We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the frames along the trajectories. Each data frame contains a) RGB images generated using physically-based rendering, b) simulated depth measurements, c) simulated IMU readings and d) ground truth occupancy grid of a house. Our dataset serves a wider range of purposes compared to existing datasets and is the first large-scale benchmark focused on the mapping component of SLAM. The dataset is split into train/validation/test parts sampled from different sets of virtual houses. We present benchmarking results for both classical geometry-based [1], [2] and recent learning-based [3] SLAM algorithms, a baseline mapping method [4], semantic segmentation [5] and panoptic segmentation [6]. The dataset and source code for reproducing our experiments will be publicly available at the time of publication.

Many service applications use actors as a programming model for the middle tier, to simplify synchronization, fault-tolerance, and scalability. However, efficient operation of such actors in multiple, geographically distant datacenters is challenging, due to the very high communication latency. Caching and replication are essential to hide latency and exploit locality; but it is not a priori clear how to combine these techniques with the actor programming model. We present Geo, an open-source geo-distributed actor system that improves performance by caching actor states in one or more datacenters, yet guarantees the existence of a single latest version by virtue of a distributed cache coherence protocol. Geo's programming model supports both volatile and persistent actors, and supports updates with a choice of linearizable and eventual consistency. Our evaluation on several workloads shows substantial performance benefits, and confirms the advantage of supporting both replicated and single-instance coherence protocols as configuration choices. For example, replication can provide fast, always-available reads and updates globally, while batching of linearizable storage accesses at a single location can boost the throughput of an order processing workload by 7x.

Multi-server distributed systems are becoming increasingly popular with the emergence of cloud computing. These systems need to provide high throughput with low latency, which is a difficult task to achieve. Manual performance tuning and diagnosis of such systems, however, is hard as the amount of relevant performance diagnosis data is large. To help system developers with performance diagnosis, we have developed a tool called Performance Anomaly Detector (PAD). PAD combines user-driven navigation analysis with automatic correlation and comparative analysis techniques. The combination results in a powerful tool that can help find a number of performance anomalies. Based on our experience in applying PAD to the Orleans system, we discovered that PAD was able to reduce developer time and effort detecting anomalous performance cases and improve a developer's ability to perform deeper analysis of such behaviors.

Cloud computing is a new computing paradigm, combining diverse client devices -- PCs, smartphones, sensors, single-function, and embedded -- with computation and data storage in the cloud. As with every advance in computing, programming is a fundamental challenge, as the cloud is a concurrent, distributed system running on unreliable hardware and networks.