http://sumo.sourceforge.net/.
[2] DATA.GOV. [Online]. Available: https://catalog.data.gov/dataset/
crime-camera.
[3] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, Feb. 2014.
[4] P. Charith, Z. Arkady, C. Peter, and G. Dimitrios, “Sensing as a service model for smart cities supported by Internet of Things,” Transactions on Emerging Telecommunications Technologies, vol. 25, no. 1, pp. 81–93,
[5] F. Mohammed, A. Idries, N. Mohamed, J. Al-Jaroodi, and I. Jawhar, “UAVs for smart cities: Opportunities and challenges,” in Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS), May 2014, pp. 267–273.
[6] P. Ramaswamy, “IoT smart parking system for reducing green house gas emission,” in Proceedings of International Conference on Recent Trends in Information Technology (ICRTIT), Apr. 2016, pp. 1–6.
[7] G. Cacciatore, C. Fiandrino, D. Kliazovich, F. Granelli, and P. Bouvry, “Cost analysis of smart lighting solutions for smart cities,” in Proceedings of IEEE International
Conference on Communications (ICC), May 2017, pp. 1–6.
[8] S. Yamaguchi and Y. Tamura, “Modeling of stalkers’ behavior and development of simulated experience game for education,” in Proceedings of IEEE 6th Global Conference on Consumer Electronics (GCCE), Oct. 2017, pp. 1–4.
[9] Euro 2016 : la préfecture de police de Paris s’équipe de drones. [Online]. Available: http://www.lepoint.fr/.
[10] First UK police drone unit launched in Devon, Cornwall and Dorset. [Online]. Available: http://www.bbc.com/news.
[11] R. Van Krevelen and R. Poelman, “A Survey of Augmented Reality Technologies, Applications and Limitations,” International Journal of Virtual Reality (ISSN 1081-1451), vol. 9, p. 1, Jun. 2010.
[12] D. Chatzopoulos, C. Bermejo, Z. Huang, and P. Hui, “Mobile Augmented Reality Survey: From Where We Are to Where We Go,” IEEE Access, vol. 5, pp. 6917–6950, 2017.
[13] A. Al-Shuwaili and O. Simeone, “Energy-Efficient Resource Allocation for Mobile Edge Computing-Based Augmented Reality Applications,” IEEE Wireless Communications Letters, vol. 6, no. 3, pp. 398–401, Jun. 2017.
[14] T. Braud, F. H. Bijarbooneh, D. Chatzopoulos, and P. Hui, “Future Networking Challenges: The Case of Mobile Augmented Reality,” in Proceedings of IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Jun. 2017, pp. 1796–1807.
[15] M. Wittie, “Network performance requirements of Augmented Reality Systems,” in Proceedings of Workshop on Active Internet Measurements, 2017.
[16] W. Bao, D. Yuan, Z. Yang, S. Wang, W. Li, B. B. Zhou, and A. Y. Zomaya, “Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment,” IEEE Communications Magazine, vol. 55, no. 11, pp. 72–78, Nov. 2017.
[17] Open Street Map: OSM. [Online]. Available: https://www.openstreetmap.org/.
[18] S. Secci, P. Raad, and P. Gallard, “Linking Virtual Machine Mobility to User Mobility,” IEEE Transactions on Network and Service Management, vol. 13, no. 4, pp. 927–940, Dec. 2016.
[19] L. F. Bittencourt, M. M. Lopes, I. Petri, and O. F. Rana, “Towards virtual machine migration in fog computing,” in Proceedings of International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Nov. 2015, pp. 1–8.
[20] I. Farris, T. Taleb, A. Iera, and H. Flinck, “Lightweight service replication for ultrashort latency applications in mobile edge networks,” in Proceedings of IEEE International Conference on Communications (ICC), May 2017, pp. 1–6.
[21] P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Communications Surveys Tutorials, vol. 19, no. 3, pp. 1628–1656, thirdquarter 2017.
[22] T. G. Rodrigues, K. Suto, H. Nishiyama, and N. Kato, “Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control,” IEEE Transactions on Computers, vol. 66, no. 5, pp. 810–819, May 2017.
[23] A. Ksentini, T. Taleb, and M. Chen, “A Markov Decision Process-based Service Migration Procedure for Follow Me Cloud,” in Proceedings of IEEE International Conference on Communications (ICC), Jun. 2014, pp. 1350–1354.
[24] S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, and K. K. Leung, “Mobility-Induced Service Migration in Mobile Micro-clouds,” in Proceedings of IEEE Military Communications Conference, Oct. 2014, pp. 835–840.
[25] S. Wang, R. Urgaonkar, K. Chan, T. He, M. Zafer, and K. K. Leung, “Dynamic service placement for mobile micro-clouds with predicted future costs,” in Proceedings of IEEE International Conference on Communications (ICC), Jun. 2015, pp. 5504–5510.
[26] Y. J. Yu, T. C. Chiu, A. C. Pang, M. F. Chen, and J. Liu, “Virtual Machine Placement for Backhaul Traffic Minimization in Fog Radio Access Networks,” in Proceedings of IEEE International Conference on Communications (ICC), May 2017, pp. 1–7.
[27] S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic Service Migration in Mobile Edge-Clouds,” in Proceedings of IFIP Networking Conference (IFIP Networking), May 2015, pp. 1–9.
[28] R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, and K. K. Leung, “Dynamic Service Migration and Workload Scheduling in Edge-clouds,” Perform. Eval., vol. 91, no. C, pp. 205–228, Sep. 2015.
[29] J. Oueis, E. C. Strinati, and S. Barbarossa, “The fog balancing: Load distribution for small cell cloud computing,” in Proceedings of IEEE 81st Vehicular Technology Conference (VTC Spring), May 2015, pp. 1–6.
[30] L. Tong, Y. Li, and W. Gao, “A hierarchical edge cloud architecture for mobile computing,” in Proceedings of IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, Apr. 2016, pp. 1–9.
[31] H. Tan, Z. Han, X. Y. Li, and F. C. M. Lau, “Online job dispatching and scheduling in edge-clouds,” in Proceedings of IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, May 2017, pp. 1–9.
[32] A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live Service Migration in Mobile Edge Clouds,” IEEE Wireless Communications, vol. 25, no. 1, pp. 140–147, Feb. 2018.
[33] Z. Meng, Z. Chen, and Z. Guan, “Seamless service migration for human-centered computing in future Internet architecture,” in Proceedings of Proceed2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Aug. 2017, pp. 1–7.
[34] L. Ma, S. Yi, and Q. Li, “Efficient Service Handoff Across Edge Servers via Docker Container Migration,” in Proceedings of the Second ACM/IEEE Symposium on Edge Computing, ser. SEC ’17, 2017, 11:1–11:13.