Abstract:

[Objective] The data element circulation strategy under the East-West Computing Resource Transfer” project needs to consider comprehensively various system optimization objectives, such as energy consumption, cost, and latency. This paper uses the deep reinforcement learning algorithm to implement an efficient data classification and circulation strategy. [Methods] Firstly, the data element circulation system architecture and key business logic under the “East-West Computing Resource Transfer” project are analyzed, and based on the control factors of key processes such as data classification and grading, data transmission, and data services, multiple types of business optimization goals are integrated to construct a general mathematical model for system optimization, and finally problem-solving and strategy optimization are realized through deep reinforcement learning. [Results] Through system simulation and comparison with multiple baseline algorithms, the advantages of the proposed method in convergence, long-term system benefit, and target equilibrium are verified. [Limitations] The proposed method in this paper simplifies the modeling of the relevant system. Further improvement can be achieved by combining the existing methods with practical system analysis and defining reward functions based on the actual system. [Conclusions] In complex scenarios like “East-West Computing Resource Transfer”, comprehensive consideration of multiple business optimization goals and optimizing data element circulation strategies through algorithms such as deep reinforcement learning can effectively improve the performance of the system while ensuring service quality.

Key words: East-West Computing Resource Transfer, data flow management, deep reinforcement learning