A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.

中文翻译：

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具有垂直和水平卸载功能的云边缘计算系统的工作负载和容量优化

提出了云计算和边缘计算之间的协作集成，以便能够利用这两种技术的优势。然而，现有的大多数研究只考虑了仅支持本地边缘节点和远程云服务器之间垂直卸载的两层云边缘计算系统。因此，本文提出了一种云边缘计算的通用架构，旨在提供服务节点之间的垂直和水平卸载。为了研究不同操作场景设计的有效性，我们将其表述为工作负载和容量优化问题，目标是最小化系统计算和通信成本。因为这样的混合整数非线性规划 (MINLP) 问题是 NP-hard 问题，我们进一步开发了一种近似算法，该算法应用分支定界方法来迭代地获得最优解。实验结果表明，与仅支持垂直卸载的传统设计相比，这种云边缘计算架构可以显着降低系统总成本约34%。我们的结果还表明，为了适应相同数量的输入工作负载，异构服务分配场景比同构场景需要高出约 23% 的系统成本。