Dynamic IoT service placement based on shared parallel architecture in fog-cloud computing

Abstract

Fog-cloud computing is a promising computing paradigm for processing and storing massive data produced by Internet of Things (IoT) devices. Considering that the fog computing nodes are distributed, heterogeneous and have limited resources, it is not possible to deploy all the services belonging to IoT applications on the fog environment. Therefore, an efficient and autonomous mechanism is needed to map IoT services on the fog environment. The problem becomes more complicated when the various requirements of services including response time and cost are considered. With this motivation, we introduce a mixed-integer linear programming model to address the Fog Service Placement (FSP) problem in fog-cloud computing environments. This model is formulated as a multi-objective model with the aim of minimizing deadline violation, resource loss and service cost, and maximizing resource usage. Meanwhile, we propose an Elitism-based Genetic Algorithm to solve the FSP problem developed with a Shared Parallel Architecture (EGASPE). Our algorithm considers the efficient distribution of resources under certain Quality of Service (QoS) requirements and seeks a balance between exploration and exploitation. Also, EGASPE can save more resources for future requests by considering the deadline of IoT services over time. Extensive experiments have been performed to evaluate the performance of EGASPE on a synthetic fog environment. The obtained results show the effectiveness of EGASPE up to 6.5% on average compared to state-of-the-art algorithms.