Hermes: a Fast, Fault-Tolerant and Linearizable Replication Protocol

Today's datacenter applications are underpinned by datastores that are responsible for providing availability, consistency, and performance. For high availability in the presence of failures, these datastores replicate data across several nodes. This is accomplished with the help of a reliable replication protocol that is responsible for maintaining the replicas strongly-consistent even when faults occur. Strong consistency is preferred to weaker consistency models that cannot guarantee an intuitive behavior for the clients. Furthermore, to accommodate high demand at real-time latencies, datastores must deliver high throughput and low latency. This work introduces Hermes, a broadcast-based reliable replication protocol for in-memory datastores that provides both high throughput and low latency by enabling local reads and fully-concurrent fast writes at all replicas. Hermes couples logical timestamps with cache-coherence-inspired invalidations to guarantee linearizability, avoid write serialization at a centralized ordering point, resolve write conflicts locally at each replica (hence ensuring that writes never abort) and provide fault-tolerance via replayable writes. Our implementation of Hermes over an RDMA-enabled reliable datastore with five replicas shows that Hermes consistently achieves higher throughput than state-of-the-art RDMA-based reliable protocols (ZAB and CRAQ) across all write ratios while also significantly reducing tail latency. At 5% writes, the tail latency of Hermes is 3.6X lower than that of CRAQ and ZAB.Comment: Accepted in ASPLOS 202

Content Conditioning and Distribution for Dynamic Virtual Worlds

Metaverses are three-dimensional virtual worlds where anyone can add and script new objects. Metaverses today, such as Second Life, are dull, lifeless, and stagnant because users can see and interact with only a tiny region around them, rather than a large and immersive world. The next-generation Sirikata metaverse server scales to support large, complex worlds, even as it allows users to see and interact with the entire world. However, enabling large worlds poses a new challenge to graphical clients to display high-quality scenes quickly over a network. Arbitrary 3D content is often not optimized for real-time rendering, limiting the ability of clients to display large scenes consisting of hundreds or thousands of objects. We present the design and implementation of Sirikata's automatic, unsupervised conversion process that transforms 3D content into a format suitable for real-time rendering while minimizing loss of quality. The resulting progressive format includes a base mesh, allowing clients to quickly display the model, and a progressive portion for streaming additional detail as desired. 3D models are large--often several megabytes in size. This poses a challenge for online, interactive virtual worlds like Sirikata, where 3D content must be downloaded on-demand. When a client enters a scene containing many objects and the models are not cached locally on the client's device, it can take a long time to download, resulting in poor visual fidelity. Deciding how to order downloads has a huge impact on performance. Should a client download a higher texture resolution for one model, or stream additional vertices for another? Worse, underpowered clients might not be able to display a high resolution mesh, resulting in wasted time downloading unneeded content. Several metrics, such as the distance and scale of an object in the scene or the camera angle of the observer, can be taken into account when designing a scheduling algorithm. We present the design and implementation of a framework for evaluating scheduling algorithms for progressive meshes and we perform this evaluation on several independent metrics and methods for combining metrics, including a linear optimization algorithm. After a thorough evaluation, our results show that a simple metric--solid angle--consistently outperforms all other metrics

Object Storage on CRAQ High-throughput chain replication for read-mostly workloads

Massive storage systems typically replicate and partition data over many potentially-faulty components to provide both reliability and scalability. Yet many commerciallydeployed systems, especially those designed for interactive use by customers, sacrifice stronger consistency properties in the desire for greater availability and higher throughput. This paper describes the design, implementation, and evaluation of CRAQ, a distributed object-storage system that challenges this inflexible tradeoff. Our basic approach, an improvement on Chain Replication, maintains strong consistency while greatly improving read throughput. By distributing load across all object replicas, CRAQ scales linearly with chain size without increasing consistency coordination. At the same time, it exposes noncommitted operations for weaker consistency guarantees when this suffices for some applications, which is especially useful under periods of high system churn. This paper explores additional design and implementation considerations for geo-replicated CRAQ storage across multiple datacenters to provide locality-optimized operations. We also discuss multi-object atomic updates and multicast optimizations for large-object updates.

UNSUPERVISED CONVERSION OF 3D MODELS FOR INTERACTIVE METAVERSES

A virtual-world environment becomes a truly engaging platform when users have the ability to insert 3D content into the world. However, arbitrary 3D content is often not optimized for real-time rendering, limiting the ability of clients to display large scenes consisting of hundreds or thousands of objects. We present the design and implementation of an automatic, unsupervised conversion process that transforms 3D content into a format suitable for real-time rendering while minimizing loss of quality. The resulting progressive format includes a base mesh, allowing clients to quickly display the model, and a progressive portion for streaming additional detail as desired. Sirikata, an open virtual world platform, has processed over 700 models using this method

JavaScript in JavaScript (js.js): Sandboxing third-party scripts

Running on billions of today’s computing devices, JavaScript has become a ubiquitous platform for deploying web applications. Unfortunately, an application developer who wishes to include a third-party script must enter into an implicit trust relationship with the thirdparty—granting it unmediated access to its entire application content. In this paper, we present js.js, a JavaScript interpreter (which runs in JavaScript) that allows an application to execute a third-party script inside a completely isolated, sandboxed environment. An application can, at runtime, create and interact with the objects, properties, and methods available from within the sandboxed environment, giving it complete control over the third-party script. js.js supports the full range of the JavaScript language, is compatible with major browsers, and is resilient to attacks from malicious scripts. We conduct a performance evaluation quantifying the overhead of using js.js and present an example of using js.js to execute Twitter’s Tweet Button API.