Management platform for deployment and distribution of applications in multi-cloud environmen

• Grant number: 17/01055-4
• Support Opportunities: Scholarships abroad - Research
• Effective date (Start): July 17, 2017
• Effective date (End): July 16, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Marcio Andrey Teixeira
• Grantee: Marcio Andrey Teixeira
• Host Investigator: Raj Jain
• Host Institution: Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP). Campus Catanduva. Catanduva , SP, Brazil
• Research place: Washington University in St. Louis, United States

Abstract

The development, deployment and distribution of cloud applications has attracted substantial interest from both the academic community and the software industries. The application deployments in cloud can bring benefits such as scalability of complex applications, application availability, and fault tolerance due to the high capacity of the cloud infrastructure. However, new challenges arise and one of them is to develop mechanisms that can be used to make the deployment and configuration of a cloud application to different clouds easier, since each cloud service provider has specific mechanisms to deploy the application components in Virtual machines. This research project proposes to develop the core of the management platform for deployment and distribution of applications in the multi-cloud environment. The core of the proposed platform will consist of a central component, called global controller and a number of local components, called local controllers. Through the global controller, the application service providers can specify the deployment and configuration policies for their applications. The policies will be applied across multiple clouds through local controllers that will interface with cloud management systems. The applications deployment in a cloud consists in creating several virtual functions. Based on performance parameters, the global controller will find the proper placement to deploying virtual functions among the multiple clouds. (AU)

Publicações científicas

(7)

(Referências obtidas automaticamente do Web of Science e do SciELO, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores)

ZOLANVARI, MAEDE; TEIXEIRA, MARCIO A.; JAIN, RAJ; IEEE. Analysis of AeroMACS Data Link for Unmanned Aircraft Vehicles. 2018 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), v. N/A, p. 8-pg., 2018-01-01. (17/01055-4)

ZOLANVARI, MAEDE; TEIXEIRA, MARCIO A.; JAIN, RAJ; LEE, D; SAXENA, N; KUMARAGURU, P; MEZZOUR, G. Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning. 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), v. N/A, p. 6-pg., 2018-01-01. (17/01055-4)

TEIXEIRA, MARCIO ANDREY; ZOLANVARI, MAEDE; KHAN, KHALED M.; JAIN, RAJ; MESKIN, NADER. Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach. IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS, MAY 2021. (17/01055-4)

TEIXEIRA, MARCIO ANDREY; ZOLANVARI, MAEDE; KHAN, KHALED M.; JAIN, RAJ; MESKIN, NADER. Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach. IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS, v. 6, n. 3, p. 14-pg., 2021-05-31. (17/01055-4)

DALARMELINA, NICOLE DO VALE; TEIXEIRA, MARCIO ANDREY; MENEGUETTE, I, RODOLFO. A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS. SENSORS, v. 20, n. 1, JAN 2020. (15/11536-4, 17/01055-4)

TEIXEIRA, MARCIO ANDREY; SALMAN, TARA; ZOLANVARI, MAEDE; JAIN, RAJ; MESKIN, NADER; SAMAKA, MOHAMMED. SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach. FUTURE INTERNET, v. 10, n. 8, AUG 2018. (17/01055-4)

ZOLANVARI, MAEDE; TEIXEIRA, MARCIO A.; GUPTA, LAV; KHAN, KHALED M.; JAIN, RAJ. Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE INTERNET OF THINGS JOURNAL, v. 6, n. 4, p. 6822-6834, AUG 2019. (17/01055-4)