Thus, we introduce granular computing theory into cloud task scheduling and propose a greedy scheduling strategy based on different information granules, dividing the tasks into three types (i.e., CPU, memory, and hybrid type). Finally, we assign various scheduling strategies for ...
we introduce the basic scenario, and present the system model and problem formulation. In Section4, a collaborative task offloading and resource allocation algorithm is designed, and its mathematical analysis is provided. In Section5, we conduct simulation experiments to validate ...
Pham et al.[114]presented a taskscheduling algorithmin the Fog-Cloud environment. The proposed algorithm performs the scheduling by specifying the priorities of tasks, and determining which node to execute tasks. The obtained results show that the proposed algorithm provides an efficient balance betwee...
Azizi, S., et al.: Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: a semi-greedy approach. J. Netw. Comput. Appl. 201, 103333 (2022) Article Google Scholar Coello, C.C., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimiza...
and the offloading decision for each IoT device. Then, they determined the optimization objective of minimizing system energy consumption while ensuring that all offloading tasks were completed. And an efficient collaborative greedy task scheduling algorithm was designed to achieve the optimization objective...
have proposed a novel Multi-objective algorithm to schedule tasks on a cloud environment named MGGS (modified genetic algorithm (GA) combined with greedy strategy). The MGGS algorithm was evaluated based on total completion time, average response time, and QoS parameters. The proposed genetic ...
Greedy algorithm for scheduling Fork-Join task graphs The goal of task scheduling algorithm is to allocate the tasks of a parallel program to processors in order to minimize the com-pletion time of the program... F Yang - 《Computer Engineering & Design》 被引量: 0发表: 2008年 Feasibility...
Conflict-based strategy combined integrated optimal conflict avoidance algorithm ArticleOpen access15 February 2025 Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot ArticleOpen access02 August 2022 Global and local path planning of robots combining ACO and dynamic...
Hajam, S. S., & Sofi, S. A. (2023). Resource management in fog computing using greedy and semi-greedy spider monkey optimization.Soft Computing.https://doi.org/10.1007/s00500-023-09123-7. ArticleGoogle Scholar Khiat, A., Haddadi, M., & Bahnes, N. (2024). Genetic-based algorithm...
The result shows that it can reduce about 40% of the energy consumption of the non-power-aware data center and reduce 1.7% energy consumption of the greedy scheduling algorithm in data center scheduling area [18]. Lin et al. used TD-error reinforcement learning to reduce the energy ...