Cloud Workflow Scheduling With VM Reuse and Security Risk Estimation
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Solution Overview
Problem
Existing workflow scheduling methods in cloud computing environments fail to optimally utilize virtual machines and adequately estimate security risks, leading to inefficiencies in cost and execution time, particularly for scientific workflows with heterogeneous tasks.
Innovation Solution
A method and system for secure scheduling of workflows and virtual machine utilization that employs combinatorial optimization techniques to select optimal VM type and security level combinations, while ensuring risk rates remain below permissible limits, by computing timing parameters and sorting tasks based on start times to minimize costs and execution times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing workflow scheduling methods are used, then virtual machines can be utilized for task execution, but optimal VM reuse and security risk estimation are not achieved, leading to higher costs and longer execution times
Solution Approach 1:
The system performs preliminary security risk estimation and VM suitability assessment before task allocation. By pre-calculating security risks and VM compatibility for each task, the system makes informed scheduling decisions that optimize both execution efficiency and resource utilization, avoiding reactive adjustments during workflow execution.
Solution Approach 2:
The system dynamically adjusts scheduling parameters based on task characteristics, security requirements, and VM availability. By changing parameters such as security level thresholds, VM selection criteria, and risk tolerance levels, the system optimizes the balance between execution speed and resource efficiency for different workflow scenarios.
2Reliability
If security risk estimation is not performed, then workflow execution can proceed faster, but security vulnerabilities may be overlooked
Solution Approach 1:
Security risk estimation is performed as a preliminary step before task scheduling and execution. By assessing security risks in advance and categorizing tasks according to their security requirements, the system ensures that security considerations are integrated into the scheduling process without causing delays during actual workflow execution.
Solution Approach 2:
The system applies differentiated security measures to different tasks based on their specific risk profiles and requirements. Instead of applying uniform security checks to all tasks, the system tailors security estimation and protection levels to match the actual security needs of each task, optimizing both security assurance and processing efficiency.
3Loss of energy
If VM re-utilization is optimized, then costs are reduced, but task scheduling complexity increases
Solution Approach 1:
The system optimizes VM re-utilization by dynamically adjusting parameters such as VM retention duration, task-VM matching criteria, and resource allocation thresholds. By changing these parameters based on workflow patterns and resource availability, the system reduces computational costs while managing scheduling complexity through adaptive parameter tuning rather than complex algorithms.
Data Source
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AI summary
This disclosure relates to method and system for secure scheduling of workflows and virtual machine utilization in cloud. Scheduling of tasks in workflow comprises of heterogeneous and interdependent computational tasks. The method receives a set of workflows comprising of one or more heterogeneous tasks. Further, a set of parameters are extracted from each heterogeneous task to select a set of optimal VM type combination parameters and a set of security level combination parameters. The method selects the optimized combination of VM types, security service levels and task order. Further, a workflow schedule is generated for the tasks of the selected VM type combinations. The method further performs optimal selection of VM types and security services, with efficient schedule generation, and effectively reuses VM with reduced overall cost without delay in make span. Additionally, the method enhances security model with accurate risk estimation.