Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of real-time IoT applications. However, due to the mobility of users and a wide range of IoT applications with different requirements, it is a challenging issue to satisfy these applications' requirements. The execution of IoT applications exclusively on one fog/edge server may not be always feasible due to limited resources, while execution of IoT applications on different servers needs further collaboration among servers. Also, considering user mobility, some modules of each IoT application may require migration to other servers for execution, leading to service interruption and extra execution costs. In this article, we propose a new weighted cost model for hierarchical fog computing environments, in terms of the response time of IoT applications and energy consumption of IoT devices, to minimize the cost of running IoT applications and potential migrations. Besides, a distributed clustering technique is proposed to enable the collaborative execution of tasks, emitted from application modules, among servers. Also, we propose an application placement technique to minimize the overall cost of executing IoT applications on multiple servers in a distributed manner. Furthermore, a distributed migration management technique is proposed for the potential migration of applications' modules to other remote servers as the users move along their path. Besides, failure recovery methods are embedded in the clustering, application placement, and migration management techniques to recover from unpredicted failures. The performance results show that our technique significantly improves its counterparts in terms of placement deployment time, average execution cost of tasks, total number of migrations, total number of interrupted tasks, and cumulative migration cost.

中文翻译：

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一种面向边缘和雾计算环境的分布式应用部署和迁移管理技术

雾/边缘计算模型允许利用物联网 (IoT) 设备附近的资源来支持各种类型的实时物联网应用。然而，由于用户的移动性和广泛的物联网应用的不同要求，满足这些应用的要求是一个具有挑战性的问题。由于资源有限，仅在一台雾/边缘服务器上执行物联网应用程序可能并不总是可行，而在不同服务器上执行物联网应用程序需要服务器之间的进一步协作。另外，考虑到用户的移动性，每个物联网应用的部分模块可能需要迁移到其他服务器执行，导致服务中断和额外的执行成本。在本文中，我们为分层雾计算环境提出了一种新的加权成本模型，在物联网应用的响应时间和物联网设备的能耗方面，最大限度地降低运行物联网应用和潜在迁移的成本。此外，提出了一种分布式集群技术，以实现服务器之间从应用程序模块发出的任务的协作执行。此外，我们提出了一种应用程序放置技术，以最大限度地降低以分布式方式在多台服务器上执行物联网应用程序的总体成本。此外，还提出了一种分布式迁移管理技术，用于在用户沿其路径移动时将应用程序模块潜在迁移到其他远程服务器。此外，故障恢复方法嵌入在集群、应用程序放置和迁移管理技术中，以从不可预测的故障中恢复。