Edge Federation: A Dependency-Aware Multi-Task Dispatching and Co-location in Federated Edge Container-Instances
Authors: Awada, U. and Zhang, J.
Journal: Proceedings - 2020 IEEE 13th International Conference on Edge Computing, EDGE 2020
Existing research on Edge Computing has proposed several edge deployment types, such as unmanned aerial vehicles (UAV)-enabled edge computing, telecommunication base stations endowed with edge clusters, fog nodes, cloudlets, etc. However, none of them consider the ability of keeping edge resources running across various edge deployments in a single pool, such that these resources can be holistically managed and controlled from a single federated plane, also eliminate vendor lock-in situations. Moreover, these schemes assume each server can only execute one task or job at any time. They do not consider lightweight tools that can wrap, isolate and co-locate multiple tasks in a virtualized server. As such, they can easily result in fragmentation and over-allocation of resources. Furthermore, as modern applications are becoming more complex, relatively few research considers the different characteristics of tasks, like the class, constraints and dependencies. This research aims to address these limitations, extend the state-of-the-art by providing an intelligent resource scheduling and optimization solutions for high performance in a federated edge computing system, considering both task dependencies and heterogeneous resource demands at the same time. Extensive simulations on real-world data-trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands, show the effectiveness, faster executions, and resource efficiency of our approach.