Multi-objective QoS-aware optimization for deployment of IoT applications on cloud and fog computing infrastructure

Internet of Things (IoT) technology serves many industries to improve their performance. As such, utilizing far distant cloud datacenters to run time-sensitive IoT applications has become a great challenge for the sake of real-time interaction and accurate service delivery time requests. Therefore, the fog computing as a deployment approach of IoT applications has been presented in the edge network. However, inefficient deployment of applications’ modules on the fog infrastructure leads to physical resource and bandwidth dissipations, and debilitation of quality of service (QoS), and also increases the power consumption. When all application’s modules are highly utilized on a single fog node owing to the reduction in the power consumption, the level of service reliability is decreased. To obviate the problem, this paper takes the concept of fault tolerance threshold into account as a criterion to guarantee applications’ running reliability. This paper formulates deployment of IoT applications’ modules on fog infrastructure as a multi-objective optimization problem with minimizing both bandwidth wastage and power consumption approach. To solve this combinatorial problem, a multi-objective optimization genetic algorithm (MOGA) is proposed which considers physical resource utilization and bandwidth wastage rate in their objective functions along with reliability and application’s QoS in their constraints. To validate the proposed method, extensive scenarios have been conducted. The result of simulations proves that the proposed MOGA model has 18, 38, 9, and 43 percent of improvement against MODCS, MOGWO-I, MOGWO-II, and MOPSO in terms of total power consumption (TPC) and it has 6.4, 15.99, 28.15, and 15.43 dominance percent against them in term of link wastage rate (LWR), respectively.