An adaptive strategy of online session migration for Streaming Media Edge Cloud

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

“…In the case of high load (ζ > 61), the receiving rate of user requests and the total number of migrating sessions of this method are lower than Ref. [8] algorithm and Ref. [9] algorithm.…”

“…As can be seen from Figure 7, for both low load and high load, the running time of the proposed algorithm is better than Ref. [8] algorithm and Ref. [9] algorithm, and the running time is shortened by 39.98% and 54.54% on average, respectively.…”

“…In order to better reflect the effect of algorithm optimization, under the same experimental conditions, the proposed algorithm is compared with Ref. [8] algorithm and Ref. [9] algorithm.…”

“…The parameters of the algorithm are as follows: the learning rate of policy network is 0.0001, the learning rate of Q network is 0.001, the discount coefficient is 0.95, the capacity of buffer is 1,00000, the preheating coefficient of buffer is 1,000, the number of iterations is 100,000, the upper limit of time step is 60 steps, Ref. [8] algorithm Ref. [9] algorithm The proposed method 7 Wireless Communications and Mobile Computing the number of samples for each iteration is 600, and the weight coefficient in the return function is ω 1 = 0:8, ω 2 = 0:1, ω 3 = 0:1.…”

“…Ref. [8] explored how to make the streaming media edge cloud admit more requests via online session migration and proposed an adaptive strategy of online session migration. Besides the load information, the video popularity is adopted for obtaining the allocation thresholds of different videos on each server, and a new request would be admitted under the guidance of the obtained threshold distribution.…”