This paper presents a study of x86-64 instruction usage across 9,337 C/C++ applications and libraries in the Ubuntu 16.04 GNU/Linux distribution. We present metrics for reasoning about the relative importance of instructions weighted by the popularity of applications that contain them. From this data, we systematize and empirically ground conventional wisdom regarding the relative importance of various components of an ISA, with particular focus on building binary translation tools. We also verify the representativity of two commonly used benchmark suites, and highlight areas for improvement.

Nearly all modern software has security flaws---either known or unknown by the users. However, metrics for evaluating software security (or lack thereof) are noisy at best. Common evaluation methods include counting the past vulnerabilities of the program, or comparing the size of the Trusted Computing Base (TCB), measured in lines of code (LoC) or binary size. Other than deleting large swaths of code from project, it is difficult to assess whether a code change decreased the likelihood of a future security vulnerability. Developers need a practical, constructive way of evaluating security.

This paper presents a study of Linux API usage across all applications and libraries in the Ubuntu Linux 15.04 distribution. We propose metrics for reasoning about the importance of various system APIs, including system calls, pseudo-files, and libc functions. Our metrics are designed for evaluating the relative maturity of a prototype system or compatibility layer, and this paper focuses on compatibility with Linux applications. This study uses a combination of static analysis to understand API usage and survey data to weight the relative importance of applications to end users.

Trusted, setuid-to-root binaries have been a substantial, long-lived source of privilege escalation vulnerabilities on Unix systems. Prior work on limiting privilege escalation has only considered privilege from the perspective of the administrator, neglecting the perspective of regular users---the primary reason for having setuid-to-root binaries.

Library OSes are a promising approach for applications to efficiently obtain the benefits of virtual machines, including security isolation, host platform compatibility, and migration. Library OSes refactor a traditional OS kernel into an application library, avoiding overheads incurred by duplicate functionality. When compared to running a single application on an OS kernel in a VM, recent library OSes reduce the memory footprint by an order-of-magnitude.

An essential goal of Virtual Machine Introspection (VMI) is assuring security policy enforcement and overall functionality in the presence of an untrustworthy OS. A fundamental obstacle to this goal is the difficulty in accurately extracting semantic meaning from the hypervisor's hardware level view of a guest OS, called the semantic gap. Over the twelve years since the semantic gap was identified, immense progress has been made in developing powerful VMI tools. Unfortunately, much of this progress has been made at the cost of reintroducing trust into the guest OS, often in direct contradiction to the underlying threat model motivating the introspection. Although this choice is reasonable in some contexts and has facilitated progress, the ultimate goal of reducing the trusted computing base of software systems is best served by a fresh look at the VMI design space. This paper organizes previous work based on the essential design considerations when building a VMI system, and then explains how these design choices dictate the trust model and security properties of the overall system. The paper then observes portions of the VMI design space which have been under-explored, as well as potential adaptations of existing techniques to bridge the semantic gap without trusting the guest OS. Overall, this paper aims to create an essential checkpoint in the broader quest for meaningful trust in virtualized environments through VM introspection.

We present Purlieus, a MapReduce resource allocation system aimed at enhancing the performance of MapReduce jobs in the cloud. Purlieus provisions virtual MapReduce clusters in a locality-aware manner enabling MapReduce virtual machines (VMs) access to input data and importantly, intermediate data from local or close-by physical machines. We demonstrate how this locality-awareness during both map and reduce phases of the job not only improves runtime performance of individual jobs but also has an additional advantage of reducing network traffic generated in the cloud data center. This is accomplished using a novel coupling of, otherwise independent, data and VM placement steps. We conduct a detailed evaluation of Purlieus and demonstrate significant savings in network traffic and almost 50% reduction in job execution times for a variety of workloads.