Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment
Edge computing has recently emerged as an important paradigm to bring filtering, processing, and caching resources to the edge of networks. However, with the increasing popularity of augmented reality and virtual reality application, user requirements on data access speed have increased. Since the e...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2019-08-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719867864 |
| _version_ | 1797719554101608448 |
|---|---|
| author | Danyue Wang Xingshuo An Xianwei Zhou Xing Lü |
| author_facet | Danyue Wang Xingshuo An Xianwei Zhou Xing Lü |
| author_sort | Danyue Wang |
| collection | DOAJ |
| description | Edge computing has recently emerged as an important paradigm to bring filtering, processing, and caching resources to the edge of networks. However, with the increasing popularity of augmented reality and virtual reality application, user requirements on data access speed have increased. Since the edge node has limited cache space, efficient data caching model is needed to improve the performance of edge computing. We propose a multi-objective optimization data caching model in the edge computing environment using data access counts, data access frequency, and data size as optimization goals. Our model differs from previous data caching schemes that focused only on data access counts or data size. In addition, a cyclic genetic ant algorithm is proposed to solve the multi-objective optimization data caching model. We compare the following three performance indicators: cache hit ratio, average response speed, and bandwidth cost. Simulation results show that the model can improve the cache hit ratio and reduce the response latency and the bandwidth cost. |
| first_indexed | 2024-03-12T09:06:28Z |
| format | Article |
| id | doaj.art-3041a64f9e32431b89ffcd81b53faa3b |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T09:06:28Z |
| publishDate | 2019-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-3041a64f9e32431b89ffcd81b53faa3b2023-09-02T15:14:03ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772019-08-011510.1177/1550147719867864Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environmentDanyue WangXingshuo AnXianwei ZhouXing LüEdge computing has recently emerged as an important paradigm to bring filtering, processing, and caching resources to the edge of networks. However, with the increasing popularity of augmented reality and virtual reality application, user requirements on data access speed have increased. Since the edge node has limited cache space, efficient data caching model is needed to improve the performance of edge computing. We propose a multi-objective optimization data caching model in the edge computing environment using data access counts, data access frequency, and data size as optimization goals. Our model differs from previous data caching schemes that focused only on data access counts or data size. In addition, a cyclic genetic ant algorithm is proposed to solve the multi-objective optimization data caching model. We compare the following three performance indicators: cache hit ratio, average response speed, and bandwidth cost. Simulation results show that the model can improve the cache hit ratio and reduce the response latency and the bandwidth cost.https://doi.org/10.1177/1550147719867864 |
| spellingShingle | Danyue Wang Xingshuo An Xianwei Zhou Xing Lü Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment International Journal of Distributed Sensor Networks |
| title | Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| title_full | Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| title_fullStr | Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| title_full_unstemmed | Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| title_short | Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| title_sort | data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment |
| url | https://doi.org/10.1177/1550147719867864 |
| work_keys_str_mv | AT danyuewang datacacheoptimizationmodelbasedoncyclicgeneticantcolonyalgorithminedgecomputingenvironment AT xingshuoan datacacheoptimizationmodelbasedoncyclicgeneticantcolonyalgorithminedgecomputingenvironment AT xianweizhou datacacheoptimizationmodelbasedoncyclicgeneticantcolonyalgorithminedgecomputingenvironment AT xinglu datacacheoptimizationmodelbasedoncyclicgeneticantcolonyalgorithminedgecomputingenvironment |