Puchen Liu

This is a comment on the paper Stoch. Dyn. 19(1) (2019) 1950004, http://doi.org/10.1142/S0219493719500047 ) titled “Averaging Principle for SDEs of Neutral Type Driven by G-Brownian Motion”, preceded by a note on the G-Itô formula. In the commented paper, the authors provided a wrong result and incorrect computations in their Theorem 4.1 and Appendix B, which probably result from the misapplication of the G-Itô Formula in the sublinear expectation theory. Hence, we provide a differential form of the G-Itô formula with a calculation table and the correct computation on the solution related to Theorem 4.1 of the commented paper.

Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficiency of concurrent editing and the satisfaction of service users under the constraint of limited memory buffer resources, the allocation of memory buffers of concurrent editing servers is transformed into the bin-packing problem, which is solved using an ant colony algorithm to achieve the least loaded utilization batch. Meanwhile, a new distributed online concurrent editing algorithm for video streams is designed for the conflict problem of large-scale video editing in an edge computing environment. It incorporates dual-buffer read-and-write technology to solve the difficult problem of concurrent inefficiency of editing and writing disks. The experimental results of the simulation show that the scheme not only achieves a good performance in the scheduling of concurrent editing but also implements the editing resource allocation function in an efficient and reasonable way. Compared with the benchmark traditional single-exclusive editing scheme, the proposed optimized scheme can simultaneously enhance editing efficiency and user satisfaction under the restriction of providing the same memory buffer computing resources. The proposed model has a wide application to video real-time processing application scenarios in edge computing.

Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent transmission method is proposed. Data Plane Development Kit (DPDK) technology supports the concurrent receiving and forwarding function of multiple Network Interface Cards (NICs). The Q-learning data stream scheduling model is proposed to solve the load scheduling of multiple queues of multiple NICs. The Central Processing Unit (CPU) transmission processing unit was dynamically selected by data stream classification, as well as a reward function, to achieve the dynamic load balancing of data stream transmission. The experiments conducted demonstrate that this method expands the bandwidth by 3.6 times over the benchmark scheme for a single network port, and reduces the average CPU load ratio by 18%. Compared to the UDP and DPDK schemes, it lowers the average system latency by 21%, reduces the data transmission packet loss rate by 0.48%, and improves the overall system transmission throughput. This transmission optimization scheme can be applied in data centers and edge computing clusters to improve the communication performance of big data processing.