Sky Computing
Towards Utility Computing for the Cloud
Sponsors
NEWS
Ion Stoica to deliver Keynote at Data @Scale 2022
May 18, 2022
Data @Scale is a technical conference for engineers who are interested in building, operating, and using data systems at scale.
Sky Computing, the Next Era After Cloud Computing
April 13, 2022
Ion and Scott featured in THENEWSTACK
From Cloud Computing to Sky Computing: SIGOPS
April 13, 2022
We consider the future of cloud computing and ask how we might guide it towards a more coherent service we call sky computing. The barriers are more economic than technical,
EVENTS
Sky Computing Summer 2022 Retreat
May 25, 2022
Dates Announced! May 25-27, 2022 at the Hyatt Regency Lake Tahoe Resort
Sky Seminar: John Ousterhout – The Story of Raft
April 12, 2022
“In this talk I will discuss the back-story behind the Raft consensus algorithm: why we decided to undertake this project, how the algorithm developed, and the challenges of publishing an idea that “”gores a sacred cow””. I will also make several observations about how to perform research, how program committees work, and the relationship between Paxos and Raft.”
Systems Reading Group
April 21, 2022
PUBLICATIONS
Serverless Boom or Bust? An Analysis of Economic Incentives
April 1, 2022
12th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) -Charles Lin, Joseph E. Gonzalez, and Joseph M. Hellerstein.
New Directions in Cloud Programming
April 2, 2022
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.
CostCO: An automatic cost modeling framework for secure multi-party computation
April 3, 2022
Vivian Fang, Lloyd Brown, William Lin, Wenting Zheng, Aurojit Panda, Raluca Ada Popa
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 Rise Lab. 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.
Context
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.
Mission
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.
Natacha Crooks – Featured Project
Basil
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
RALF
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.
Raluca Ada Popa – Featured Project
MC2
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.
Ion Stoica – Featured Project
SKYPLANE
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.
Koushik Sen – Featured Project
FuzzFactory
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.