Smart cities generate huge volumes of data to be processed by applications with different criticality and requirements. For example, a healthcare application needs lower latency when requested from an ambulance travelling to a hospital during an emergency compared to applications in less-critical domains. Cities can use Multi-access Edge Computing to reduce latency by placing applications’ services closer to users. A service placement process selects the set of servers to run the services for deployment. Smart cities challenge this selection as a large number of servers and services generate a large number of potential solutions with different QoS properties. Additionally, placement approaches must consider applications’ criticality and users’ mobility to offer an appropriate overall latency. Current approaches have considered servers’ utilisation and users’ location to place services. However, they do not consider applications’ criticality and mobile users’ paths. This paper presents MAACO, a Mobility-Aware, priority-driven, ACO-based service placement model that prioritises applications according to their criticality and minimises critical applications’ latency, while considering predicted paths for mobile users. Evaluation results show that MAACO achieves lower latency and waiting time compared against baselines at the cost of reduced load balance between the network servers.

中文翻译：

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MAACO：智慧城市的动态服务安置模型


智慧城市会产生大量数据，供具有不同关键性和要求的应用程序处理。例如，与不太关键领域的应用程序相比，在紧急情况下前往医院的救护车发出请求时，医疗保健应用程序需要更低的延迟。城市可以使用多接入边缘计算，通过将应用程序的服务放置在更靠近用户的位置来减少延迟。服务放置过程选择一组服务器来运行服务以进行部署。智慧城市对这种选择提出了挑战，因为大量服务器和服务会产生大量具有不同 QoS 属性的潜在解决方案。此外，放置方法必须考虑应用程序的关键性和用户的移动性，以提供适当的总体延迟。当前的方法考虑了服务器的利用率和用户的位置来放置服务。然而，他们没有考虑应用程序的关键性和移动用户的路径。本文介绍了 MAACO，这是一种移动感知、优先级驱动、基于 ACO 的服务放置模型，该模型根据应用程序的重要性对应用程序进行优先级排序，并最大限度地减少关键应用程序的延迟，同时考虑移动用户的预测路径。评估结果表明，与基线相比，MAACO 实现了更低的延迟和等待时间，但代价是网络服务器之间的负载平衡减少。