在現今的社會中，人們已經可以輕易地透過行動裝置連接網際網路及處理資料。然而，行動裝置的運算能力有限，沒辦法有效的處理高複雜度的應用程式，所以必須由擁有豐富運算能力的雲端伺服器處理，雖然雲端伺服器擁有強大的運算及處理能力，但因為距離使用者較遠而造成較長的延遲時間，進而影響使用者體驗。一種新穎的解決方式就是把運算複雜度較高的工作從行動裝置傳送到邊緣網路處理，也就是所謂的行動邊緣運算（Mobile Edge Computing），換言之雲端伺服器也能夠分擔邊緣伺服器的工作量，如此之下在滿足使用者的需求的同時能夠降低系統整體反應時間（Response Time）。本論文探討在結合了邊緣伺服器以及雲端伺服器的網路中如何提高工作卸載（Task Offloading）的效率，接著分析整體系統的反應時間。在本篇論文的假設模型中，工作會優先送到離使用者較近的邊緣伺服器，並且提供立即的服務，假如邊緣伺服器沒有足夠的資源提供服務，工作則會轉送給雲端伺服器處理，再將最終的結果回傳給使用者。基於假設的模型，將會建構馬可夫模型（Markovian Model）來分析系統的平均反應時間。最終的實驗結果可以看到模擬和假設模型在經過不同的情境測試下是完全吻合的。

The prevalence of mobile devices has changed the way that people access the network and process their data. Nevertheless, the computational resources in mobile devices are usually very limited and restrains their capability to handle the increasing complexity of certain applications. A potential solution for the problem is to offload computational-intensive tasks from mobile devices to the edge or cloud servers such that the response time of the offloaded tasks can be reduced. This paper investigates the performance of task offloading in an integrated edge-cloud computing network and analytically model the response time of task offloading on it. In the investigated edge-cloud computing paradigm, the offloaded tasks are first sent to the edge server, which is possibly close to the user and may provide the service promptly. If the edge server does not have sufficient resources to process the task, it will be forwarded to the cloud server. After the task is completed, the results are then returned to the user. A Markovian model was developed to manipulate the task offloading process and the average response time of the offloaded tasks was analytically derived based on the model. Extensive simulations were conducted to verify the correctness of the analytic solutions for the response time. From the simulation results, the derived solutions matched the simulation results almost exactly for a variety of scenarios.

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