Research: My research focuses on cloud and Big data services. My goal is to develop rigorous methodologies to ensure that next-generation cloud services are scalable, reliable, and sustainable. Research topics I actively work on include: cloud resource provisioning; cloud resource management, configuration, and auto-scaling; monitoring; DevOps.
My personal and team’s recent research has received best paper awards at ACM SIGMETRICS 2017, IEEE/IFIP IM 2017, IEEE ICCAC 2016, ACM/SPEC ICPE 2016, and IEEE CLOUD 2015. A complete list of publications can be found in my CV, on DBLP and Google scholar.
Prospective PhD/post-doc applicants: check out the openings page if you are a prospective PhD, post-doc or intern. My team offers a lively and friendly environment to carry out postgraduate research. Several departmental PhD scholarships are available for prospective PhD students, check out the application deadlines. Please contact me directly if you wish to apply for a PhD under my supervision.
Service: I am actively engaged in several international research communities, primarily in ACM SIGMETRICS, for which I am an executive board officer and an enthusiastic supporter, but also in the IFIP Working Group 7.3, and in the IEEE COMSOC management community, for which I serve as associate editor on the IEEE TNSM journal and a regular contributor to conferences in the area such as IM, NOMS, CNSM. My service record includes conference chairing for several venues in performance engineering including SIGMETRICS, MASCOTS, ICPE, QEST, VALUETOOLS, and ICAC. I am also a co-founder of the QUDOS workshop series, which focuses on quality-aware DevOps.
Projects: I currently coordinate the EU Horizon 2020 project DICE (2015-18) on Big data software engineering. My team also recently participated to the EU FP7 project MODAClouds (2012-15) on multi-cloud applications and to the ESPRC OptiMAM project on service design and business processes. I co-maintain research software projects such as LINE, KPC-Toolbox and Java Modelling Tools (JMT), a suite of applications for performance engineering based on queueing models. JMT is used in academic courses worldwide and in the IT industry.