Towards Utility Computing for the Cloud
October 19, 2022
Interview: ACM features Raluca Popa on “People of ACM Profiles”
October 5, 2022
Congrats to Shishir Patil for the POET project being featured in IEEE Spectrum!
September 29, 2022
Gateway groundbreaking brings new opportunity for computing, data science
“It’s not only about data, it’s what you’re going to do with the data,” said Stoica, adding that society is only beginning to see the impact of big data in many fields.
April 5, 2023
Sky Security Seminar: Miranda Christ (Columbia) – “Limits on revocable proof systems, with applications to stateless blockchains”
March 22, 2023
Sky Security Seminar: Alexandra Henzinger (MIT)- “One Server for the Price of Two: Simple and Fast Single-Server Private Information Retrieval”
March 10, 2023
Sky Seminar: Pankaj Mehra (Elephance Memory) – Memory-Centric System Architecture
Disaggregated Memory in the data center will solve problems of memory cost and capacity, but will introduce new challenges due to its remoteness and latency. New system foundations will be required for addressing these new challenges in the context of data-intensive …
March 8, 2023
Sky Security Seminar: Elissa Redmiles (Max Planck Institute)
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.