In the context of the rapid deployment of IoT, 5G, and cloud computing, numerous emerging applications demand efficient networked computing capacity for task offloading from mobile and IoT users. This paper focuses on the optimization of network resource allocation and reduction of end-to-end (E2E) latency through the strategic decision of whether and where to offload user requests in a cloud-edge elastic optical network (CE-EON). To address this problem, we first formulate the problem into an integer linear programming (ILP) model as an initial solution. Additionally, we introduce several heuristic approaches that leverage the concept of partial resource offloading, specifically based on proportional segmentation (PRO_PS), partial resource offloading based on average segmentation (PRO_AS), all resource offloading (ARO), and all local processing (ALP). Furthermore, we implement a collaborative cloud-edge (CCE) offloading approach as a baseline for comparison. Our results demonstrate that the PRO_PS approach closely approximates the optimal solutions obtained from the ILP model in static scenarios. Moreover, the PRO_PS approach achieves the lowest E2E latency, blocking probability, and optimized network resource allocation in dynamic scenarios. This highlights the effectiveness of the proposed approach in improving system performance and addressing the challenges of CE-EONs.

中文翻译：

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云边弹性光网络中的低时延部分资源卸载

在物联网、5G和云计算快速部署的背景下，众多新兴应用需要高效的网络计算能力来卸载移动和物联网用户的任务。本文重点关注通过云边缘弹性光网络（CE-EON）中是否以及在何处卸载用户请求的战略决策来优化网络资源分配并减少端到端（E2E）延迟。为了解决这个问题，我们首先将问题表述为整数线性规划（ILP）模型作为初始解。此外，我们还介绍了几种利用部分资源卸载概念的启发式方法，特别是基于比例分段（PRO_PS）、基于平均分段的部分资源卸载（PRO_AS）、所有资源卸载（ARO）和所有本地处理（ALP） 。此外，我们实施了协作云边缘（CCE）卸载方法作为比较的基准。我们的结果表明，PRO_PS 方法非常接近静态场景中从 ILP 模型获得的最优解。此外，PRO_PS方法实现了动态场景下最低的端到端时延、阻塞概率和优化的网络资源分配。这凸显了所提出的方法在提高系统性能和应对 CE-EON 挑战方面的有效性。