TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments

One of the main issues addressed nowadays in IOT environments is reducing data latency and ensuring an efficient workload balancing. The classical cloud-computing paradigm can no more face the huge amount of data generated by thousands of nearby connected objects. Consequently, the new emerging edge and fog paradigms can help reducing latency, preserving network bandwidth, and improving service quality. Following this trend, we investigate the use of fuzzy logic to improve tasks orchestration. Our proposed TSFTO algorithm is deployed on a multi-layer architecture challenging various device types and important data volume. It also allows managing workload balancing in a cloud, fog, edge, and IoT infrastructure. This process takes into account the unloaded tasks features and the computational resources states. In addition, we have used a list of new criteria as fuzzy entries having a major impact on improving the orchestration method. To validate our proposed TSFTO protocol, we have conducted several simulation tests using the PureEdgeSim simulator. On average, results show that TSFTO reduces the task execution response time by 46.66%, enhances task failure rate by 3%, and saves 10% energy gain.