This paper introduces techniques for Deep Learning in conjunction with spiked random
neural networks that closely resemble the stochastic behaviour of biological neurons in …

Modern software systems often have to cope with uncertain operation conditions, such as
changing workloads or fluctuating interference in a wireless network. To ensure that these …

Task allocation systems in the Cloud have been recently proposed so that their performance
is optimised in real-time based on reinforcement learning with spiking Random Neural …

Failures in computer systems can be often tracked down to software anomalies of various
kinds. In many scenarios, it might be difficult, unfeasible, or unprofitable to carry out …

Microservice applications are increasingly embracing cloud platforms to run their services.
These applications can often be impacted by anomalies. Detecting anomalies at runtime is …

Various literature studies demonstrated that the cloud computing paradigm can help to
improve availability and performance of applications subject to the problem of software …

In this paper, we present a novel framework for supporting the management and
optimization of application subject to software anomalies and deployed on large scale cloud …

In order to develop an emergency response plan for contaminant flushing in drinking water
networks in intentional or accidental contamination, a decision tree-based model is …

This paper illustrates the effort to integrate a machine learning-based framework which can
predict the remaining time to failure of computing nodes with Hadoop applications. This work …

Nowadays we are witnessing multiple changes in the way data-and compute-intensive
services are offered to the users due to the influences of cloud computing, automatic …