A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments
نویسندگان
چکیده
With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network storage services. The dramatic increase in data size complexity AI computation models have put higher demands on performance computing. Rational optimal scheduling data-intensive tasks can greatly improve overall To this end, particle swarm algorithm based objective ranking is proposed to optimize task execution time cost by designing model achieve an environment. It necessary fully understand concept symmetry resource utilization indicators. method utilizes nonlinear inertia weights shrinkage factor update mechanisms optimization-seeking ability convergence speed particle-to-task solution space. are reduced. Simulation experiments conducted using Cloudsim toolkit experimentally compare TS-MOPSO with three other improvement algorithms, experimental results show that time, maximum completion total reduced 31.6%, 23.1% 16.6%, respectively. suitable handling large complex optimization efforts.
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ژورنال
عنوان ژورنال: Symmetry
سال: 2022
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym14122565