Explosive changes in applications have been observed with influx of machine intelligence (AI) into systems with critical resilience requirements. This new generation of systems while managed by AI, also rely on conventional fault models that rely on traditional methods of redundancy, coding, checkpointing, and storage management. This course will address both classical and AI-centric fault tolerance techniques via three applications with significant societal impact.
Autonomous Vehicles: Autonomy is already a part of modern vehicles, and increasingly newer methods are being adopted which has lead to significant concerns on safety.
Hybrid Cloud Infrastructure: The performance and resilience of the cloud systems is rapidly being automated, driving new fault models and recovery methods tied to performance and resilience objectives referred to as service level metrics.
Resource Disaggregation: Disaggregation is the latest emerging paradigm in datacenters where resources such as computing, storage, and memory are decoupled from their physical limits; thus allowing for dynamic allocation based on real-time demands and workloads. Resource disaggregation introduces new fault models due to communication between traditionally colocated components.
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|Ravishankar K. Iyer
|Office Hours: 255 Coordinated Science Lab; 10:00am - 11:00am Monday
|Office Hours: 245 Coordinated Science Lab; 10:00am - 11:00am Wednesday
The University of Illinois at Urbana-Champaign Student Code should also be considered as a part of this syllabus. Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the Code at the following URL: http://studentcode.illinois.edu/.
Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy: http://studentcode.illinois.edu/. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. Do not hesitate to ask the instructor(s) if you are ever in doubt about what constitutes plagiarism, cheating, or any other breach of academic integrity.