Schedulability Analysis of Real-Time Systems Using Metaheuristic Search and Machine Learning

Schedulability analysis aims at determining whether task executions complete before their specified deadlines. It is an important activity in developing real-time systems. However, in practice, engineers have had difficulties applying existing techniques mainly because the working assumptions of existing methods are often not valid in their systems. Specifically, uncertainties in real-time systems and hybrid scheduling policies that combine standard scheduling policies have not been fully studied in the literature.

Taking SickKids Mobile

Taking SickKids Mobile is an initiative of the Hospital for Sick Children (SickKids) that explores the use of mobile technologies for enhancing patient care. It includes six sub-projects, three of which relate to improving current patient care processes and three focus on making existing services more effective and easier to use.

A Risk-Based Continuous Authentication Engine Using a Probabilistic Model around Behavioral Biometrics

Traditional static authentication systems have a fundamental deficiency; it assumes the presence of the validated user through the length of the session. Continuous authentication algorithms periodically validate the identity of a user during the entire session. It relies on information that can be automatically extracted from the user such as biometrics and behavior patterns. A probabilistic approach can naturally model the noise and latent variables present in the data. The probabilistic output of such models is a confidence value.

GPU Scheduler Modeling for Early Power-Performance Estimation of Mobile Applications

User experience and battery life are key concerns for smartphone makers. Given the growing trend of graphic-rich applications on mobile devices, embedded Graphics Processing Units (GPUs) are increasingly being incorporated in smartphone hardware platforms. In this project the intern will develop fast, early and accurate models of embedded GPUs, before the GPU hardware is available.

FlexMOSS: A Flexible Multicore Operating System Scheduler model for power efficient embedded devices

Battery life is a key concern for smartphone makers, and the power consumed by smartphone applications is directly tied to battery life. In this project the intern will develop fast, early and accurate models of smartphone power consumption, before the hardware and the operating system are available. These models will be useful for early optimization of smartphone applications and operating systems, thereby improving battery life, while meeting time-to-market constraints.