Assessing the Energy Consumption of Proactive Mobile Edge Caching in Wireless Networks

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. INDEX TERMS Wireless edge caching, energy consumption, 5G, multiaccess edge computing, proactive caching.

An Energy-Efficient Collaborative Caching Scheme for 5G Wireless Network

“…From Fig. 6, it can be seen that the overall energy consumption probability of our proposed scheme is lower then the caching schemes used in [55] and [56] and random caching for a different number of batch requests. Our proposed method is performing better than the existing schemes mentioned in Fig.…”

“…Fig. 6 shows the cumulative energy consumption of our proposed scheme compared with the caching schemes in [55] and [56] and random caching. Here, the storage capacity of cache cluster is limited to one-fifth of batch request generated by the hotspot users at a specific time period.…”

“…Furthermore, Fang et al formulated the content placement problem as a non-cooperative game, where they used the pure Nash equilibria in the non-cooperative game, and with a payment scheme the game can always implement the social optimum in the best case by giving content routers incentive to replicate. In [56], uses proactive caching algorithm which determines the prioritization of the content items and corresponding weighting factors. By using proactive caching algorithm, the authors investigated the energy consumption of edge-caching by considering different configuration such as caching size limitation, caching period and popularity (Zipf distribution).…”

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“…The optimal allocation of popular content objects at the caching nodes significantly reduces energy costs [153]- [155]. Fang et al [141] introduced a distributed caching strategy for content placement and replacement in a CCN.…”

Mobile Edge Cache Strategy Based on Neural Collaborative Filtering

“…The most obvious case in point is the popularization of smartphones. In response to so many challenges, how to efficiently lower the transmission delay and improve the network transmission quality has become an issue of great concern, and the construction and edge computing of Content Delivery Network (CDN) have provided an effective solution plan [3,4]. As a special network that is different from the traditional network, CDN relies on edge servers distributed The associate editor coordinating the review of this manuscript and approving it for publication was Dapeng Wu . in different places to realize scheduling and load balancing of the content, thus ensuring efficient delivery of the content.…”