Abstract
Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches.
Highlights
We (1) overfit an artificial neural network (ANN) [14] to retain as much information about specific environment configurations and adjustments as possible, (2) integrate nonoverfitted artificial neural networks (ANNs) and support vector machines (SVMs) [15] to provide low response times and high accuracy for environments unknown until runtime, and (3) evaluate the machine learning techniques using the AccuLate metric that quantitatively combines accuracy and latency
We provided the expected output to the ANNs and SVMs which is the transport protocol that provided the best QoS with respect to data reliability, average latency, and jitter
The Dynamic Control of Behavior based on Learning (DCBL) middleware developed by Vienne and Sourrouille [29] incorporates reinforcement machine learning in support of autonomic control for QoS management
Summary
Supervised machine learning techniques use training data to guide learning [9] These techniques can provide constant time complexity along with perfect accuracy in determining appropriate adaptations for environments on which they have been trained (i.e., known a priori). We (1) overfit an artificial neural network (ANN) [14] (which is a technique modeled on interactions of neurons in the human brain) to retain as much information about specific environment configurations and adjustments as possible (e.g., greatly increasing the number of connections between input environment characteristics and output adjustments used in an ANN), (2) integrate nonoverfitted ANNs and support vector machines (SVMs) [15] (which generate the boundaries between different groupings to maximize the differences between groupings and aid in classification) to provide low response times and high accuracy for environments unknown until runtime, and (3) evaluate the machine learning techniques using the AccuLate metric that quantitatively combines accuracy and latency.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
