隨著資訊與通訊技術(Information and Communication Technology, ICT)及IoT裝置快速的成長，4G網路難以滿足使用者對於連線頻寬、連線數量及連線延遲的需求，因此5G網路被提出，以滿足多種不同類型服務的需求。5G搭配多接取邊緣運算(Multi-access Edge Computing, MEC)架構可以有效減少資料往返雲端時間，大幅提升遊戲即服務(Gaming as a Service, GaaS)的服務品質，改善延遲影響體驗的問題。然而邊緣伺服器並不像雲端伺服器具有巨量的計算資源，因此必須要有分流策略來動態配置網路服務的執行環境，藉此改善整體網路服務的運作效益。
本研究提出基於5G邊緣運算之低延遲應用分流策略，以資料蒐集模組(Data Collection)取得UE、MEC及Cloud的資訊，再透過服務模擬模組(Services Simulation Model)預先模擬服務在各個環境執行的模擬服務傳輸延遲及處理延遲，最後，服務分流模組(Offload Model)選擇最佳的分流方案，最小化總體延遲。本研究針對不同的服務類型及不同的UE數量進行分析。研究結果顯示，透過本研究的分流策略，能夠避免服務發生排隊與等候造成的壅塞現象。與過去的研究相比，當有 20 位使用者，於服務類型為 720P@60fps 影音串流的情境中可以有效改善約 6.27%端到端延遲及減少服務掉包率約 26.67%；於服務類型為 1080P@60fps 影音串流的情境中可以有效改善約 1.9%端到端延遲及減少服務掉包率約 22.25%。當使用者提高到 50 位，服務類型為 1080P@60fps 影音串流的情境中可以有效改善約 2.41%端到端延遲及減少服務掉包率約 35.32%。藉此改善服務使得系統可以提供更好的網路服務品質。

With the rapid growth of information and communication technology (ICT) and Internet of Things devices, 4G networks cannot meet the needs of users for bandwidth, many connections, and latency. Therefore, 5G networks have been proposed to meet various needs. Type of service requirement. 5G adopts the multi-access edge computing (MEC) architecture, which can effectively reduce the time for data to enter and exit the cloud, greatly improve the service quality of Gaming as a Service (GaaS) and improve the problem of latency affecting the experience. However, the edge server does not have the huge computing resources of the cloud server, so it is necessary to have an offloading policy to dynamically configure the execution environment of the network service, improving the overall operating efficiency of the network service.
This study proposed a low-latency computation offloading based on 5G edge computing system. It uses Data Collection to obtain UE, MEC, and Cloud information and then simulates service transmission and execution latency in various environments in advance through the Services Simulation Model. Finally, Offload Model chooses the best offloading policy to minimize the overall latency. This study analyzes different types of services and different numbers of UEs. The research results show that through the low latency services offloading policy based on greedy (LSOPG) algorithm, service congestion caused by queuing and waiting can be avoided. Compared with the previous study, when there are 20 users, can improve about 6.27% E2E latency and 26.67% packet loss rate when the service type is 720P@60fps video streaming; when the service type is 1080P@60fps video streaming, it can improve about 1.9% E2E latency and 22.25% packet loss rate. When the number of users is increased to 50 and the service type is 1080P@60fps video streaming, it can improve about 2.41% E2E latency and 35.32% packet loss rate. This experimental result confirms that the LSOPG algorithm can provide well service quality in edge network.

[1] T.A. Beehr, J.E. Newman, "Job stress, employee health, and organizational effective-ness: A facet analysis, model and literature review," Personnel Psychology, Vol.31, No.4, pp.665-699, 1978.
[2] American Psychological Association, "Psychology Topics:Stress," Retrieved from https://www.apa.org/topics/stress (last visited on 2021/04/28)
[3] Cigna Life Insurance, "2019 Cigna 360 Well-Being Survey," Retrieved from https://www.cignaglobal.com/ (last visited on 2021/04/28)
[4] Cisco, "Riot Games is Powering the Next Decade of Esports with Cisco Server and Network Technologies," Retrieved from https://www.cisco.com/c/en/us/solutions/collateral/data-center/riot-games-case-study.html?oid=csycsm024728 (last visited on 2021/04/28)
[5] S. Pontiroli, "Cheat or death? The secret world of malware-like cheats in video games," Retrieved from https://www.kaspersky.com/blog/malware-like-cheats/29231/ (last visited on 2021/04/28)
[6] D. An, "Find out how you stack up to new industry benchmarks for mobile page speed," Retrieved from https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/mobile-page-speed-new-industry-benchmarks/ (last visited on 2021/04/28)
[7] M.S. Elbamby, C. Perfecto, M. Bennis and K. Doppler, "Toward low-latency and ultra-reliable virtual reality," IEEE Network, Vol.32, No.2, pp.78-84, 2018.
