鑑於近年大量如人工智慧或區塊鏈等高性能消耗應用的興起使得雲端資源漸趨吃緊,以物聯網裝置來卸載雲端應用的研究成果正不斷地進展。然而,由於物聯網的高度動態特性使得其中裝置難以被組織並執行卸載任務。

為此,本研究提出了一套用於高度動態的物聯網環境的任務卸載方法。此方法能根
據卸載任務的被執行時間來隨時動態調整任務卸載配置機制。並且,此配置方法不
同於大部分的卸載方法,其在追求執行時間達到標準的同時亦盡量避免佔用較高性
能的節點。有了以上兩個特點後,使得卸載系統具備動態環境適應性以及任務執行
效益性,在實際應用於物聯網環境有高度的應用能力。並且本研究考慮到不同物聯
網環境其動態性與負載程度恐怕相去甚遠,因此另外提出更新距離 (UpdateWidth) 與系統負載 (SystemLoad) 兩個參數,以根據實際環境調整對於動態環境的適應能力與高性能節點對於低消耗任務的執行傾向。

最後,透過文末提出的實驗結果,可以發現系統在擁有充足可選的節點時時,其時
間消耗的表現與預期時間消耗差距小於 5%。另外,在同時併發實驗時也能注意到,若卸載的任務消耗高於或者低於系統中的可分配節點之能力時,整體的執行時間變異將擴大,整體平均消耗遠離預期消耗的同時,亦提高最高與最低時間消耗的差距。

In this paper, we proposed a task offloading method for highly dynamic IoT environments, named Progressive Dynamic Task Offloading(PDTO). PDTO dynamically adjusts the task offloading strategy to achieve a more efficient deployment at any time. Therefore, PDTO ensures the actual time consumption close to the preferred time cost while avoiding the use of higher performance nodes. These properties make PDTO adaptable, efficient, and feasible to offload tasks in higly dynamic environments like IoT. Considering the dynamics and loading of different IoT environments are quite different, the two parameters are proposed to adjust the adaptability and the tendency of high-performance nodes to execute low cost tasks.

Finally, through the experimental results, we found that if there is a candidate node with the ideal performance, the actual time costs will close to the preferred time cost which difference won’t greater than 5%. On the contrary, if no ideal candidate node existing, the variation of overall time costs will be expanded, and the average changes.

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