Big Data Analytics for QoS Prediction Through Probabilistic Model Checking
This addresses the need for businesses to anticipate and avoid QoS breaches to maintain competitiveness, though it appears incremental as it combines existing model checking with big data analytics.
The paper tackles the problem of predicting Quality of Service (QoS) breaches in service workflows to enable proactive mitigation, proposing a model checking approach that uses big data tools for continuous parameter updates and demonstrating it with a prototype and case study.
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.