Oort, our federated learning system for scalable machine learning over millions of edge devices has received the distinguished artifact award at this year's USENIX OSDI conference!
This is a testament to a lot of hard work put in by Fan and Xiangfeng over the course of last … Continue Reading ››
Oort's working title was Kuiper.
With the wide deployment of AI/ML in our daily lives, the need for data privacy is receiving more attention in recent years. Federated Learning (FL) is an emerging sub-field of machine learning that focuses on in-situ processing of data wherever it is generated. … Continue Reading ››
Distributed data-parallel queries run on large collections of resources -- within a single datacenter in case of traditional big data queries and across multiple datacenters in case of geo-distributed analytics. The total amount of available resources to a running query changes over time for a variety of reasons, including changes in the fair share of … Continue Reading ››
Update: Camera-ready version is available here now!
With the wide adoption of distributed data-parallel applications, large-scale resource scheduling has become a constant source of innovation in recent years. There are tens of scheduling solutions that try to optimize for objectives such as user-level fairness, application-level performance, and cluster-level efficiency. However, given the well-known tradeoffs between fairness, performance, and efficiency, these solutions have traditionally focused … Continue Reading ››
Update: Camera-ready version is available here now!
In-memory caching is the de facto solution to enable low latency analytics over large datasets. While caching objects, one must be careful about maximizing the number of requests that can be served from memory in the presence of popularity skew, background load imbalance, and server failures. Traditional solutions use selective replication, i.e., … Continue Reading ››