Collaborative caching strategy based on optimization of latency and energy consumption in MEC

2021 ◽  
pp. 107523
Author(s):  
Chunlin Li ◽  
Yong Zhang ◽  
Qinqin Sun ◽  
Youlong Luo
2021 ◽  
Vol 69 (2) ◽  
pp. 2045-2060
Author(s):  
Fang Liu ◽  
Zhenyuan Zhang ◽  
Zunfu Wang ◽  
Yuting Xing

2019 ◽  
Vol 11 (8) ◽  
pp. 181 ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang

Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment.


2018 ◽  
Vol 189 ◽  
pp. 03018 ◽  
Author(s):  
Qing Hu ◽  
Chengming Li ◽  
Touhidul Hasan ◽  
Chengjun Li ◽  
Qingshan Jiang

Content-centric Networking (CCN) is one of the most promising network architectures for the future Internet. In-network caching is an attractive feature of CCN, however, the existing research does not consider the off-path nodes, or gives a large communication overhead for cooperation, which makes the caching utilization lower, and hard to achieve comprehensive performance optimization. To reduce the data redundancy and improve the caching utilization, we propose a Regional Hashing Collaborative Caching Strategy (RHCCS). According to calculate the importance of nodes in the network topology, we divide the network into the core area and edge area. In core area, we select the relevant nodes for cooperation, store the block in the off-path nodes with the hashing algorithm, and add a new table in original data structures for routing in the collaborative areas. As for edge area, we deploy the on-path reversion scheme. By simulating in ndnSIM and comparing with the basic caching strategy in CCN, experimental results indicate that the RHCCS can effectively reduce data redundancy, routing hops, requesting delay, and significantly increase the hit rate.


2021 ◽  
Author(s):  
Haowen Xu ◽  
Rong Chen ◽  
Mingzhi Xu ◽  
Ming Jiang ◽  
Xuming Lu

Author(s):  
Shahzeen Z. Attari ◽  
Michael L. DeKay ◽  
Cliff I. Davidson ◽  
Wandi Bruine de Bruin

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Shunquan Huang ◽  
Siqin Yu ◽  
Zhongmin Liu

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