Cloud Task Scheduling:
Concepts, Algorithms, and Frameworks
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Cloud task scheduling is the process of assigning computational tasks to cloud resources-like virtual machines or containers-to optimize performance and meet user-defined goals such as minimizing execution time (makespan), reducing costs, and maximizing resource utilization. Given the dynamic, heterogeneous, and large-scale nature of cloud environments, task scheduling is a complex and NP-hard problem.This field has evolved from classical heuristics (e.g., FCFS, Min-Min) to advanced techniques such as metaheuristics (e.g., Genetic Algorithms, PSO, ACO) and AI-driven approaches like reinforceme...
Cloud task scheduling is the process of assigning computational tasks to cloud resources-like virtual machines or containers-to optimize performance and meet user-defined goals such as minimizing execution time (makespan), reducing costs, and maximizing resource utilization. Given the dynamic, heterogeneous, and large-scale nature of cloud environments, task scheduling is a complex and NP-hard problem.This field has evolved from classical heuristics (e.g., FCFS, Min-Min) to advanced techniques such as metaheuristics (e.g., Genetic Algorithms, PSO, ACO) and AI-driven approaches like reinforcement learning and deep learning. Real-world frameworks like Hadoop YARN, Kubernetes, and CloudSim implement these strategies to manage workloads effectively.Modern cloud scheduling emphasizes multi-objective optimization, balancing trade-offs between speed, cost, energy consumption, and fairness. Emerging trends include edge computing, serverless scheduling, and green computing, positioning task scheduling as a foundational challenge for the future of cloud services.
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