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    Summary
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    This study introduces a novel ant colony system for cloud workflow scheduling, optimizing both execution time and cost. The approach effectively balances multiple objectives for improved cloud resource management.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Cloud Computing

    Background:

    • Cloud workflow scheduling faces challenges due to scale, resource elasticity, and heterogeneity.
    • Cloud pricing models necessitate optimization of both execution time and cost.

    Purpose of the Study:

    • To model cloud workflow scheduling as a multiobjective optimization problem.
    • To propose a novel multiobjective ant colony system for optimizing execution time and cost simultaneously.

    Main Methods:

    • A co-evolutionary multiple populations for multiple objectives framework with two colonies.
    • Novel pheromone update rule using a global archive of nondominated solutions.
    • Complementary heuristic and elite study strategies to enhance search and solution quality.

    Main Results:

    • The proposed algorithm demonstrates superior performance compared to state-of-the-art multiobjective and constrained optimization approaches.
    • Experimental simulations on real-world scientific workflows and Amazon EC2 properties validate the algorithm's effectiveness.

    Conclusions:

    • The novel multiobjective ant colony system offers an effective solution for complex cloud workflow scheduling.
    • The approach successfully balances execution time and cost, outperforming existing methods.