Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

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.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model Checking, SLA compliance monitorin

SLA compliance monitoring through semantic processing

For IT-services providers, user satisfaction is the key for their company's success. Service providers need to understand the requirements of their users and translate them into their own business goals. Service malfunctions could have negative impact on user satisfaction, therefore to detect and resolve failures of the business process level has become a mission critical requirement for any IT-company. Unfortunately, even if a failure manifests itself at the business level, the data describing this failure are scattered into low level components of the system and stored with a formalism incomprehensible to any business analyst. In forensic analysis, the semantic gap between collected data and business analysts' knowledge is closed by the adoption of data-mining and data-warehousing techniques, but such techniques are unsuitable for real-time business process analysis due to their long latencies. The purpose of this paper is to present a framework that allows business process analysts investigating the delivery status of business services in near real-time. The framework requires a first set up phase where domain specialists define ontologies describing low level concepts and the mapping among business events and data gathered into the system, and then it provides business process analysts, aware only of business logics, with a way to investigate service delivery status in near real time. The capability of the framework of processing data in near real time is ensured by the use of emerging technologies such as complex event processing (CEP) engines, which are able to process in real time huge amount of data. Furthermore in the paper, it is also showed a case study from the telecommunication industry aiming to demonstrate the applicability of the framework in a real word scenario