A Distributed Application Placement and Migration Management Techniques for Edge and Fog Computing Environments
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 37–56 (2021)
Abstract. Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of latency-sensitive IoT applications. However, due to the mobility of users and a wide range of IoT applications with different resource 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 the execution of IoT applications on different servers requires further collaboration and management among servers. Moreover, 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 demonstrate that our technique significantly improves its counterparts in terms of placement deployment time, average execution cost of tasks, the total number of migrations, the total number of interrupted tasks, and cumulative migration cost.
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