FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices

In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particle swarm optimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.