A Novel Approach to Cost-Efficient Scheduling of Multi-workflows in the Edge Computing Environment with the Proximity Constraint

The edge computing paradigm is featured by the ability to offload computing tasks from mobile devices to edge clouds and provide high cost-efficient computing resources, storage and network services closer to the edge. A key question for workflow scheduling in the edge computing environment is how to reduce the monetary cost while fulfilling Service-Level-Agreement in terms of performance and quality-of-service requirements. However, it’s still a challenge to guarantee user-perceived quality of service of applications deployed upon edge infrastructures due to the fact that such applications are constantly subject to negative impacts, e.g., network congestions, unexpected long message delays, shrinking coverage range of edge servers due to battery depletion. In this paper, we study the multi-workflow scheduling problem and propose a novel approach to Cost-Efficient Scheduling of Multi-Workflows in the Edge Computing Environment With Proximity Constraint. The proposed approach aims at minimizing edge computing costs while meeting user-specified workflow completion deadlines and leverages a discrete firefly algorithm for yielding the scheduling plan. We conduct experimental case studies based on multiple well-known scientific workflow templates and a real-world dataset of edge resource locations as well. Experimental results clearly suggest that our proposed approach outperforms traditional ones in terms of cost and makespan.