Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the most suitable locations within a network to deploy various services, is critical to balancing workloads dynamically and ensuring efficient resource utilization. In this paper, we jointly optimize service placement, edge/cloud cooperation, task offloading, and bandwidth allocation to enhance processing efficiency and response times. The main objective is to minimize both the overall end-to-end latency and the system cost, including service deployment and operational costs. The formulated problem belongs to the class of non-convex mixed-integer nonlinear programming, where finding a feasible solution is already challenging. Towards a stable system, we first transform the original problem into a more tractable form and then decompose it into sub-problems which are solved at different timescales. Combining tools from relaxation and the successive convex approximation method, we develop iterative algorithms to solve these problems efficiently. With an appropriate penalty parameter, the proposed algorithms guarantee convergence to at least a local optimum. We produce extensive numerical results to demonstrate the superior performance of the proposed algorithms over benchmark schemes as well as emphasize the significance of the joint service placement and resource allocation in enhancing system performance and efficiency.

翻译：分层边缘云计算辅助的物联网网络为日益增长的数据密集型物联网设备提供了低延迟和低成本的服务。然而，优化服务放置——即确定在网络中部署各种服务的最优位置——对于动态平衡负载并确保资源高效利用至关重要。本文联合优化了服务放置、边缘/云协作、任务卸载和带宽分配，以提升处理效率和响应时间。主要目标是最小化整体端到端延迟和系统成本，包括服务部署和运营成本。所构建的问题属于非凸混合整数非线性规划类，其中寻找可行解本身已具挑战性。为实现稳定系统，我们首先将原始问题转化为更易处理的形式，然后将其分解为在不同时间尺度上求解的子问题。结合松弛方法和逐次凸近似工具，我们开发了迭代算法以高效求解这些问题。在适当的惩罚参数下，所提算法保证至少收敛到局部最优。我们通过大量数值结果展示了所提算法相较于基准方案的优越性能，并强调了联合服务放置与资源分配在提升系统性能与效率方面的重要性。