|Table of Contents|

A Two-Stage Data Placement Strategy Based on Recommendation and Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年04期
Page:
80-90
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
A Two-Stage Data Placement Strategy Based on Recommendation and Learning
Author(s):
Liang Yang1; 2; Ding Changsong1; 2; Hu Zhigang3
(1.School of Informatics,Hunan University of Chinese Medicine,Changsha 410208,China)
(2.Big Data Analysis Laboratory of TCM in Hunan Province,Changsha 410208,China)
(3.School of Computer Science and Engineering,Central South University,Changsha 41
Keywords:
data placement;  quality of service;  data-intensive;  replica recommendation;  rule learning
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.04.012
Abstract:
Aiming at the problem that the unreasonable data layout leads to the decrease of service quality and the increase of operation cost in cloud-edge collaborative cluster,a two-stage data replica management mechanism based on ‘recommendation and learning'method is proposed. In view of the characteristics of data-intensive mobile applications,the optimal trade-off between data access latency and placement cost in the cloud environment is considered. The multi-objective mathematical model of replica placement is established in theory,and the decision problem is described as a bi-objective optimization problem with delay and cost constraints. In the recommendation stage,a replica recommendation engine based on motion prediction and feedback optimization is proposed to reduce the blindness of replica creation. In the learning stage,a reinforcement learning replica placement rule learning model based on Asynchronous Advantage Actor-Critic algorithm(A3C)is constructed to improve the global performance index of replica service. The experimental results show that the two-stage data placement strategy based on ‘recommendation and learning' method can reduce latency time and save cost overhead effectively and efficiently. Meanwhile,our proposed method provides an effective solution for challenges of data managemen in the real-life cloud-edge collaboration systems,which has important theoretical significance and application value.

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Last Update: 2023-12-15