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Quick Links:
Information on Call for Proposals
Proposal Matchmaking (Archive from 2020 CFP)
C3 Administration (password protected)
Have Questions? Please contact one of us:
- Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
- R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
- Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)
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Announcing our Third Call for Proposals:
AI to Transform Cybersecurity and Secure Critical Infrastructure
It is anticipated that up to USD $10 million in Research Awards will be awarded from this Call for Proposals. Proposals can request funding of USD $100,000 to $1,000,000 for an initial period of one (1) year.
C3DTI will also make available up to USD $2 million in Azure Cloud computing resources, supercomputing resources at UIUC’s NCSA and LBNL’s NERSC, and free, unlimited access to the C3 AI Suite hosted on the Microsoft Azure Cloud.
**** We will be offering a virtual session with information on the CFP including an opportunity to ask questions early in January. Please keep an eye out on you email and this wiki page for more information. ****
Proposals are Due February 7, 2022
Awards will be made in March 2022 with start dates of around June 1, 2022
Announcements
Colloquium on Digital Transformation Science
September 30, 3 pm CT
Challenges & Opportunities in Cloud Operations Research
Ishai Menache, Senior Principal Research Manager, Microsoft Research
Cloud computing is a multi-billion-dollar business that has revolutionized the way computing resources are consumed. The emergence of cloud computing is attributed to lowering the risks for end-users (e.g., scaling-out resource usage based on demand), while allowing providers to reduce costs by managing compute resources at scale. In this talk, we will provide an overview of some of the challenges faced by providers at different dimensions of cloud operations. We will highlight the role of operations research and algorithms in increasing efficiency and return on investment of cloud infrastructure. As a concrete example, we zoom in on the Virtual Machine (VM) allocation problem, one of the fundamental problems in this area. VMs need to be assigned to physical machines in a way that reduces fragmentation and efficiently utilizes available machines. Motivated by advances in machine learning that provide good estimates of workload characteristics, we consider the effect of having extra information in the form of VM lifetimes and future demand. We show that even basic information about demand (e.g., its average) leads to algorithms with significantly better guarantees.
Ishai Menache received his PhD in Electrical Engineering from the Technion, Israel Institute of Technology. He was a Postdoctoral Associate at the Laboratory for Information and Decision Systems (LIDS) at MIT. Ishai has been with Microsoft Research since 2011, where he is the founder and manager of the Cloud Operations Research (CORE) group. His research focuses on developing large-scale optimization frameworks for cloud systems and applications. More broadly, his areas of interest include systems and networking, optimization, and machine learning.Quick Links:
Information on Call for Proposals
C3 Administration (password protected)
Have Questions? Please contact one of us:
- Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
- R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
- Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)
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