CAREER: Timeliness as a Controllable Dimension via Knowledge-driven System Management

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The modern lifestyle is permeated and largely defined by a symbiotic interaction with computing systems. These are expected to take meaningful actions (logical correctness) at the right time (timeliness). Timeliness is crucial in safety-critical autonomous systems such as driverless vehicles and aircraft, manufacturing and power plants, and devices for intensive care and life support. Similarly, quality-of-service in enterprise and cloud systems primarily encompasses the temporal characteristics of application workloads. Unfortunately, the intense push for intelligent systems over the last decade has caused an explosion in the complexity of software and hardware layers. In turn, the sharp rise in system complexity has significantly weakened our ability to reason about, predict, and control the timeliness of application workloads. This Faculty Early Career Development (CAREER) project aims to reconcile the critical need for timeliness with the growing complexity of modern intelligent systems with an integrative research and education plan. The research focuses on turning timeliness into a dimension that can be directly and explicitly managed in high-performance platforms. It does so by recognizing the importance of gathering and acting upon precise knowledge about the low-level interplay between software and hardware components. Systemic paradigms to collect, retain, and leverage such knowledge are studied, with a specific focus on practical technology that can be immediately employed on commercial platforms. In a complementary way, the PI has devised educational activities to introduce timeliness as a fundamental design-time dimension in computer science curricula, with a number of outreach activities designed for pre-college students to explore challenges in time-sensitive systems.Through a coordinated plan to rethink the role of hardware and platform-software components, the PI will lay down the blueprints for knowledge-driven system management with explicit control over timeliness as a goal. Emerging commercial-off-the-shelf platforms that include traditional processing elements and reprogrammable logic provide a unique opportunity to practically instantiate said blueprints. Three orthogonal research thrusts comprise the pillars of this research effort. The first thrust focuses on designing and developing a set of enabling mechanisms to observe and control performance-critical hardware resources with minimal overhead. In particular, observation mechanisms are designed to collect insights into micro-scale application-platform interactions. Conversely, control mechanisms are devised to enforce fine-grained resource provisioning decisions at a micro-architectural level. The second thrust tackles the challenge of extracting knowledge from fine-grained data. This entails contextualizing the acquired data streams to construct a notion of timely application progress. Timely progress information is leveraged to detect past under-/over-provisioning and perform allocation/reclaiming accordingly. Simultaneously, strategies to accurately predict the impact of resource provisioning policies over future application progress are explored. Finally, the third thrust concerns the criticality-aware composition of multiple application workloads with specified timeliness requirements, profiled demand for hardware resources, and known temporal behavior in response to changes in resource provisioning. The significance of this project is threefold. First, direct control over the timeliness of complex applications via knowledge-driven system management provides the necessary principled foundations for data-driven real-time decision-making. Second, the methodologies and techniques developed as part of this effort will serve as a reference for future hardware platforms to achieve control over timeliness by design. Third, cross-cutting activities targeting pre-college, undergraduate, and graduate students at Boston University aim to bring temporal behavior analysis and timeliness into the foreground of computer science education.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.