Created during COVID to handle video boom and sliced bandwidth costs by 30 percent
Huawei has released details of how it manages its own cloud with a dynamic traffic allocation system optimized by machine learning and developed in response to surging demand for its services during the COVID-19 pandemic.
China experienced a 47 percent annual increase in live streaming users as of June 2021, according to the authors. Companies rushed migration of digital assets, services, databases, and applications into the cloud to accommodate, and in turn, cloud service providers faced sudden escalated demand – along with higher quality expectations.and all of its challenges hit.
A traffic forecaster module in GSCO first uses machine learning techniques to estimate future requests based on historical data. It relies on machine learning methods like BHT-ARIMA, a statistical modeling technique that can take into account sudden changes, like those that can occur when there is breaking news a rush of viewers want to stream.
The monthly offline solver is formulated as a minimum-cost network flow problem linearly approximated with several combined algorithms- such as the generalized primal-dual Algorithm, the balanced augmented Lagrangian method, the extended alternating direction method of multipliers, and the network simplex method.
Next, the online solver kicks in, leverage the data from the offline solvers to create an allocation table full of probabilities that access requests from each edge region will be routed to each respective edge node by the traffic allocation actuator.“The GSCO system only needs to execute a search algorithm to obtain the corresponding probabilities from the allocation table, and then a random number generation method to finalize the allocation decision,” explained the boffins.
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