Improving QoS Management Using Associative Memory and Event-Driven Transaction History

Abstract

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm.