MOMBI-based smart city application-oriented multi-target computing migration method and device
A multi-objective, urban technology, applied in computing, computing models, data processing applications, etc., can solve problems such as undesigned smart city scenarios, improve resource utilization and cluster load balancing, facilitate popularization and application, and execute costs low effect
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Embodiment 1
[0104] This embodiment provides a multi-objective computing migration method for smart city applications based on MOMBI. The flow chart of the method is as follows figure 1 As shown, the framework of smart city empowered by edge computing is as follows: figure 2 As shown, the calculation migration position encoding map is shown as image 3 As shown, the individual crossover process is as Figure 4 As shown, the individual variation process is as follows Figure 5 As shown, the method includes the following steps:
[0105] Step 10, according to the current network environment, read in the task data in the pre-set tasks;
[0106] Step 20. Randomly generate a parent population of size N As a set of solutions to calculate the migration strategy, where i represents the current number of iterations, the initial value of i is set to 0, and then the maximum number of iterations δ is obtained, and the mutation probability P m and the crossover probability P c (The obtained para...
Embodiment 2
[0208] In this embodiment, a multi-objective computing migration device for smart city applications based on MOMBI is provided, such as Image 6 shown, including:
[0209] Task data acquisition module, initialization module, classification module, next generation population generation module, calculation update module and population iteration module;
[0210] The task data acquisition module is used to read in task data in preset tasks according to the current network environment;
[0211] The initialization module is used to randomly generate a parent population of size N As a set of solutions to calculate the migration strategy, where i represents the current number of iterations, the initial value of i is set to 0, and then the maximum number of iterations δ is obtained, and the mutation probability P m and the crossover probability P c ;
[0212] The grading module is used to calculate the parent population The value of the objective function, the objective function...
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