2021/08/04 by Mohammad Ali Goudarzi, Goudarzi, Mohammad, Marimuthu Palaniswami +3 · 1 citation
Computer Science · Engineering · #IoT and Edge/Fog Computing #Transportation and Mobility Innovations #Cloud Computing and Resource Management
paper · pdf · doi:10.48550/arxiv.2108.02328
Fog/Edge computing model allows harnessing of resources in the proximity of\nthe Internet of Things (IoT) devices to support various types of real-time IoT\napplications. However, due to the mobility of users and a wide range of IoT\napplications with different requirements, it is a challenging issue to satisfy\nthese applications' requirements. The execution of IoT applications exclusively\non one fog/edge server may not be always feasible due to limited resources,\nwhile execution of IoT applications on different servers needs further\ncollaboration among servers. Also, considering user mobility, some modules of\neach IoT application may require migration to other servers for execution,\nleading to service interruption and extra execution costs. In this article, we\npropose a new weighted cost model for hierarchical fog computing environments,\nin terms of the response time of IoT applications and energy consumption of IoT\ndevices, to minimize the cost of running IoT applications and potential\nmigrations. Besides, a distributed clustering technique is proposed to enable\nthe collaborative execution of tasks, emitted from application modules, among\nservers. Also, we propose an application placement technique to minimize the\noverall cost of executing IoT applications on multiple servers in a distributed\nmanner. Furthermore, a distributed migration management technique is proposed\nfor the potential migration of applications' modules to other remote servers as\nthe users move along their path. Besides, failure recovery methods are embedded\nin the clustering, application placement, and migration management techniques\nto recover from unpredicted failures. The performance results show that our\ntechnique significantly improves its counterparts in terms of placement\ndeployment time, average execution cost of tasks, total number of migrations,\ntotal number of interrupted tasks, and cumulative migration cost.\n