CAREER: Anahita: A Resilient and Agile Fog Framework for Large-scale Disaster Incidence Response

Cuny Hunter College, New York NY

Investigators

Abstract

Fog computing is distinguished from Cloud computing by not using resources contained in large remote data centers, but instead leveraging computation, storage, and network resources that are available as close to the user as possible. These computation resources may be, for example, a part of the communications network (Edge resources) that the user is connected to, rather than using that network simply to connect to the Cloud. For rapid situational awareness to first responders during large-scale disaster incidence response, Fog computing-based response systems can be more effective than Cloud-based systems because of this proximity. However, design of effective Fog-based systems for disaster response is non-trivial as generic Fog computing methods and models assume Cloud-like environment which cannot be applied for disaster scenarios. This project proposes fundamental research, design, and development of a Fog framework that is novel in tackling the unique challenges of Fog resource management for disaster incidence response. These will benefit: 1) disaster management efforts by the first responders, 2) incident response management planning and policy makers, 3) cloud, cyber infrastructure, network management, and future Internet research communities, and 4) faculty and students in cloud and network management classrooms and labs. The educational activities will help motivate and train students at Hunter College and The City University of New York (CUNY) to pursue computer science where the student body predominantly consists of women and first-generation college students. The proposed Fog framework will be resilient against an unpredictable system environment and agile in provisioning resources for mission-critical and real-time applications. The project will use a novel sociological based organizational theory for emergency management inspired schema to characterize the fluctuations and their impact as adversarial and trust models. Using adversarial queuing theory, the project will shift the paradigm from traditional long-term cloud and edge resource optimization towards transient fog utility optimization under challenged environments. At the same time, the project will generate genetic algorithms and deep learning algorithms that aim to optimize fluctuation avoidance without compromising limited fog resources. The outcomes of such optimizations in terms of algorithms will be implemented through design and development of a software-defined fog architecture with unified resource broker services. The data generated in this project will initially be stored in the CUNY Institute CoSSMO data storage computer, and, after publication of the results, will be made available through digital repositories such as FigShare.com and drum.lib.edu. The project related algorithms and software tools will, after publication, be made available under an open source license on GitHub.com. The proposed Fog testbed will be made available to the broader cloud research community through integration with NSF supported COSMOS testbed. Published scholarly articles will be made available through web repositories such as arXiv.org. All project related resources can be access via project website:http://www.cs.hunter.cuny.edu/~S.Debroy99/projects-disaster.html. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.