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To meet energy and quality of service (QoS) constraints in consumer/user-based embedded devices (CEDs), configurable caches can be tuned to a best configuration that consumes the least amount of energy while adhering to QoS expectations. However, due to disparate consumer QoS expectations and a myriad of unknown, third-party CED applications, tuning caches in CEDs is very challenging. In this paper, we propose a quality of service-aware, scalable tuning algorithm for configurable caches, which requires no a priori knowledge of applications or design-time efforts.

Online video streaming has gained ubiquity in disparate educational, governmental, and corporate environments. The ubiquity of these videos elicits new challenges in video classification that is used to promote relative videos and block unwanted content. These challenges include the incorporation of contextual information, rapid development of the ad-hoc query modules, and keeping pace with contemporary contextual information. In this article, the authors present a framework model for incorporating contextual information into video classification information. To illustrate the model, the authors propose a framework which comprises video classification, web search engines, social media platforms, and third-party classification modules. The modules enable the framework's flexibility and adaptivity to different contextual environment—educational, governmental, and corporate. Additionally, the model emphasizes standardized module interface to enable the framework's extensibility and rapid development of future modules.

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