We present VC3, the first system that allows users to run distributed MapReduce computations in the cloud while keeping their code and data secret, and ensuring the correctness and completeness of their results. VC3 runs on unmodified Hadoop, but crucially keeps Hadoop, the operating system and the hyper visor out of the TCB, thus, confidentiality and integrity are preserved even if these large components are compromised. VC3 relies on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running within isolated regions, to prevent attacks due to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and 8% with read/write integrity.

Fay is a flexible platform for the efficient collection, processing, and analysis of software execution traces. Fay provides dynamic tracing through use of runtime instrumentation and distributed aggregation within machines and across clusters. At the lowest level, Fay can be safely extended with new tracing primitives, including even untrusted, fully optimized machine code, and Fay can be applied to running user-mode or kernel-mode software without compromising system stability. At the highest level, Fay provides a unified, declarative means of specifying what events to trace, as well as the aggregation, processing, and analysis of those events. We have implemented the Fay tracing platform for Windows and integrated it with two powerful, expressive systems for distributed programming. Our implementation is easy to use, can be applied to unmodified production systems, and provides primitives that allow the overhead of tracing to be greatly reduced, compared to previous dynamic tracing platforms. To show the generality of Fay tracing, we reimplement, in experiments, a range of tracing strategies and several custom mechanisms from existing tracing frameworks. Fay shows that modern techniques for high-level querying and data-parallel processing of disagreggated data streams are well suited to comprehensive monitoring of software execution in distributed systems. Revisiting a lesson from the late 1960s [Deutsch and Grant 1971], Fay also demonstrates the efficiency and extensibility benefits of using safe, statically verified machine code as the basis for low-level execution tracing. Finally, Fay establishes that, by automatically deriving optimized query plans and code for safe extensions, the expressiveness and performance of high-level tracing queries can equal or even surpass that of specialized monitoring tools.