People

I am a Tenure-track Assistant Professor of Computer Science in the SySMA research unit at IMT School for Advanced Studies Lucca.  I was previously a SySMA Research Collaborator (2018-2021) and received my Ph.D. in Computer Science from the Gran Sasso Science Institute of L’Aquila, Italy, in 2019. I also hold a degree in Computer Engineering from the University of L’Aquila.

My research activity is focused on modeling and controlling quantitative properties, e.g., response time or throughput, of software systems that are subject to stringent quality of service (QoS) requirements (e.g., business-critical, safety-critical). In particular, most of my research efforts have been directed towards two main objectives: A) the development of efficient run-time self-adaptation techniques B) the automated derivation of analytical performance models from system execution traces.

A) In this context, I focused on techniques for controlling performance-related properties of software systems that are executed under adverse environmental conditions, e.g, sudden peak loads, hardware degradation. The main goal of such techniques is to confer to systems the ability to autonomously reconfigure themselves (e.g., improved load balancing) to prevent the raising of performance issues. I considered software systems that can be described by a well-known performance modeling notation, i.e., multi-class queuing networks (QNs). Unfortunately, the analysis of the QN’s time-dependent evolution suffers the well-known problem of state space explosion, due to the huge state space of the underlying Markov chain. To tackle this issue, we considered an approximate representation of QNs based on ordinary differential equations unleashing a range of techniques that would not be otherwise applicable. The novelty of my approach is the formulation of the performance- driven self-adaptation problem using model predictive control (MPC) and efficient mathematical optimization techniques. Recent results are summarized in a recent tutorial at ICPE’19 and in my P.h.D. thesis:”Formal Design of Performance-driven Self-adaptive Systems under Uncertainty”.

B) The automatic derivation of analytical performance models essential to promote a wider adoption of performance engineering in practice. Unfortunately, despite the importance of such techniques, the attempts pursuing that goal in the literature focus on the estimation of service demand parameters only (i.e., the computational effort needed to realize the desired functionality). I recently proposed two different approaches (based on linear programming and neural-network, respectively) that allowed to automatically derive fully specified QN models from sampled execution traces only. By using models of increasing complexity, we showed the efficiency and the effectiveness of the proposed techniques in yielding models with high prediction power when employed for evaluating the quantitative behavior of systems, under unseen configurations (i.e., not used for the learning process).

From Shell to Cloud: Theory and Practice  (October 2024) 

The course's main objective is to introduce students to the world of containerized applications and how these can be the basis for exploiting the enormous computational resources offered by the Cloud. To this end, the course will consist of an introductory overview of the development process of container-based applications in Linux environments and how to interact with them through the command line interface. Subsequently, it will introduce the concepts underlying the major Cloud providers, such as Google and Microsoft, going into detail on how to develop high-performance applications with Kubernetes technology. The course ends with an introduction to the process of writing projects to request credits to support research activities on the Cloud.

See my Google Scholar profile or my DBLP page for a full list of publications.