Imagine some product team inside Google wants 100,000 CPU cores + RAM + flash + accelerators + disk in a couple of months. We need to decide where to put them, when; whether to deploy new machines, or re-purpose/reconfigure old ones; ensure we have enough power, cooling, networking, physical racks, data centers and (over longer a time-frame) wind power; cope with variances in delivery times from supply logistics hiccups; do multi-year cost-optimal placement+decisions in the face of literally thousands of different machine configurations; keep track of parts; schedule repairs, upgrades, and installations; and generally make all this happen behind the scenes at minimum cost. And then after breakfast, we get to dynamically allocate resources (on the small-minutes timescale) to the product groups that need them most urgently, accurately reflecting the cost (opex/capex) of all the machines and infrastructure we just deployed, and monitoring and controlling the datacenter power and cooling systems to achieve minimum overheads - even as we replace all of these on the fly. This talk will highlight some of the exciting problems we're working on inside Google to ensure we can supply the needs of an organization that is experiencing (literally) exponential growth in computing capacity.

Incremental view maintenance has been for a long time a central problem in database theory. Many solutions have been proposed for restricted classes of database languages, such as the relational algebra, or Datalog.  These techniques do not naturally generalize to richer languages.  In this work we give a general solution to this problem in 3 steps: (1) we describe a simple but expressive language called DBSP for describing computations over data streams; (2) we give a general algorithm for solving the incremental view maintenance problem for arbitrary DBSP programs, and (3) we show how to model many rich database query languages (including the full relational queries, grouping and aggregation, monotonic and non-monotonic recursion, and streaming aggregation) using DBSP.   As a consequence, we obtain efficient incremental view maintenance techniques for all these rich languages This is joint work with Val Tannen from University of Pennsylvania, Frank McSherry from Materialize.io

11am – 465 Soda hall and on Zoom.

Visit our friends at the Berkeley Security Lab for an exciting schedule of Seminars.

We are excited to host the following speakers for our 2022 Spring Sky Seminar Series! Click the link for the full schedule and abstracts. Deniz Altınbüken, John Ousterhout, Alex Smola, Ippokratis Pandis, Lalith Suresh, Olivier Tardieu, Philip Levis, Vincent Liu, Dahlia Malkhi, Yibo Zhu, and Dhruba Borthakur.

Dates Announced! May 25-27, 2022 at the Hyatt Regency Lake Tahoe Resort