Haitao Yuan

TL;DR: The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

TL;DR: In this paper, a hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSPO) was proposed to minimize the total energy consumed by mobile devices and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of mobile devices, allocated bandwidth of available channels, and transmission power of each mobile device in each time slot.

TL;DR: A novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization that can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost.

TL;DR: A novel algorithm is proposed to maximize the profit of distributed cloud and edge computing systems while meeting response time limits of tasks and realizes a larger profit than several typical offloading strategies.

TL;DR: A multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC.