Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1568-1581 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Network and Service Management |
| Volume | 17 |
| Issue number | 3 |
| Early online date | 21 May 2020 |
| DOIs | |
| Publication status | Published - Sept 2020 |
| Externally published | Yes |
Keywords
- Geo-aware
- container cloud
- multi-agent system
- scientific workflow
- task allocation
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