Improving Makespan and Resource Utilization in Cloud Workflow Scheduling: A CSA-POAM Approach

Workflow scheduling in cloud computing is crucial for efficient resource management and task execution, yet existing algorithms often struggle to balance exploration and exploitation effectively. This research addresses this challenge by proposing a novel hybrid algorithm, termed CSA-POAM, which combines the Crow Search Algorithm (CSA) and the Penguin Optimization Algorithm (POA) with Adaptive Memory Management (AMM). The CSA-POAM hybrid dynamically adjusts memory size to maintain a diverse pool of high-quality solutions, enhancing scheduling efficiency. Through extensive simulations, the hybrid algorithm demonstrates superior performance compared to the original CSA and POA, achieving improved makespan, cost reduction, and optimized resource utilization. The adaptive memory management strategy enables CSA-POAM to achieve a better balance between exploration and exploitation, highlighting its potential as a robust solution for workflow scheduling in cloud computing environments.

Keywords:

Cloud Computing , Penguin Algorithm , Crow Search , Algorithm , Workflow Scheduling , Hybrid Optimization