Adapting and evaluating distributed real-time and embedded systems in dynamic environments

Abstract

Quality of Service (QoS)-enabled publish/subscribe (pub/- sub) middleware provides much needed infrastructure for data dissemination in distributed real-time and embedded (DRE) systems. It is hard, however, to quantify the performance of mechanisms that support multiple interrelated QoS concerns, e.g., reliability, latency, and jitter. Moreover, once an appropriate mechanism is selected, it is hard to maintain QoS properties as the operating environment fluctuates since the chosen mechanism might no longer provide the needed QoS. For DRE systems operating in such environments, adjustments to mechanisms supporting QoS must be both timely and resilient to unforeseen environments. This paper describes our work to (1) define composite metrics to evaluate multiple interrelated QoS concerns and (2) analyze various adjustment techniques ( i.e., policy-based approaches, machine learning techniques) used for the QoS mechanisms of a DRE system in a dynamic environment. Our results show that (1) composite metrics quantify the support that mechanisms provide for multiple QoS concerns to ease mechanism evaluation and creation of related composite metrics and (2) neural network machine learning techniques provide the constant-time complexity needed for DRE pub/- sub systems to determine adjustments and the robustness to handle unknown environments.