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.
翻译:og/Edge 计算模型允许在Things(IoT)的互联网附近使用资源,以支持各种实时IoT应用程序,然而,由于用户的流动性和具有不同要求的IoT应用程序的广泛范围,满足这些应用程序的要求是一个具有挑战性的问题。由于资源有限,在不同的服务器上实施IoT应用程序可能并不总是可行,而在不同服务器上实施IoT应用程序需要服务器之间进一步协作。此外,考虑到用户流动性,每个IoT应用程序的一些模块可能需要迁移到其他服务器,以便执行,导致服务中断和额外的平均执行费用。在本篇文章中,我们提出了一个新的等级雾计算环境加权成本模型,这是根据IoT应用程序的响应时间和IoT装置的能源消耗,以尽量减少使用IoT应用程序的成本和潜在的迁移。此外,为了协作执行从应用模块中释放的任务,我们提出了一个分散的组合技术,以最大限度地减少应用IoT应用程序的总成本,导致服务中断,在多种服务器上,在迁移的服务器上,在迁移过程中,将一个潜在的迁移方法加以传播。