近年來，網絡中出現了許多的物聯網裝置。而這些物聯網裝置通常因為物品尺寸大小的限制，導致計算能力較差。因此，物聯網裝置必須將其任務卸載到雲端伺服器進行處理。近幾年，邊緣計算的技術被提出，這項技術使得能夠處理物聯網裝置任務的服務提供商可以將服務放置在更靠近終端設備的邊緣伺服器上，這能有效提高服務品質。但，如果物聯網裝置的任務過度集中在網路中的特定伺服器上，仍然可能會降低整體的網絡性能。因此，適當的服務放置和物聯網裝置的任務分配是很重要的，這可以影響網路中伺服器的負載平衡，並且避免上述的情況發生。而任務傳輸的距離過長的話，有可能導致延遲時間過長的問題，因此任務傳輸的距離也是本論文考慮的重要因素。本論文透過考慮網路中伺服器的工作量和物聯網任務的傳輸距離，達到網路中伺服器的負載平衡。服務放置和任務分配的問題為一個混合整數線性規劃問題。對此問題，本論文所提出一個基於模擬退火的演算法來解決服務放置問題，而任務分配則使用拉格朗日對偶理論和次梯度方法來解決。最後，實驗結果證實所提出的演算法是有效的，並且明顯優於比較方法。此外，實驗結果驗證了所提出的演算法適用於大規模和小規模的網絡拓撲。

In recent years, many Internet-of-Things (IoT) devices have emerged in the network. These IoT devices are usually limited in size, resulting in poor computing capabilities. Therefore, IoT devices must offload their tasks to the cloud server for processing. Recently, Multi-access edge computing (MEC) allows service providers to place services on edge servers closer to terminal devices, effectively improving the quality of services (QoS). However, if tasks of IoT devices are overly concentrated on specific servers, it may still degrade overall network performance. Therefore, proper service placement and task allocation are important, which can affect the load balancing of servers in the network to avoid the above situation. This paper will consider both workload of servers in the network and the distances from the nodes to the servers. The service placement and task allocation will be formulated as a mixed-integer linear programming problem. The proposed algorithm is based on simulated annealing to solve the service placement, while the task allocation is solved using the Lagrangian duality theory with the sub-gradient method. The experimental results showed that the proposed algorithm was efficient and significantly outperformed the compared methods. Also, the experimental results verified that the proposed algorithm was suitable for both large-scale and small-scale network topologies.

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