Title: Toward an aspect-oriented cache autoloading framework with annotation

Authors: Kun Ma; Xuewei Niu; Ziqiang Yu; Ke Ji

Addresses: Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, Shandong, China ' Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, Shandong, China ' Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, Shandong, China ' Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, Shandong, China

Abstract: In recent years, researches focus on addressing the query bottleneck issue using data cache in the internet-of-things. However, the challenges of this method are how to implement autonomous management of data cache. In this paper, we propose an aspect-oriented cache autoloading framework with annotation (ACALFA). The architecture, annotation, expression are introduced to address cache autoloading. There are some features for improving performance, such as avoiding cache breakdown and cache penetration using load waiting and autoloading, loose coupling of business and cache logic using AOP, and batch delete of cache. The result of experiments indicated that our method is nearly 25% faster than other cache frameworks in case of high concurrency.

Keywords: big data; data cache; aspect-oriented programming; AOP; annotation; pointcut; grid services.

DOI: 10.1504/IJWGS.2019.100841

International Journal of Web and Grid Services, 2019 Vol.15 No.3, pp.304 - 318

Received: 27 Oct 2018
Accepted: 12 Feb 2019
Published online: 18 Jul 2019 *

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