RAMCloud is a new class of super-high-speed storage for large-scale datacenter applications. It is designed for applications in which a large number of servers in a datacenter need low-latency access to a large durable datastore. RAMCloud offers the following properties:
From a practical standpoint, RAMCloud enables a new class of applications that manipulate large data sets very intensively. Using RAMCloud, an application can combine tens of thousands of items of data in real time to provide instantaneous responses to user requests. Unlike traditional databases, RAMCloud scales to support very large applications, while still providing a high level of consistency. We believe that RAMCloud, or something like it, will become the primary storage system for structured data in cloud computing environments such as Amazon's AWS or Microsoft's Azure. We have built the system not as a research prototype, but as a production-quality software system, suitable for use by real applications.
RAMCloud is also interesting from a research standpoint. Its two most important attributes are latency and scale. The first goal is to provide the lowest possible end-to-end latency for applications accessing the system from within the same datacenter. We currently achieve latencies of around 5μs for reads and 15μs for writes, but hope to improve these in the future. In addition, the system must scale, since no single machine can store enough DRAM to meet the needs of large-scale applications. We have designed RAMCloud to support at least 10,000 storage servers; the system must automatically manage all the information across the servers, so that clients do not need to deal with any distributed systems issues. The combination of latency and scale has created a large number of interesting research issues, such as how to ensure data durability without sacrificing the latency of reads and writes, how to take advantage of the scale of the system to recover very quickly after crashes, how to manage storage in DRAM, and how to provide higher-level features such as secondary indexes and multiple-object transactions without sacrificing the latency or scalability of the system. Our solutions to these problems are described in a series of technical papers.
The RAMCloud project was based in the Department of Computer Science at Stanford University. The project is no longer active and the students working on RAMCloud have graduated, so we cannot provide support for anyone wishing to use RAMCloud.
General information about RAMCloud, such as talks and papers. Much of the information here is related to the research aspects of the project, as opposed to information on how to use RAMCloud.
Articles about RAMCloud (Web and print media, written by people outside the RAMCloud group)
RAMCloud has now reached a level of maturity where it is suitable for production use with real applications. The links below provide information on how to set up a RAMCloud cluster and on the RAMCloud APIs for applications.
Measurements of RAMCloud performance, as well as comparisons between RAMCloud and other systems.
Information for people who are working on the RAMCloud code base; it is intended primarily for the internal use of the RAMCloud team at Stanford, but may be useful to other people as well.
Information about the cluster we use for RAMCloud testing at Stanford. Unfortunately not all of this information is completely up to date.
These documents were used at various points in the project to record our early ideas about various parts of the system. Most of these pages are now out of date (they typically are not updated once serious coding begins) but they may still provide useful background information as well as alternatives that we considered. Entries below are in reverse chronological order (most recent design notes first).