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Authored by Peng Zhang, Jules White, Douglas Schmidt, and Tom Dennis
Authored by Ted Bapty, Sandeep Neema, and Jason Scott
Authored by Akram Hakiri, Pascal Berthou, Prithviraj Patil, and Aniruddha Gokhale
Authored by Pranav Kumar, William Emfinger, and Gabor Karsai
Authored by Marco Beccani, Hakan Tunc, Addisu Taddese, Ekawahyu Susilo, P Volgyesi, Akos Ledeczi, and Pietro Valdastri
Authored by O Yapar, P Basu, Peter Volgyesi, and Akos Ledeczi
Authored by Robert Owens, Sandor Nyako, Robert Boyles, Fred Eisele, Joseph Hite, Michael Myers, Jason Scott, Ted Bapty, and Sandeep Neema
Authored by Sandeep Neema, Ted Bapty, and Daniel Balasubramanian
Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.
Authored by Shashank Shekhar, Hamzah Abdel-Aziz, Michael Walker, Faruk Caglar, Aniruddha Gokhale, and Xenofon Koutsoukos
Authored by Pranav Kumar, William Emfinger, Amogh Kulkarni, Gabor Karsai, Dexter Watkins, Benjamin Gasser, Cameron Ridgewell, and Amrutur Anilkumar
Authored by Waseem Abbas, Aron Laszka, and Xenofon Koutsoukos
Authored by Ted Bapty, Sandeep Neema, and Jason Scott
Authored by Prithviraj Patil, Aniruddha Gokhale, and Akram Hakiri
Authored by Janos Sztipanovits, Ted Bapty, Sandeep Neema, Xenofon Koutsoukos, and Jason Scott
Authored by Jason Scott, Ted Bapty, and Robert Boyles
Authored by Xenofon Koutsoukos, Ted Bapty, and Sandeep Neema
Authored by Sandeep Neema, Jason Scott, and Ted Bapty
Resiliency and reliability is of paramount impor- tance for energy cyber physical systems. Electrical protection systems including detection elements such as Distance Relays and actuation elements such as Breakers are designed to protect the system from abnormal operations and arrest failure propagation by rapidly isolating the faulty components. However, failure in the protection devices themselves can and do lead to major system events and fault cascades, often leading to blackouts. This paper augments our past work on Temporal Causal Diagrams (TCD), a modeling formalism designed to help reason about the failure progressions by (a) describing a way to generate the TCD model from the system specification, and (b) understand the system failure dynamics for TCD reasoners by configuring simulation models.
Authored by Ajay Chhokra, Abhishek Dubey, Nagahbhushan Mahadevan, and Gabor Karsai
Authored by Theodore Bapty, Sandeep Neema, Jason Scott, and Scott Eisele
Authored by Ted Bapty, Sandeep Neema, Jason Scott, and Scott Eisele
Authored by Himanshu Neema, Sandeep Neema, and Ted Bapty
Authored by Ted Bapty, Justin Knight, Zsolt Lattmann, Sandeep Neema, and Jason Scott
Authored by Zsolt Lattmann, James Klingler, Patrik Meijer, Ted Bapty, and Sandeep Neema
Authored by Zsolt Lattmann, James Klingler, Patrik Meijer, Ted Bapty, Sandeep Neema, and Jason Scott
This report (1) presents use cases and requirements for a vehicle information architecture platform (VIAP), (2) reviews and evaluates the Automotive Open System Architecture (AUTOSAR) and the Distributed Real-time Managed System (DREMS) architecture specifications, and (3) presents a preliminary architecture specification VIAP that addresses the needs of the DARPA Adaptive Vehicle Make program.
Authored by Daniel Balasubramanian and Gabor Karsai