Automation of application‐level caching in a seamless way

• Published in: Software, practice & experience Vol. 48; no. 6; pp. 1218 - 1237
• Main Authors: Mertz, Jhonny, Nunes, Ingrid
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.06.2018
• Subjects: adaptive systems; Applications programs; application‐level caching; Automation; cache; Caching; framework; User satisfaction; web application
• ISSN: 0038-0644; 1097-024X
• DOI: 10.1002/spe.2571

Summary Meeting performance and scalability requirements while delivering services is a critical issue in web applications. Recently, latency and cost of Internet‐based services are encouraging the use of application‐level caching to continue satisfying users' demands and improve the scalability and availability of origin servers. Application‐level caching, in which developers manually control cached content, has been adopted when traditional forms of caching are insufficient to meet such requirements. Despite its popularity, this level of caching is typically addressed in an ad hoc way, given that it depends on specific details of the application. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. As a result, application‐level caching is a time‐consuming and error‐prone task, becoming a common source of bugs. Among all the issues involved with application‐level caching, the decision of what should be cached must frequently be adjusted to cope with the application evolution and usage, making it a challenging task. In this paper, we introduce an automated caching approach to automatically identify application‐level cache content at runtime by monitoring system execution and adaptively managing caching decisions. Our approach is implemented as a framework that can be seamlessly integrated into new and existing web applications. In addition to the reduction of the effort required from developers to develop a caching solution, an empirical evaluation showed that our approach significantly speeds up and improves hit ratios with improvements ranging from 2.78% to 17.18%.