The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g., close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for the orchestration of services in data center environments, where it is very efficient. It, however, fails to give the same performance when including edge resources. At the edge, resources are more limited, and networking conditions are changing over time.

In this article, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. This methodology can adapt to changing environments, e.g., moving end-users. We are also monitoring some Key Performance Indicators of the applications to ensure that cost optimizations do not negatively impact their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the service and, if so, moves the service. To demonstrate the performance of our scheduling approach, we evaluate it using a vehicle cooperative perception use case, a representative 5G application. With this use case, we can demonstrate that our scheduling approach can robustly lower the cost in different scenarios, while other approaches that are already available fail in either being adaptive to changing environments or will have poor cost-effectiveness in some scenarios.

边缘到数据中心计算连续体是位于网络边缘（例如，靠近 5G 天线）和传统数据中心服务器之间任何位置的计算资源的聚合。 Kubernetes 是数据中心环境中服务编排的事实标准，非常高效。然而，当包含边缘资源时，它无法提供相同的性能。在边缘，资源更加有限，并且网络条件随着时间的推移而变化。

在本文中，我们提出了一种降低在边缘到云计算连续体中运行应用程序的成本的方法。该方法可以适应不断变化的环境，例如移动的最终用户。我们还监控应用程序的一些关键性能指标，以确保成本优化不会对其服务质量产生负面影响。此外，为了确保即使用户移动时性能也能达到最佳，我们引入了一个后台进程，该进程会定期检查是否有更好的位置可用于服务，如果有，则移动服务。为了展示我们的调度方法的性能，我们使用车辆协作感知用例（一个代表性的 5G 应用程序）对其进行评估。通过这个用例，我们可以证明我们的调度方法可以在不同场景下显着降低成本，而其他现有方法要么无法适应不断变化的环境，要么在某些场景下成本效益较差。