We present LittleTable, a relational database that Cisco Meraki has used since 2008 to store usage statistics, event logs, and other time-series data from our customers' devices.

Large-scale distributed systems are hard to deploy, and distributed hash tables (DHTs) are no exception. To lower the barriers facing DHT-based applications, we have created a public DHT service called OpenDHT. Designing a DHT that can be widely shared, both among mutually untrusting clients and among a variety of applications, poses two distinct challenges. First, there must be adequate control over storage allocation so that greedy or malicious clients do not use more than their fair share. Second, the interface to the DHT should make it easy to write simple clients, yet be sufficiently general to meet a broad spectrum of application requirements. In this paper we describe our solutions to these design challenges. We also report our early deployment experience with OpenDHT and describe the variety of applications already using the system.

There has been a steady stream of research over the years into componentized network protocols: protocol implementations assembled from a variety of building blocks. A promise of such frameworks has generally been flexibility: a protocol stack tailored for a particular application can be easily assembled, usually without writing any new code, by binding protocol objects together.

We are heading for a device information disaster. Many people already store information on dozens of devices, but are unable to organize and find their scattered information effectively. A given picture might be on a digital camera, a home PC, a laptop, an iPod Photo, or a cell phone. Even knowing a file's location is often not enough, because the relevant device may be on the far side of a firewall or not currently plugged into a PC's USB port. Sharing pictures, documents, or multimedia with family, friends, and colleagues today requires one to upload the information from a portable device to a personal computer and then E-mail it, or copy files via a physical medium such as a USB key.

We are developing a distributed query processor called PIER, which is designed to run on the scale of the entire Internet. PIER utilizes a Distributed Hash Table (DHT) as its communication substrate in order to achieve scalability, reliability, decentralized control, and load balancing. PIER enhances DHTs with declarative and algebraic query interfaces, and underneath those interfaces implements multihop, in-network versions of joins, aggregation, recursion, and query/result dissemination. PIER is currently being used for diverse applications, including network monitoring, keyword-based filesharing search, and network topology mapping. We will demonstrate PIER's functionality by showing system monitoring queries running on PlanetLab, a testbed of over 300 machines distributed across the globe.

We present Tapestry, a peer-to-peer overlay routing infrastructure offering efficient, scalable, location-independent routing of messages directly to nearby copies of an object or service using only localized resources. Tapestry supports a generic decentralized object location and routing applications programming interface using a self-repairing, soft-state-based routing layer. The paper presents the Tapestry architecture, algorithms, and implementation. It explores the behavior of a Tapestry deployment on PlanetLab, a global testbed of approximately 100 machines. Experimental results show that Tapestry exhibits stable behavior and performance as an overlay, despite the instability of the underlying network layers. Several widely distributed applications have been implemented on Tapestry, illustrating its utility as a deployment infrastructure.

Despite traditional web caching techniques, redundant data is often transferred over HTTP links. These redundant transfers result from both resource modification and aliasing. Resource modification causes the data represented by a single URI to change; often, in transferring the new data, some old data is retransmitted. Aliasing, in contrast, occurs when the same data is named by multiple URIs, often in the context of dynamic or advertising content. Traditional web caching techniques index data by its name and thus often fail to recognize and take advantage of aliasing.Despite traditional web caching techniques, redundant data is often transferred over HTTP links. These redundant transfers result from both resource modification and aliasing. Resource modification causes the data represented by a single URI to change; often, in transferring the new data, some old data is retransmitted. Aliasing, in contrast, occurs when the same data is named by multiple URIs, often in the context of dynamic or advertising content. Traditional web caching techniques index data by its name and thus often fail to recognize and take advantage of aliasing.

We propose probabilistic location to enhance the performance of existing peer-to-peer location mechanisms in the case where a replica for the queried data item exists close to the query source. We introduce the attenuated Bloom filter, a lossy distributed index data structure. We describe how to use these data structures for document location and how to maintain them despite document motion. We include a detailed performance study which indicates that our algorithm performs as desired, both finding closer replicas and finding them faster than deterministic algorithms alone.

OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.