Online QoS Modeling in the Cloud: A Hybrid and Adaptive Multi-Learners Approach

TL;DR

This paper introduces a fully dynamic, hybrid multi-learner approach for online QoS modeling in cloud computing, improving accuracy and adaptability amidst fluctuating QoS and interference.

Contribution

It proposes a novel hybrid and adaptive multi-learner framework that dynamically selects the best model and partitions input space for enhanced QoS prediction accuracy.

Findings

Outperforms state-of-the-art QoS modeling approaches.

Achieves higher accuracy in dynamic cloud environments.

Effectively adapts to QoS fluctuations and interference.

Abstract

Given the on-demand nature of cloud computing, managing cloud-based services requires accurate modeling for the correlation between their Quality of Service (QoS) and cloud configurations/resources. The resulted models need to cope with the dynamic fluctuation of QoS sensitivity and interference. However, existing QoS modeling in the cloud are limited in terms of both accuracy and applicability due to their static and semi- dynamic nature. In this paper, we present a fully dynamic multi- learners approach for automated and online QoS modeling in the cloud. We contribute to a hybrid learners solution, which improves accuracy while keeping model complexity adequate. To determine the inputs of QoS model at runtime, we partition the inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques, and we then combine the sub-spaces results. The learners are also adaptive; they simultaneously allow several machine learning algorithms to model QoS function and dynamically select the best model for prediction on the fly. We experimentally evaluate our models using RUBiS benchmark and realistic FIFA 98 workload. The results show that our multi-learners approach is more accurate and effective in contrast to the other state-of-the-art approaches.