[8] 3GPP, "3GPP Release-15," Retrieved from https://www.3gpp.org/(last visited on 2021/05/01)
[9] Ericsson, "Ericsson Mobility Report 2020," Retrieved from https://www.ericsson.com/en/mobility-report (last visited on 2021/04/30)
[10] Busuness Software Alliance, "First Annual BSA and IDC Global Software Piracy Study," Retrieved from https://www.bsa.org/reports (last visited on 2021/04/28)
[11] W. Cai, M. Chen and V.C.M. Leung, "Toward Gaming as a Service," IEEE Internet Computing, Vol.18, No.3, pp.12-18, 2014.
[12] NARLabs, "The impact and change of mobile communication technology development on life," Retrieved from https://www.narlabs.org.tw/xcscience/ (last visited on 2021/06/01)
[13] S.C.T Chi, "The evolution of mobile communication," Retrieved from https://ejournal.stpi.narl.org.tw/ (last visited on 2021/06/01)
[14] L. Yung-Yu, "The history of mobile communication," Retrieved from https://www.ncc.gov.tw/ (last visited on 2021/06/01)
[15] J. Roach and K. Parrish, "What is cloud gaming ?" Retrieved from https://www.digitaltrends.com/gaming/what-is-cloud-gaming-explained/ (last visited on 2021/05/04)
[16] C.Y. Huang, C.H. Hsu, Y.C. Chang, and K.T. Chen, "GamingAnywhere: An open cloud gaming system," Proc. ACM 4th Conf. Multimedia Syst. (MMSys), pp.36-47, 2013.
[17] Rainway, Retrieved from https://rainway.com/ (last visited on 2021/05/04)
[18] Moonlight, Retrieved from https://moonlight-stream.org/ (last visited on 2021/05/04)
[19] K. Chen, Y. Chang, H. Hsu, D. Chen, C. Huang and C. Hsu, "On the Quality of Service of Cloud Gaming Systems," IEEE Transactions on Multimedia, Vol.16, No.2, pp.480-495, 2014.
[20] O.S. Peñaherrera-Pulla, C. Baena, S. Fortes, E. Baena and R. Barco, "Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks, " Sensors, Vol.21, No.4, pp.1387, 2021.
[21] Y. Sun, T. Wei, H. Li, Y. Zhang and W. Wu, "Energy-Efficient Multimedia Task Assignment and Computing Offloading for Mobile Edge Computing Networks," IEEE Access, Vol.8, pp.36702-36713, 2020.
[22] J.H. Anajemba, T. Yue, C. Iwendi, M. Alenezi and M. Mittal, "Optimal Cooperative Offloading Scheme for Energy Efficient Multi-Access Edge Computation," IEEE Access, Vol.8, pp.53931-53941, 2020.
[23] A. Mseddi, W. Jaafar, H. Elbiaze and W. Ajib, "Joint Container Placement and Task Provisioning in Dynamic Fog Computing," IEEE Internet of Things Journal, Vol.6, No.6, pp.10028-10040, 2019.
[24] M. Amiri, H.A. Osman, S. Shirmohammadi and M. Abdallah, "Toward delay-efficient game-aware data centers for cloud gaming," ACM Trans. Multimedia Comput. Commun. Appl., Vol.12, No.5, pp.1-19, 2016.
[25] A. Alshahrani, I.A. Elgendy, A. Muthanna, A.M. Alghamdi and A. Alshamrani, "Efficient multi-player computation offloading for VR edge-cloud computing systems", Applied Sciences, Vol.10, No.16, pp.5515, 2020.
[26] M. Qin, N. Cheng, Z. Jing, T. Yang, W. Xu, Q. Yang and R.R. Rao, "Service-Oriented Energy-Latency Tradeoff for IoT Task Partial Offloading in MEC-Enhanced Multi-RAT Networks," IEEE Internet of Things Journal, Vol.8, No.3, pp.1896-1907, 2021.
