Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

TL;DR

This paper presents a probabilistic model checking approach combined with big data tools to predict and monitor QoS breaches in service workflows, enabling proactive management and mitigation.

Contribution

It introduces a novel integration of probabilistic model checking with big data analytics for real-time QoS prediction in formal process models.

Findings

Prototype implementation demonstrates feasibility.

Case study validates prediction accuracy.

Approach enables proactive QoS management.

Abstract

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.