Optimal Scheduling Algorithm for Distributed Streaming of Data Flow across Edge Devices and Cloud

Edge computing is an emerging paradigm to assist intelligent decisions for cloud centric analytics. The major limitation of edge computing is the non-availability of a open-source platforms-as-a- service for various applications across cloud and edge. ECHO(an adaptive orchestration platform for streaming hybrid data flows across cloud and edge) attempts to fill this gap . It enables streaming data flows across distributed resources where user tasks are represented as vertices in a directed acyclic graph (DAG) and edges represent the routing channels between data and tasks. These DAGs are executed upon data arrival. ECHO’s current scheduler schedules jobs using round robin algorithm. This paper proposes improvement in ECHO’s scheduler. We consider current device health before scheduling dataflow in ECHO. The proposed scheduling algorithm makes use of CPU utilization and memory utilization as parameters. The experimental results show that the proposed algorithm improves the scheduler performance.