Distributed Systems
Invitation Code: RFP-24-06
Distributed computing is a foundation for modern online services and systems. As billions of devices and users generate and consume more and more data, the scale of those services and systems grows, too. In addition, the advent of new technologies such as generative AI fuels the creation of new services and creates new challenges in designing, architecting, and operating distributed systems. The purpose of this RFP is to invite research ideas and proposals for cutting-edge research in large-scale distributed systems. Specific topics of interest include but are not limited to the following:
Systems for Machine Learning
- Efficient model serving
- Highly available and scalable model serving
- Multi-agent collaboration / agentic workflow Speculative Inference
- Retrieval Augmented Generation (RAG)
- Multi-modality models
- Mixture-of-Experts (MoE) models
- Heterogenous accelerators
- Efficient model training
Observability for Distributed Systems
- Online Fault Detection/Localization
- System Characterization
- Fault Prioritization
- Root Cause Analysis
- Fault prediction and early detection
- Telemetry Data Analytics
Distributed Data Management Systems
- High-performant streaming data processing
- Time-series data analytics
- Vector database
- Data governance/provenance/lineage
- Data privacy/compliance
- Data protection and backup
- Geo-distributed data management
- Data caching
Other Systems
- AR/VR/MR systems
- Multi-Access Edge Computing (MEC)
- Cloud, datacenter, 5/6/7G networks
- Future Networking Protocols
- Decentralized Systems
Proposal Submission:
After a preliminary review, we may ask you to revise and resubmit your proposal. RFPs maybe be withdrawn as research proposals are funded, or interest in the specific topic is satisfied. Researchers should plan to submit their proposals as soon as possible.
General Requirements for Consideration, Proposal Details, FAQs
You can find the information by scrolling down to the bottom of the webpage: Research Gifts. If your questions are not answered in the FAQs, please contact research@cisco.com.
Constraints and other information
IPR will stay with the university. Cisco expects customary scholarly dissemination of results and hopes that promising results would be made available to the community without limiting licenses, royalties, or other encumbrances.