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%.

Pervasive computing allows users to benefit from information and services provided by advanced applications using smart and interconnected devices. Given the inherent complexity of this software environment, platforms become a typical means of easing the development and execution of pervasive applications. These platforms are usually in charge of managing the application context, ensuring that data are reliable and available to applications. Most platforms provide this feature, making real-time information available, even when applications do not need such information in real-time. This occurs, for example, when sensors continuously provide measurements to in-house temperature controllers and these measurements have limited time variance. Therefore, controllers can be updated only occasionally, reducing application overload without compromising effectiveness. To address this issue, in this paper, we present a pervasive platform, iCasa, extended with an autonomic manager of service-oriented context module. Our solution consists of tracking application needs and context information to achieve the best caching configuration, regarding a multi-application pervasive environment. This caching configuration can decrease the power consumption of devices due to the reduced unnecessary network communications between platform and devices.

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. Despite its popularity, this level of caching involves the manual implementation by developers and is typically addressed in an ad-hoc way, given that it depends on specific details of the application. As a result, application-level caching is a time-consuming and error-prone task, becoming a common source of bugs. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. In this paper, we present the results of a qualitative study of how developers handle caching logic in their web applications, which involved the investigation of ten software projects with different characteristics. The study we designed is based on comparative and interactive principles of grounded theory, and the analysis of our data allowed us to extract and understand how developers address cache-related concerns to improve performance and scalability of their web applications. Based on our analysis, we derived guidelines and patterns, which guide developers while designing, implementing and maintaining application-level caching, thus supporting developers in this challenging task that is crucial for enterprise web applications.