scholarly journals Re-provisioning of Cloud-Based Execution Infrastructure Using the Cloud-Aware Provenance to Facilitate Scientific Workflow Execution Reproducibility

2016 ◽  
pp. 74-94 ◽  
Author(s):  
Khawar Hasham ◽  
Kamran Munir ◽  
Richard McClatchey ◽  
Jetendr Shamdasani
Keyword(s):  
Scientific Workflow ◽  
Workflow Execution

A QoS-aware exception handling method in scientific workflow execution

2011 ◽  
Vol 23 (16) ◽  
pp. 1951-1968 ◽  
Author(s):  
Lianyong Qi ◽  
Wenmin Lin ◽  
Wanchun Dou ◽  
Jian Jiang ◽  
Jinjun Chen
Keyword(s):  
Exception Handling ◽  
Scientific Workflow ◽  
Workflow Execution ◽  
Handling Method

scholarly journals Dynamic Fault-Tolerant Workflow Scheduling with Hybrid Spatial-Temporal Re-Execution in Clouds

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 169 ◽  
Author(s):  
Na Wu ◽  
Decheng Zuo ◽  
Zhan Zhang
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Critical Path ◽  
High Reliability ◽  
Low Cost ◽  
Cost Effective ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Workflow Execution ◽  
Computing Environments

Improving reliability is one of the major concerns of scientific workflow scheduling in clouds. The ever-growing computational complexity and data size of workflows present challenges to fault-tolerant workflow scheduling. Therefore, it is essential to design a cost-effective fault-tolerant scheduling approach for large-scale workflows. In this paper, we propose a dynamic fault-tolerant workflow scheduling (DFTWS) approach with hybrid spatial and temporal re-execution schemes. First, DFTWS calculates the time attributes of tasks and identifies the critical path of workflow in advance. Then, DFTWS assigns appropriate virtual machine (VM) for each task according to the task urgency and budget quota in the phase of initial resource allocation. Finally, DFTWS performs online scheduling, which makes real-time fault-tolerant decisions based on failure type and task criticality throughout workflow execution. The proposed algorithm is evaluated on real-world workflows. Furthermore, the factors that affect the performance of DFTWS are analyzed. The experimental results demonstrate that DFTWS achieves a trade-off between high reliability and low cost objectives in cloud computing environments.

A clustering based coscheduling strategy for efficient scientific workflow execution in cloud computing

2013 ◽  
Vol 25 (18) ◽  
pp. 2523-2539 ◽  
Author(s):  
Kefeng Deng ◽  
Kaijun Ren ◽  
Junqiang Song ◽  
Dong Yuan ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Scientific Workflow ◽  
Workflow Execution
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