Jorgen Thelin

Orleans: cloud computing for everyone

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. Orleans is a software framework for building reliable, scalable, and elastic cloud applications. Its programming model encourages the use of simple concurrency patterns that are easy to understand and employ correctly. It is based on distributed actor-like components called grains, which are isolated units of state and computation that communicate through asynchronous messages. Within a grain, promises are the mechanism for managing both asynchronous messages and local task-based concurrency. Isolated state and a constrained execution model allow Orleans to persist, migrate, replicate, and reconcile grain state. In addition, Orleans provides lightweight transactions that support a consistent view of state and provide a foundation for automatic error handling and failure recovery. We implemented several applications in Orleans, varying from a messaging-intensive social networking application to a data- and compute-intensive linear algebra computation. The programming model is a general one, as Orleans allows the communications to evolve dynamically at runtime. Orleans enables a developer to concentrate on application logic, while the Orleans runtime provides scalability, availability, and reliability.

PAD: Performance Anomaly Detection in Multi-server Distributed Systems

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 developers ability to perform deeper analysis of such behaviors.

Geo-distribution of actor-based services

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. Geos 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.