GMTA: A Geo-Aware Multi-Agent Task Allocation Approach for Scientific Workflows in Container-Based Cloud

Publisher:
Institute of Electrical and Electronics Engineers
Publication Type:
Journal Article
Citation:
IEEE Transactions on Network and Service Management, 2020, 17, (3), pp. 1568-1581
Issue Date:
2020-09-01
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Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.
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