GMAS: A Geo-Aware MAS-Based Workflow Allocation Approach on Hybrid-Edge-Cloud Environment

Cloud computing is expanding to distributed edge computing(or known as fog computing). Connecting edge and cloud open great potential for real-time and mobility support workflow applications. However, scheduling workflow on a hybrid edge-cloud environment is an NP-hard problem. This paper proposes the Geo-Aware Multi-Agents-System-Based Workflow Allocation Approach(GMAS). Leveraging a novel geo-aware negotiation mechanism, GMAS addresses resource location caused transmission delays, which are the primary sources of workflow bottlenecks. In Multi-Agents-System(MAS), this paper proposes a geo-aware cost model and a dynamic workflow re-structuring strategy that decrease the impact of resource locations on workflow cost. Finally, this paper evaluates GMAS on Cloudsim, and the result shows that GMAS decreases the workflow makespan and traffic overheads.