Scott Shenker

Organizations increasingly need to collaborate by performing a computation on their combined dataset, while keeping their data hidden from each other. Certain kinds of collaboration, such as collaborative data analytics and AI, require a level of performance beyond what current cryptographic techniques for distributed trust can provide. This is because the organizations run software in different trust domains, which can require them to communicate over WANs or the public Internet. In this paper, we explore how to instead run such applications using fast datacenter-type LANs. We show that, by carefully redesigning distributed trust frameworks for LANs, we can achieve up to order-of-magnitude better performance than naïvely using a LAN. Then, we develop deployment models for Distributed But Proximate Trust (DBPT) that allow parties to use a LAN while remaining physically and logically distinct. These developments make secure collaborative data analytics and AI significantly more practical and set new research directions for developing systems and cryptographic theory for high-performance distributed trust.

Several recently proposed techniques achieve latency reduction by trading it off for some amount of additional bandwidth usage. But how would one quantify whether the tradeoff is actually beneficial in a given system? We develop an economic cost vs. benefit analysis for answering this question. We use the analysis to derive a benchmark for wide-area client-server applications, and demonstrate how it can be applied to reason about a particular latency saving technique --- redundant DNS requests.

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100 faster than Apache Hive, and machine learning programs up to 100 faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engines provide. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.

The purpose of this paper is to propose the creation of a security-enhancing hypervisor for PCs as a collaborative agenda for the research community. This agenda is not necessarily about answering fundamentally new research questions. Rather, it is a call to action about a rare chance for the community to have substantial impact. If researchers demonstrate compelling near-term benefits from a modest security layer, then OS vendors may adopt such a layer as a way to increase security without costly reengineering. The introduction of this secure foothold into the consumer software stack could then yield significant long-term benefits by providing a much better avenue for deploying security solutions. This agenda consists of two parts: (1) exploring how hypervisors can address end-user security issues and (2) exploring how to architect a small, secure hypervisor that provides several of these facilities. We believe that there are interesting and worthwhile challenges in both parts. The rest of this paper is organized as follows. We begin by explaining why hypervisors provide a highly attractive insertion point for security (Section 2) and summarizing work in this area (Section 3). We then discuss security facilities that a hypervisor can provide in Section 4, with a focus on trusted paths to online services. We conclude by discussing challenges associated with our proposal in Section 5.

Current approaches to beach and surf zone mine clearance depend on the dispensing of large numbers of darts from a parent missile or projectile. The mine clearance mission requires a uniform distribution of darts over the target area. The primary long-term goal of the present effort is to understand and characterize, through simulation and analysis, the important physics affecting dart dispersal during large dart-pack dispense events. In this effort, the MODS (Mine Obstacle Defeat System) configuration is considered. The MIDS configuration consists of packs of darts arranged along the longitudinal axis. A current configuration under investigation consists of 9 packs, with each pack consisting of hundreds of darts mounted in a hexagonal close packing arrangement. The aggregate number of darts is beyond what can be reasonably computed using existing viscous CFD approaches. Accordingly, deriving useful data on fullsized MODS configurations requires modeling that includes the relevant geometric efforts on the flowfield without necessarily modeling every dart. The results of efforts for 2007 are presented in this document. The three main efforts were: 1) Determination of Scaling Effects on Rotating MODS Dart Pack 2) CFD/Tunnel Test Validation 3) HPC Challenge Project. All simulations used the OVERFLOW-2 code, which is the premier overset-methods Navier-Stokes code developed by NASA.