The panel consisted of Mendel Rosenblum from Stanford (and VMWare, of course); Rebecca Isaacs from Microsoft Research; John Wilkes from Google; and Ion Stoica from Berkeley. The charge to the panel was to discuss the gap between academic research in cloud computing and the realities faced by industry. This came about in part because a bunch of cloud papers were submitted to HotOS from academic research groups. In some cases, the PC felt that the papers were trying to solve the wrong problems, or making incorrect assumptions about the state of cloud computing in the real world. We thought it would be interesting to hear from both academic and industry representatives about whether and how academic researchers can hope to do work on the cloud, given that there's no way for a university to build something at the scale and complexity of a real-world cloud platform. The concern is that academics will be relegated to working on little problems at the periphery, or come up with toy solutions.
The big challenge, as I see it, is how to enable academics to do interesting and relevant work on the cloud when it's nearly impossible to build up the infrastructure in a university setting. John Wilkes made the point that that he never wanted to see another paper submission showing a 10% performance improvement in Hadoop, and he's right -- this is not the right problem for academics to be working on. Not because 10% improvement is not useful, or that Hadoop is a bad platform, but because those kinds of problems are already being solved by industry. In my opinion, the best role for academia is to open up new areas and look well beyond where industry is working. But this is often at odds with the desire for academics to work on "industry relevant" problems, as well as to get funding from industry. Too often I think academics fall into the trap of working on things that might as well be done at a company.
Much of the debate at HotOS centered around the industry vs. academic divide and a fair bit of it was targeted at my previous blog posts on this topic. Timothy Roscoe argued that academia's role was to shed light on complex problems and gain understanding, not just to engineer solutions. I agree with this. Sometimes at Google, I feel that we are in such a rush to implement that we don't take the time to understand the problems deeply enough: build something that works and move onto the next problem. Of course, you have to move fast in industry. The pace is very different than academia, where a PhD student needs to spend multiple years focused on a single problem to get a dissertation written about it.
We're not there yet, but there are some efforts to open up cloud infrastructure to academic research. OpenCirrus is a testbed supported by HP, Intel, and Yahoo! with more than 10,000 cores that academics can use for systems research. Microsoft has opened up its Azure cloud platform for academic research. Only one person at HotOS raised their hand when asked if anyone was using this -- this is really unfortunate. (My theory is that academics have an allergic reaction to programming in C# and Visual Studio, which is too bad, since this is a really great platform if you can get over the toolchain.) Google is offering a billion core hours through its Exacycle program, and Amazon has a research grant program as well.
Providing infrastructure is only one part of the solution. Knowing what problems to work on is the other. Many people at HotOS bemoaned the fact that companies like Google are so secretive about what they're doing, and it's hard to learn what the "real" challenges are from the outside. My answer to this is to spend time at Google as a visiting scientist, and send your students to do internships. Even though it might not result in a publication, I can guarantee you will learn a tremendous amount about what the hard problems are in cloud computing and where the great opportunities are for academic work. (Hell, my mind was blown after my first couple of days at Google. It's like taking the red pill.)
A few things that jump to mind as ripe areas for academic research on the cloud:
- Understanding and predicting performance at scale, with uncertain workloads and frequent node failures.
- Managing workloads across multiple datacenters with widely varying capacity, occasional outages, and constrained inter-datacenter network links.
- Building failure recovery mechanisms that are robust to massive correlated outages. (This is what brought down Amazon's EC2 a few weeks ago.)
- Debugging large-scale cloud applications: tools to collect, visualize, and inspect the state of jobs running across many thousands of cores.
- Managing dependencies in a large codebase that relies upon a wide range of distributed services like Chubby and GFS.
- Handling both large-scale upgrades to computing capacity as well as large-scale outages seamlessly, without having to completely shut down your service and everything it depends on.