Abstract
To meet the challenges posed by the explosive growth of mobile traffic, data caching at the network edge has been considered a key technology in future mobile networks, while the potential of device-to-device (D2D) communications in areas such as traffic offloading is also of great interest. Existing work does not have a network switching mechanism, which would ensure load balancing and improve quality of service are also ignored. Existing simulators perform poorly in terms of algorithmic compatibility, and require a high level of coding ability which are difficult to get started. In this paper, an edge simulator called SimEdgeIntel is presented for resource management that opens up detailed configuration options, enabling researchers quickly deploy mobile with edge intelligence. It supports researchers to customize the development of mobility models, caching algorithms and switching strategies. The interface-oriented system architecture helps researchers achieve cross-platform and cross-language algorithm import with machine learning techniques. In the experimental section, we perform a comprehensive evaluation of SimEdgeIntel based on real-world tracing, proving its scalability and effectiveness in terms of cache hit rate, delivery latency, and backhaul traffic, and evaluating its performance in terms of CPU and memory, respectively.
Published Version
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