A fault-intrusion-tolerant system and deadline-aware algorithm for scheduling scientific workflow in the cloud

Background: Recent technological developments have enabled the execution of more scientific solutions on cloud platforms. Cloud-based scientific workflows are subject to various risks, such as security breaches and unauthorized access to resources. By attacking side channels or virtual machines, attackers may destroy servers, causing interruption and delay or incorrect output. Although cloud-based scientific workflows are often used for vital computational-intensive tasks, their failure can come at a great cost. Methodology: To increase workflow reliability, we propose the Fault and Intrusion tolerant Workflow Scheduling algorithm (FITSW). The proposed workflow system uses task executors consisting of many virtual machines to carry out workflow tasks. FITSW duplicates each sub-task three times, uses an intermediate data decision-making mechanism, and then employs a deadline partitioning method to determine sub-deadlines for each sub-task. This way, dynamism is achieved in task scheduling using the resource flow. The proposed technique generates or recycles task executors, keeps the workflow clean, and improves efficiency. Experiments were conducted on WorkflowSim to evaluate the effectiveness of FITSW using metrics such as task completion rate, success rate and completion time. Results: The results show that FITSW not only raises the success rate by about 12%, it also improves the task completion rate by 6.2% and minimizes the completion time by about 15.6% in comparison with intrusion tolerant scientific workflow ITSW system.

Automated summary

The FITSW algorithm proposes a method to increase the reliability of workflows by duplicating sub-tasks, using an intermediate data decision-making mechanism, and deadline partitioning method to achieve dynamic task scheduling and improve efficiency. Experimental results show that FITSW not only improves success rate and task completion rate, but also reduces completion time compared to systems with intrusion tolerant features.