Towards Utility Computing for the Cloud
February 20, 2024
Prof. Matei Zaharia and his students and collaborators talk about compound AI systems and their research on them
AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. This naturally led to an intense focus on models as the primary ingredient in AI application development, with everyone wondering what capabilities new LLMs will bring. As more developers begin to build using LLMs, however, we believe that this focus is rapidly changing: state-of-the-art AI results are increasingly obtained by compound systems with multiple components, not just monolithic models.
February 7, 2024
Ion Stoica elected into the National Academy of Engineering
The National Academy of Engineering (NAE) announced today that three UC Berkeley faculty members — Arpad Horvath, Ravi Prasher and Ion Stoica — have been elected to its ranks.
February 6, 2024
Salk Institute scientists scale brain research on Google Cloud with SkyPilot
The cloud, with its large numbers of the latest processors and scalable storage systems, is becoming indispensable to modern biomedical research organizations, who use it to generate and analyze vast amounts of data. However, research by its very nature is not a linear process, and so cloud resources must be flexible enough to rapidly scale up and down in response to changing demands. Additionally, research always has limited funding, and so cost- and time-efficiency are critical to achieving research insights.
October 23, 2023
Professors Alvin Cheung, Joseph Gonzalez, and Joseph Hellerstein win awards at VLDB Conference 2023
CS Associate Professor Alvin Cheung has won the 2023 Very Large Data Bases (VLDB) Early Career Research Contribution Award. The award, which includes a $2,000 prize, recognizes researchers who have made a significant impact through a specific contribution to the field since completing their Ph.D.
August 21, 2023
Come see us at SOSP ’23!
Accepted papers by Micah Murray, Matei Zaharia, Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Lianmin Zheng, Joseph Gonzalez, Hao Zhang, Ion Stoica, and Emma Dauterman at the 29th ACM Symposium on Operating Systems Principles.
February 23, 2024
Sky Seminar Series: Fatma Ozcan (Google) – ML, LLMs and Data Management
In this talk, we discuss using ML and LLMs for data management problems. In the first part, we review natural languages interfaces to data, and how LLMs are fueling new interest and solutions in this space. After reviewing some of the existing issues, we argue that we need semantic data models and…
February 16, 2024
Sky Seminar Series: Raul Fernandez (UChicago) – On Data Ecology, Data Markets, the Value of Data, and Dataflow Governance
Data shapes our social, economic, cultural, and technological environments. Data is valuable, so people seek it, inducing data to flow. The resulting dataflows distribute data and thus value. For example, large Internet companies profit from accessing data from their users, and engineers of large la…
February 9, 2024
Sky Seminar Series: Atul Adya (Databricks) – Pulling distributed caches out of the Dark Ages
Caching is a fundamental building block in computer systems. When it comes to distributed caching in data centers, however, it is far from a solved problem. Even the most advanced systems today suffer from a myriad of issues including persistent hotspotting due to popular keys, unavailability and st…
February 2, 2024
Sky Seminar Series: Petros Maniatis (Google) – Large Sequence Models for Software Development Activities
Large language models for software are increasingly moving beyond code completion to the full range of software development activities that engineers engage in day-to-day. I will present an overview of Google’s efforts to build large foundation models trained on Google’s own internal software-engine…
Fuzzing, Symbolic Execution, and Expert Guidance for Better Testing.
Fairness in Serving Large Language Models.
CodeScholar: Growing Idiomatic Code Examples.
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines.
Picsou: Enabling Efficient Cross-Consensus Communication.
Leveraging Cloud Computing to Make Autonomous Vehicles Safer.
See, Say, and Segment: Teaching LMMs to Overcome False Premises.
Efficiently Programming Large Language Models using SGLang.
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
The Gorilla project is designed to connect large language models (LLMs) with a wide range of services and applications exposed through APIs. Imagine if ChatGPT could interact with thousands of services, ranging from Instagram and Doordash to tools like Google Calendar and Stripe, to help you accomplish tasks. This may be how we interact with computers and even the web in the future. Gorilla is an LLM that we train using a concept we call retriever-aware training (RAT), which picks the right API to perform a task that a user can specify in natural language. Gorilla also introduces an Abstract Syntax Tree (AST) based sub-tree matching algorithm, which for the first time allows us to measure hallucination of LLMs!
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.