Abstract
Multiaccess edge computing and caching (MEC) is regarded as one of the key technologies of fifth-generation (5G) radio access networks. By bringing computing and storage resources closer to the end users, MEC could help to reduce network congestion and improve user experience. However, deploying many distributed MEC servers at the edge of wireless networks is challenging not only in terms of managing resource allocation and distribution but also in regard to reducing network energy consumption. Here, we focus on the latter by assessing the network energy consumption of different cache updating and replacement algorithms. First, we introduce our proposed proactive caching (PC) algorithm for mobile edge caching with Zipf request patterns, which could potentially improve the cache hit rates compared to other caching algorithms such as least recently used, least frequently used, and popularity-based caching. Then, we present the energy assessment models for mobile edge caching by breaking down the total network energy consumption into transmission and storage energy consumption. Finally, we perform a comprehensive simulation to assess the energy consumption of the PC algorithm under different key factors and compare with that of conventional algorithms. The simulation results show that improving cache hit rates by using the PC algorithm comes at the expense of additional energy consumption for network transmission.
Highlights
Emerging technologies (e.g., virtual reality, augmented reality, three-dimensional (3D) videos/games, and autonomous driving) require high bandwidth and extremely low latency to guarantee quality-of-service (QoS), high user quality of experience (QoE) [1], [2] and safety
PROACTIVE CACHING (PC) ALGORITHM To address the technical shortcomings of least recently used (LRU) and least frequently used (LFU), i.e., both algorithms cannot predict the request rate of new online content items, and they cannot track the rapid changes in content popularity, we propose a proactive cache (PC) updating and replacing algorithm based on a prediction from big data analytics
The flexibility of the 5G network architecture has provided a platform for the deployment of multiaccess edge computing and caching (MEC) infrastructure
Summary
Emerging technologies (e.g., virtual reality, augmented reality, three-dimensional (3D) videos/games, and autonomous driving) require high bandwidth and extremely low latency to guarantee quality-of-service (QoS), high user quality of experience (QoE) [1], [2] and safety. The DU can be deployed in a macro or a small cell BS such as the micro, pico or femtocell [3], [4] This two-level network architecture allows different deployment scenarios of multiaccess edge computing and caching (MEC) servers. To address the above challenges, we first propose a proactive cache updating algorithm for MEC based on a 5G network architecture using big data analysis in our previous work [13]. To reveal the performance of different algorithms, we simulate the MEC network architecture and calculate the cache hit rate and the number of cache content items in different simulation scenarios by extending our previous work.
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