We believe datacenters can benefit from more focus on per-node efficiency, performance, and predictability, versus the more common focus so far on scalability to a large number of nodes. Improving per-node efficiency decreases costs and fault recovery because fewer nodes are required for the same amount of work. We believe that the use of complex, general-purpose operating systems is a key contributing factor to these inefficiencies.Traditional operating system abstractions are ill-suited for high performance and parallel applications, especially on large-scale SMP and many-core architectures. We propose four key ideas that help to overcome these limitations. These ideas are built on a philosophy of exposing as much information to applications as possible and giving them the tools necessary to take advantage of that information to run more efficiently. In short, high-performance applications need to be able to peer through layers of virtualization in the software stack to optimize their behavior. We explore abstractions based on these ideas and discuss how we build them in the context of a new operating system called Akaros.

We present TaintEraser, a new tool that tracks the movement of sensitive user data as it flows through off-the-shelf applications. TaintEraser uses application-level dynamic taint analysis to let users run applications in their own environment while preventing unwanted information exposure. It is made possible by techniques we developed for accurate and efficient tainting: (1) Semantic-aware instruction-level tainting is critical to track taint accurately, without explosion or loss. (2) Function summaries provide an interface to handle taint propagation within the kernel and reduce the overhead of instruction-level tracking. (3) On-demand instrumentation enables fast loading of large applications. Together, these techniques let us analyze large, multi-threaded, networked applications in near real-time. In tests on Internet Explorer, Yahoo! Messenger, and Windows Notepad, Taint- Eraser generated no false positives and instrumented fewer than 5% of the executed instructions while precisely scrubbing user-defined sensitive data that would otherwise have been exposed to restricted output channels. Our research provides the first evidence that it is viable to track taint accurately and efficiently for real, interactive applications running on commodity hardware.