Abstract
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.
Highlights
Nowadays, fifth-generation (5G) cellular technology has tremendously changed our daily lives by supporting various new applications, such as video streaming analysis, augmented reality (AR), the Internet of Things (IoT), and autonomous driving [1, 2]
These variables will be sent to the fuzzy-based mobile edge orchestrator, and they play a decisive role in the task-offloading decision process
The mobile edge orchestrator (MEO) needs to scale its capacity with respect to a large number of devices in order to avoid task failure and to reduce service time. To cope with this issue, we propose the online workload balancing algorithm used in a fuzzy-based MEO architecture as shown in Figure 6 in order to improve system performance
Summary
Fifth-generation (5G) cellular technology has tremendously changed our daily lives by supporting various new applications, such as video streaming analysis, augmented reality (AR), the Internet of Things (IoT), and autonomous driving [1, 2]. In the era of the IoT, there is a large number of sensors, actuators, and mobile devices that generate a heavy load for networks [2, 4] These applications require a very short response time and large amounts of computational resources. Simulation results suggest that the fuzzybased MEO should use a worst fit online workload balancing algorithm (2) We propose a new approach to find the proximate server and to balance the resource capacities of edge servers. By keeping track of server geographic locations and available resource capacities, our proposal can find the proximate server when the user requests arrive at the MEO in real time (3) Simulation results demonstrate the following.
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