GHCC: Grouping-based and hierarchical collaborative caching for mobile edge computing

Ren, Dewang; Gui, Xiaolin; Wei, Lu; An, Jian; Dai, Huijun; Liang, Xin

doi:10.23919/wiopt.2018.8362881

Cited by 17 publications

(2 citation statements)

References 16 publications

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“…Huang [5] et al developed a MEC edge cache structure and proposed a cooperative multicast-aware cache test to reduce the average delay caused by the delivery of cached content, and proved the effectiveness of this multicast model. Ren D [6] and others proposed a hierarchical collaborative caching strategy based on grouping. The utilization of grouping is to use the fuzzy C-means clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Edge Caching Strategy Based on User Preference and Game Theory

2021

AJCIS

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Caching video content to the mobile edge server is an effective solution to avoid multiple repeated requests from mobile terminal devices, reduce latency costs, and improve user QoE. In addition, mobile users in nearby areas tend to request the same video resource task, so reasonable deployment of video content to edge servers can effectively reduce the response time of user requests. This fact prompted us to design a caching strategy based on user preferences. The system model considered in this article contains multiple mobile users, multiple servers, and remote central servers. Based on the recommendation system predicting the user's preference for specific video resources, the recommended value ranking of the video resources in the future time period is obtained, and then based on the game theory method, in each edge cache server, each video resource is calculated for the local area and neighboring areas. The cache value of the area, based on the cache value of the video resource to be cached, minimizes the delay for users to obtain the video resource, and obtains the optimal video resource cache distribution strategy. The simulation experiment results show that compared with other caching strategies, the proposed caching strategy is better than other caching strategies in terms of response time and cache task hit rate.

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Section: Related Workmentioning

confidence: 99%

Edge Caching Strategy Based on User Preference and Game Theory

2021

AJCIS

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“…Liao J et al [21] studied the optimization of content cache placement in which file and cache sizes are different and multicast transmission is used to minimize the average return rate. Ren D et al [22] proposed a group based cache strategy which considers the allocation of storage resources to reduce the average delay and total energy consumption of the content. Wei J et al [23] studied the cooperation scheme between multi access edge computing (MEC) servers to optimize the performance of content caching and delivery between MEC and mobile devices.…”

Section: Cache Placement To Reduce Latencymentioning

confidence: 99%

A cache placement algorithm based on comprehensive utility in big data multi-access edge computing

2021

KSII TIIS

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The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

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