Michele Ciavotta

Michele Ciavotta

Personal Information

Assistant professor – non-tenure

National Academic Qualifications (ASN II fascia), SSD:

  • 01/A6 – Ricerca Operativa – 2017
  • 01/B1  – Informatica – 2018
  • 09/H1 – Sistemi Di Elaborazione delle Informazioni -2018

University of Milano-Bicocca
Department of Informatics, Systems and Communication

Viale Sarca 336 building U14 20126 Milano, Italy

Room: 2002

Tel: +39 02 6448 7846

e-mail: michele.ciavotta<at>unimib<dot>it

DBLP

Google Scholar

Researchgate

Curriculum Vitae (English)

Research Interests

My research work focus on modeling complex real-life problems mainly arising in the fields of scheduling and production planning and designing and prototyping single as well as multi-objectives techniques to solve them. In the years I have dealt with the optimization of complex human-machine environments, Logistics, Production scheduling and Vehicle Routing problems using exact methods and metaheuristic approaches as bio-inspired algorithms and randomized local search.

Lately, my interests have turned to Knowledge representation, Complex Software Design,  Model-Driven Design, Resource Management of Cloud based and Big Data applications, and Real-to-digital synchronization in Industry 4.0.

 

Relevant Publications

E. Gianniti, M. Ciavotta, D. Ardagna. Optimizing Quality-Aware Big Data Applications in the Cloud. To appear in IEEE Transactions on Cloud Computing, 2018. 

D. Ardagna, M. Ciavotta, R. Lancellotti, M. Guerriero. A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications. To appear in IEEE Transactions on Cloud Computing, 2018. 

M. Malekimajd, D. Ardagna, M Ciavotta, E. Gianniti, M. Passacantando, A. M. Rizzi. An Optimization Framework for the Capacity Allocation and Admission Control of MapReduce Jobs in Cloud Systems. To appear in the Journal of Supercomputing, 2018.

M. Ciavotta, D. Ardagna, G. P. Gibilisco. A mixed integer linear programming optimization approach for multi-cloud capacity allocationJournal of Systems and Software, 2017, 123, 64–78.

A. Evangelinou , M. Ciavotta, D. Ardagna , A. Kopaneli, G.Kousiouris, T. Varvarigou. Enterprise applications cloud rightsizing through a joint benchmarking and optimization approachFuture Generation Computer Systems, 2018, 78, 102-114.

M. Ciavotta, C. Meloni, M. Pranzo. Speeding up a Rollout algorithm for complex parallel machine schedulingInternational Journal of Production Research, 2016, 54, 4993–5009.

D. Ardagna, M. Ciavotta, M. Passacantando. Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems.  Transactions on Services Computing, 2015, 10, 381-395.

M. Mazzara and M. Ciavotta. Issues about the Adoption of Formal Methods for Dependable Composition of Web Services. International Journal of Systems and Service-Oriented Engineering, 2015, 4, 35-50.

M. Malekimajd, D. Ardagna, M. Ciavotta, A. M. Rizzi, M. Passacantando. Optimal Map Reduce Job Capacity Allocation in Cloud Systems. ACM SIGMETRICS Performance Evaluation Review, 2015, 42, 50-60.

D. Ardagna, G. Casale, M. Ciavotta, J. F. Perez, W. Wang. Quality-of-Service in Cloud computing: modeling techniques and their applications. Journal of Internet Services and Applications, 2014, 5, 1-17.

M. Ciavotta, P. Detti, C. Meloni, M. Pranzo. A bi-objective coordination setup problem in a two-stage production system. European Journal of Operational Research, 2008, 189, 734-45.

G. Minella, R. Ruiz, M. Ciavotta. A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS Journal on Computing, 2008, 20, 451-471.

M. Ciavotta, C. Meloni, M. Pranzo. Scheduling dispensing and counting in secondary pharmaceutical manufacturing. AIChE Journal, 2009, 55, 1161-1170.

G. Minella, R. Ruiz, M. Ciavotta. Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers & Operations Research, 2011, 38, 1521-1533.

M. Ciavotta, C. Meloni, M. Pranzo. Minimising general setup costs in a two-stage production system. International Journal of Production Research, 2012, 51, 2268–2280.

M. Ciavotta, G. Minella, R. Ruiz. Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research, 2013, 227, 301-313.

Latest conference papers

Vincenzo Cutrona, Federico Bianchi, Michele Ciavotta and Andrea Maurino. On the Composition and Recommendation of Multi-Feature Paths: a Comprehensive Approach.  Mobility Analytics for Spatio-temporal and Social Data (Mates 2018), Aug 27 – 31, 2018, Rio de Janeiro, Brazil

M. Ciavotta, A. Bettoni and G. Izzo. Interoperable Meta-Model for Simulation in the Loop1st IEEE International Conference on Industrial Cyber-Physical Systems (ICPS-2018), May 15-18, 2018, Saint-Petersburg, Russia.

M. Ciavotta, S. Krstić and D. A. Tamburri. HyperSpark: A Software Engineering Approach to Parallel Metaheuristics12th Metaheuristics International Conference (MIC’2017), July, 2017, Barcelona, Spain.

M. Ciavotta, M. Alge, D. Rovere, P. Pedrazzoli. A microservice-based middleware for the digital factory. 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2017). June, 2017, Modena, Italy.

E. Gianniti, D. Ardagna, M. Ciavotta, M. Passacantando. A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce. 17TH IEEE/ACM International Symposium On Cluster, Cloud And Grid (CCGRID 2017) May, 2017, Madrid, Spain.

M. Ciavotta, E. Gianniti, D. Ardagna. Capacity Allocation for Big Data Applications in the Cloud. 8th ACM/SPEC on International Conference on Performance Engineering Companion (ICPE 2017), April, 2017, L’Aquila, Italy.

Current Projects

EW-Shopp: Supporting Event and Weather-based Data Analytics and Marketing along the Shopper Journey, H2020 EU project

Previous Projects

Far-Edge: Factory Automation Edge Computing Operating System Reference Implementation, H2020 EU Project.

EuBra-BigSea: Europe-Brazil Collaboration of BIG Data Scientific Research through Cloud-Centric Applications, H2020 EU Project.

MAYA: Multi-disciplinary integrated simulation and forecasting tools, empowered by digital continuity and continuous real-world integration, H2020 EU Project.

DICE: Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements, H2020 EU Project.

MODAClouds: MOdel-Driven Approach for design and execution of applications on multiple Clouds, FP7 EU Project.