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Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

Fuzzy-Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach