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چکیده
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Cloud computing has fundamentally transformed modern information technology by enabling on-demand access to scalable and virtualized computational resources, including processing power, storage, and software services. Despite its advantages, the dynamic, heterogeneous, and large-scale nature of cloud environments introduces significant challenges in efficient resource management, particularly in job scheduling and load balancing. These challenges become more critical when multiple conflicting objectives such as minimizing execution time, ensuring fair workload distribution, and reducing energy consumption must be addressed simultaneously. Given that cloud job scheduling is an NP-hard problem, conventional heuristic approaches often fail to provide satisfactory performance, while many existing metaheuristic methods suffer from issues such as premature convergence and insufficient balance between exploration and exploitation. In this thesis, the cloud job scheduling problem is formulated as a multi-objective combinatorial optimization problem with three primary objectives: minimizing makespan, minimizing the Degree of Imbalance (DI) as a measure of load distribution fairness, and minimizing total energy consumption. To effectively address this problem, a novel scheduling framework based on the Football Team Training Algorithm (FTTA) is proposed. Furthermore, an improved version of the FTTA algorithm is developed by incorporating a greedy initialization strategy inspired by the Min-Min algorithm, a load-aware local search mechanism to reduce workload imbalance, and a stochastic fine-tuning phase to improve exploration capability and avoid local optima. The proposed approach is specifically adapted to handle discrete task-to-virtual-machine mapping in heterogeneous cloud environments. The performance of the proposed method is evaluated through extensive simulation experiments under different scenarios, including varying workload sizes and varying numbers of virtual machines. The results demonstrate that the improved FTTA algorithm consistently outperforms baseline methods such as Random allocation as well as well-known metaheuristic approaches including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Specifically, the proposed method achieves significant improvements in reducing makespan, improving load balance (lower DI), and minimizing energy consumption. Overall, the findings confirm that the proposed framework provides an efficient, scalable, and energy-aware solution for task scheduling in cloud computing environments, offering a robust trade-off among multiple conflicting objectives.
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