隨著越來越多物聯網應用部署在環境中，大量的資料將不斷的湧入雲端，造成雲端網路的阻塞。因此，思科提出霧運算概念來改善傳統物聯網服務過於依賴雲端運算的問題。在霧雲架構中，物聯網裝置將彼此合作，讓部分工作在本地端先行處理，藉此舒緩雲端壓力，降低網路延遲。對此，霧系統的資源分配就很顯得格外重要。哪些工作要分給哪些物聯網裝置處理，哪些工作又要送到雲端執行，這些決定都會嚴重影響到霧系統的效能。然而，霧系統屬於動態且多異質資源的環境，傳統的資源分配方法並不適用。為了讓霧系統能有效的分配物聯網裝置資源，本論文將提出一適用於霧運算之動態多資源管理系統。
本論文首先基於二元粒子群演算法以及貪婪演算法的分別提出兩個動態多資源分配方法。其中，二元粒子群演算法在效果最佳化上有很大的優勢，但是缺點是時間成本稍大；然而貪婪演算法雖然能快速的提供資源分配策略，但是所提供的策略有時效果不佳。因此本論文進一步透過結合二元粒子群演算法與貪婪演算法的優點，提出強化型二元粒子群演算法。藉由決策管理模組決定適合當下情境的資源分配策略，並且在各種不同的動態環境下探討演算法效果好壞。此外，為了舒緩雲端的工作壓力並處理資料隱私性問題，本論文特別設計了網路壅塞與資料保護的解決方案，讓管理者可以動態的根據不同網路壅塞情況以及服務需求採用不同的解決方案。
最後由實驗結果得知，在各種不同的動態環境設定下，例如服務尖峰/離峰、不同資源變動率與混和式環境，本論文提出的方法都能比現有方法更有效的分配霧系統的資源，並且能滿足使用者在資料隱私性、服務即時性，以及網路壅塞舒緩的各種需求。由此可知，本論文提出的系統較其他方法更適用於霧系統的資源排程。

With more IoT services are provided in our daily life, numerous data are transmitted to cloud servers leading to congestion. Thus, Cisco proposes the concept of fog computing to alleviate the problem that IoT services highly rely on cloud computing. In the Fog-Cloud architecture, IoT devices will cooperate to process some tasks locally instead of transmitting to cloud severs totally, to alleviate network loading and reduce latency. Since the decision that what tasks are performed locally and what tasks will be transmitted to cloud servers will influence system performance significantly, providing resource allocation in fog systems becomes an important issue. However, fog systems are usually dynamic with multiple resources, so the traditional resource allocation approaches are impractical. To allocate resource of IoT devices in fog systems effectively, the Dynamic Multiple Resource Management (DMRM) is further proposed.
Based on the binary particle swarm optimization (BPSO) algorithm and greedy principle, two kinds of dynamic multiple resource allocation (DMRA) approaches are proposed, respectively. The DMRA based on BPSO can provide a more practical solution but with higher time consumption. The DMRA based on greedy principle can be performed efficiently, but the solution it provided is sometimes worse. Hence, DMRM applies the Enhanced-BPSO (E-BPSO) approach which integrates both BPSO and greedy-based approaches, to derive a practical resource allocation solution efficiently. After that, the proposed DMRM is performed in various dynamic environments to verify its results. Moreover, to alleviate the workload of cloud servers and protect data privacy, two solutions are proposed for users to deal with network congestion and data protection.
Finally, according to the experiment results, the proposed DMRM is shown to have better solutions than existing approaches in allocating resources under various situations such as service peak/off-peak period, various resource available rate and both of the above situations. Furthermore, the requirements of data privacy, service instantaneity, and network offloading, can also be achieved. Therefore, the proposed DMRM can provide the most practical resource allocation solution in fog systems.

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