The safety of autonomous vehicles (AVs) depends on their ability to perform complex computations on high-volume sensor data in a timely manner. Their ability to run these computations with state-of-the-art models is limited by the processing power and slow update cycles of their onboard hardware. In contrast, cloud computing offers the ability to burst computation to vast amounts of the latest generation of hardware. However, accessing these cloud resources requires traversing wireless networks that are often considered to be too unreliable for real-time AV driving applications. Our work seeks to harness this unreliable cloud to enhance the accuracy of an AV's decisions, while ensuring that it can always fall back to its on-board computational capabilities. We identify three mechanisms that can be used by AVs to safely leverage the cloud for accuracy enhancements, and elaborate why current execution systems fail to enable these mechanisms. To address these limitations, we provide a system design based on the speculative execution of an AV's pipeline in the cloud, and show the efficacy of this approach in simulations of complex real-world scenarios that apply these mechanisms.

Autonomous vehicles (AVs) must drive across a variety of challenging environments that impose continuously-varying deadlines and runtime-accuracy tradeoffs on their software pipelines. A deadline-driven execution of such AV pipelines requires a new class of systems that enable the computation to maximize accuracy under dynamically-varying deadlines. Designing these systems presents interesting challenges that arise from combining ease-of-development of AV pipelines with deadline specification and enforcement mechanisms.

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code changes. To reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://github.com/erdos-project/pylot.

The gaming industry is affected by two key issues---cheating and DDoS attacks against game servers. In this paper, we aim to present a novel yet concrete application of the blockchain technology to address the seemingly disparate problems. Our approach uses blockchain to manage definitive game state and exploits peer consensus on every player action to track modifications to tangible player assets. While a key impediment to adopting blockchain for real-time systems is its high per-operation latency, our approach leverages several optimizations to enable real-time prevention of a large class of cheats where the reported client state is inconsistent with the observed state at the server. Further, blockchain-based games leverage the robust peer-to-peer architecture to successfully defend against DDoS attacks.

Like any other distributed system, cloud management stacks such as OpenStack, are susceptible to faults whose root cause is often hard to diagnose and may take hours or days to fix. We present GRETEL, a system that leverages non-intrusive system monitoring, to expedite root cause analysis of both operational and performance faults manifesting in OpenStack operations. GRETEL uses unique operational fingerprints to quickly identify faulty operations at runtime. GRETEL is accurate in its diagnosis, and achieves >98% precision in identifying the faulty operation with very few false positives and negatives even under conditions of stress. GRETEL is lightweight and orders of magnitude faster than prior work, sustaining a throughput of ~77 Mbps.

Software evolution in third-party libraries across version upgrades can result in addition of new functionalities or change in existing APIs. As a result, there is a real danger of impairment of backward compatibility. Application developers, therefore, must keep constant vigil over library enhancements to ensure application consistency, i.e., application retains its semantic behavior across library upgrades. In this paper, we present the design and implementation of POLLUX, a framework to detect application-affecting changes across two versions of the same dependent non-adversarial library binary, and provide feedback on whether the application developer should link to the newer version or not. POLLUX leverages relevant application test cases to drive execution through both versions of the concerned library binary, records all concrete effects on the environment, and compares them to determine semantic similarity across the same API invocation for the two library versions. Our evaluation with 16 popular, open-source library binaries shows that POLLUX is accurate with no false positives and works across compiler optimizations.