[27] S. Pang and S. Wang, "Joint Wireless Source Management and Task Offloading in Ultra-Dense Network," IEEE Access, Vol.8, pp.52917-52926, 2020.
[28] D. Hossain, T. Sultana, A. Hossain, I. Hossain, L.N.T. Huynh, J. Park and H. Eui-Nam, "Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks," Sensors, Vol.21, No.4, pp.1484, 2021.
[29] D. Hossain, A. Layek, T. Sultana, A. Hossain, I. Hossain, W. Rahman, Y.H. Kim and H. Eui-Nam, "Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks," Proceedings of the International Joint Conference on Computational Intelligence, pp.629-641, 2020.
[30] L.N.T. Huynh, P. Quoc-Viet, P. Xuan-Qui, T.D.T. Nguyen, D. Hossain and H. Eui-Nam, "Efficient computation offloading in multi-tier multi-access edge computing systems: A particle swarm optimization approach," Applied Sciences, Vol.10, No.1, pp.1-17, 2020.
[31] S. Wan, X. Li, Y. Xue, W. Lin and X. Xu, "Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks," J. Supercomput., Vol.76, No.4, pp.2518-2547, 2020.
[32] T. Song, W. Hu, X. Tan, J. Ren, S. Wang and S. Xu, "FAST-RAM: A Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC," Proceedings of the 2020 IEEE Global Communications Conference, pp.1-6, 2020.
[33] V.D. Tuong, T.P. Truong, T. Anh-Tien, A. Masood, D.S. Lakew, C. Lee, Y. Lee and S. Cho, "Delay-Sensitive Task Offloading for Internet of Things in Nonorthogonal Multiple Access MEC Networks," Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), pp.597-599, 2020.
[34] A. Ndikumana, N.H. Tran, T.M. Ho, Z. Han, W. Saad, D. Niyato and C.S. Hong, "Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing," IEEE Transactions on Mobile Computing, Vol.19, No.6, pp.1359-1374, 2020.
[35] D. Hossain, L.N.T. Huynh, T. Sultana, T.D.T. Nguyen, J.H. Park, C.S. Hong and H. Eui-Nam, "Collaborative task offloading for overloaded mobile edge computing in small-cell networks," Proceedings of the 2020 International Conference on Information Networking (ICOIN), 2020.
[36] W. Li, F. Wang, Y. Pan, L. Zhang and J. Liu, "Computing Cost Optimization for Multi-BS in MEC by Offloading," Mobile Netw Appl, 2020.
[37] Y. Yang, X. Chang, Z. Jia, Zhu Han and Zhen Han, "Processing in Memory Assisted MEC 3C Resource Allocation for Computation Offloading," Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, 2020.
[38] Y. Liao, L. Shou, Q. Yu, Q. Ai and Q. Liu, "Joint offloading decision and resource allocation for mobile edge computing enabled networks," Computer Communications, Vol.154, pp.361–369, 2020.
[39] P. Zhou, B. Finley, X. Li and S. Tarkoma, "5G MEC computation handoff for mobile augmented reality," arXiv, 2021.
[40] S.S. Shafiee, S. Schmidt, S. Zadtootaghaj, C. Griwodz and S. Möller, "Delay sensitivity classification of cloud gaming content," Proceedings of the 12th ACM International Workshop on Immersive Mixed and Virtual Environment Systems, pp.25-30, 2020.
[41] S. Göring, R. Steger, R.R.R. Rao and A. Raake, "Automated Genre Classification for Gaming Videos," Proceedings of the IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), pp.1-6, 2020.
[42] ETSI, "Mobile-edge computing introductory technical white paper," Mobile-edge Computing Industry Initiative, 2014.
[43] NS-3, Retrieved from https://www.nsnam.org/ (last visited on 2021/07/02)
[44] S. Massari, N. Mirizzi, G. Piro and G. Boggia, "An Open-Source Tool Modeling the ETSI-MEC Architecture in the Industry 4.0 Context," 2021.
[45] M. Alvarez, E. Salami, A. Ramirez and M. Valero, "A performance characterization of high definition digital video decoding using H.264/AVC," Proceedings of the IEEE Workload Characterization Symposium, pp.24-33, 2005.
[46] C.C. Chi, M. Alvarez-Mesa, B. Bross, B. Juurlink and T. Schierl, "SIMD Acceleration for HEVC Decoding," IEEE Transactions on Circuits and Systems for Video Technology, Vol.25, No.5, pp.841-855, 2015.