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.

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.