Towards Utility Computing for the Cloud
April 20, 2023
Ion Stoica and Joseph Gonzalez receive AWS AI Amazon Research Award on “A Unified Platform for Training and Serving Large Models”
April 13, 2023
Come see SkyPilot and Skyplane at NSDI ’23!
Come see SkyPilot and Skyplane give their technical presentations at the 20th USENIX Symposium on Networked Systems Design and Implementation.
April 13, 2023
An Open-Source Chatbot impressing GPT-4 with 90% ChatGPT Quality, made by LMSYS
From our Large Model Systems Organization (LMSYS), we introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
May 19, 2023
Sky Seminar: Roxana Geambasu (Columbia University) – Managing Privacy as a Computing Resource in User-Data Workloads
In this talk, I present the perspective that user privacy should be recognized as a crucial computing resource in user-data workloads and managed accordingly. These workloads, prevalent in today’s companies, constantly compute statistics or train machine learning models on user data…
May 12, 2023
Sky Seminar: Harsha Simhadri (Microsoft Research) – DiskANN: A Library for Web-Scale Vector Search Systems
Web-scale search, recommendation and generative AI scenarios increasingly use Vector search or Approximate Nearest Neighbor Search (ANNS) indices to retrieve semantically similar objects based on the distance of their learnt representations in a geometric space….
May 5, 2023
Sky Seminar: Ashot Vardanian (Unum) – Vector Search at Scale: Bottlenecks and Solutions
Modern CLIP-like AI models allow embedding multi-modal unstructured data, such as images and texts, into shared representations in some vector space. Vector search indexes retrieve similar objects in such collections in sub-linear time….
April 28, 2023
Sky Seminar: Omar Khattab and Chris Potts (Stanford) – Demonstrate-Search-Predict: Composing Retrieval and Language Models
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM)……
April 2, 2022
New Directions in Cloud Programming
11th Conference on Innovative – 11th Conference on Innovative Data Systems Research, CIDR 2021 Data Systems Research, CIDR 2021 – Cheung, A.; Crooks, N.; Hellerstein, J. M.; and Milano, M.
April 1, 2022
Serverless Boom or Bust? An Analysis of Economic Incentives
12th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) -Charles Lin, Joseph E. Gonzalez, and Joseph M. Hellerstein.
October 26, 2021
Snoopy: Surpassing the Scalability Bottleneck of Oblivious Storage
E Dauterman, V Fang, I Demertzis, N Crooks, RA Popa Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles
Ion Stoica – Featured Projects
To comply with the increasing number of government regulations about data placement and processing, and to protect themselves against major cloud outages, many users want the ability to easily migrate their workloads between clouds. We propose doing so not by imposing uniform and comprehensive standards, but by creating a fine-grained two-sided market via intercloud brokers. SkyPilot is an intercloud broker that treats the cloud ecosystem not just as a collection of individual and largely incompatible clouds but as a more integrated Sky of Computing. SkyPilot enables users to run Machine Learning and Data Science batch jobs seamlessly on any cloud, reduce cloud costs substantially, tap into best-in-class hardware on different clouds, and enjoy higher resource availability.
Cloud applications are increasingly distributing data across multiple regions and cloud providers in response to privacy regulations, to take advantage of specialized hardware, and to prevent vendor lock-in. Unfortunately, wide-area bulk data transfers are often slow and expensive due to egress fees. This work aims to reduce both the latency and the cost of inter-cloud bulk transfer by using a variety of techniques, including overlay routing, multiple instances, multiple TCP connections, and taking advantage of different network tiers. Together, these techniques allow Skyplane to significantly improve object transfer throughput and lower the costs.
Natacha Crooks – Featured Project
Basil explores the design of SQL databases with high integrity and decentralized trust. How can traditional functionality like ACID transactions and SQL queries be efficiently implemented when trust is decentralized among n distinct parties, of which a subject can misbehave.
Joseph Gonzalez – Featured Project
We are exploring the design of feature stores: the emerging class of data systems that bridge model development, training, and inference. Features stores compute, store, and managing the data and derived features at the heart of ML powered applications. Ralf is a feature store for rapidly changing data. Ralf incrementally propagates raw data changes to derived feature tables which are queryable by downstream applications such as model training and inference.
Raluca Ada Popa – Featured Project
MC2 is a platform for running secure analytics and machine learning on encrypted data. With MC2, organizations can safely upload their confidential data to the cloud in encrypted form and securely compute analytics and machine learning without exposing the unencrypted data to the cloud provider. MC2 also enables secure collaboration among multiple organizations, where the data owners can use the platform to jointly analyze their collective data without revealing their individual data to each other.
Koushik Sen – Featured Project
FuzzFactory is domain-specific fuzz testing tool that generalizes coverage-guided fuzzing to domain-specific testing goals. FuzzFactory allows users to guide the fuzzer’s search process without having to modify the core search algorithm.
Sky Computing Story
Berkeley’s computer science division has an ongoing tradition of 5-year collaborative research labs. Recent labs included the AMPLab (ended in 2016) and the RISELab. These labs have had significant impact in both academia and industry. Past labs publish their research at top conferences in systems, databases, and machine learning. On the industrial side, AMPLab and RISELab fostered several successful startups (Databricks, Opaque, Ponder, Anyscale, to name a few). We are excited to announce the Berkeley Sky Computing Lab where we will strike to make cloud computing a true commodity.
The Sky Computing Lab represents the next chapter of data-intensive systems research at Berkeley. Recent years have seen the explosion of cloud computing. Applications are moving their data and computation to the cloud; on-premise services are dying. In doing so, companies have to make difficult choices between the myriad of cloud providers, each with different services or hardware. Lock-in, whether through artificial migration costs, legal constraints or engineering baggage is real. In the Sky Computing Lab, we will leverage distributed systems, programming languages, security, and machine learning to decouple the services that a company wants to implement from the choice of a specific cloud. Much like the Internet today, cloud computing should be an undifferentiated commodity. Applications should run seamlessly on any or multiple clouds.
Our mission in the Sky Computing Lab is to transform the cloud into an undifferentiated commodity and ease application burden. As in previous labs, we’re all in — working on everything from basic research to software development, all in the Berkeley tradition of open publication and open source software. Our founding team consists of experts in distributed systems, machine learning, security and programming languages. We’ll use this space to lay out our ideas and progress as we go.
Commitment to Diversity
Sky Computing is guided by Berkeley’s Principles of Community and is committed to providing a safe and caring research environment for every member of our community. We believe that a diverse student body, faculty, and staff are essential to the open exchange of ideas that Sky Computing Lab is founded on.
Our head is in the cloud. We are heading for the SKY.