Scheduling algorithms for truly heterogeneous hierarchical fog networks

Abstract

Fog computing has emerged as a viable framework for processing delay sensitive applications. Modern applications consist of latency-sensitive and latency-tolerant jobs, leading to fog architectures that are often multi-tiered/hierarchical. FiFSA (hierarchical first fog scheduling algorithm) and EFSA (hierarchical elected fog scheduling algorithm) are capable of scheduling both online and batch jobs on hierarchical fog-cloud architectures. We consider heterogeneity in computing capacity-both among fog devices in separate layers, and among fog devices in the same layers. In general, online jobs with modest cpu requirements are scheduled on lower tier fog devices, and batch jobs with significant cpu requirements are scheduled on higher tier-fog nodes, or the cloud data center (cdc). FiFSA assigns jobs to the first fog device with sufficient spare capacity. EFSA employs a MinMin heuristic that assigns jobs to the fog device that results in minimum completion time, while considering fog load. The performance of the proposed algorithms has been evaluated on a real-life workload trace, using both simulation scenarios and a prototype testbed. FiFSA and EFSA offer an improvement of 19% to 70% in completion times and an improvement of 42% to 72% in system cost over other comparable algorithms.