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. 1

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

The sharing of caches among Web proxies is an important technique to reduce Web traffic and alleviate network bottlenecks. Nevertheless it is not widely deployed due to the overhead of existing protocols. In this paper we propose a new protocol called "Summary Cache"; each proxy keeps a summary of the URLs of cached documents of each participating proxy and checks these summaries for potential hits before sending any queries. Two factors contribute to the low overhead: the summaries are updated only periodically, and the summary representations are economical -- as low as 8 bits per entry. Using trace-driven simulations and a prototype implementation, we show that compared to the existing Internet Cache Protocol (ICP), Summary Cache reduces the number of inter-cache messages by a factor of 25 to 60, reduces the bandwidth consumption by over 50%, and eliminates between 30 % to 95 % of the CPU overhead, while at the same time maintaining almost the same hit ratio as ICP. Hence Summary Cache enables cache sharing among a large number of proxies.

Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

We present Tapestry, a peer-to-peer overlay routing infrastructure offering efficient, scalable, locationindependent 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 (DOLR) API using a self-repairing, softstate based routing layer. This 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.

Abstract. ABloomfilter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used in database applications since the 1970s, but only in recent years have they become popular in the networking literature. The aim of this paper is to survey the ways in which Bloom filters have been used and modified in a variety of network problems, with the aim of providing a unified mathematical and practical framework for understanding them and stimulating their use in future applications. 1.

this paper, we describe a new information management service called Astrolabe. Astrolabe monitors the dynamically changing state of a collection of distributed resources, reporting summaries of this information to its users. Like DNS, Astrolabe organizes the resources into a hierarchy of domains, which we call zones to avoid confusion, and associates attributes with each zone. Unlike DNS, zones are not bound to specific servers, the attributes may be highly dynamic, and updates propagate quickly; typically, in tens of seconds

In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel. Key contributions of this work include: i) an algorithm that sends data to di#erent points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing.

Accurate network traffic measurement is required for accounting, bandwidth provisioning, and detecting DOS attacks. However, keeping a counter to measure the traffic sent by each of a million concurrent flows is too expensive (using SRAM) or slow (using DRAM). The current state-of-the-art (e.g., Cisco NetFlow) methods which count periodically sampled packets are slow, inaccurate, and memory-intensive. Our paper introduces a paradigm shift by concentrating on the problem of measuring only "heavy" flows --- i.e., flows whose traffic is above some threshold such as 1% of the link. After showing that a number of simple solutions based on cached counters and classical sampling do not work, we describe two novel and scalable schemes for this purpose which take a constant number of memory references per packet and use a small amount of memory. Further, unlike NetFlow estimates, we have provable bounds on the accuracy of measured rates and the probability of false negatives. We also propose a new form of accounting called threshold accounting in which only flows above threshold are charged by usage while the rest are charged a fixed fee. Threshold accounting generalizes the familiar notions of usage-based and duration based pricing. I.

Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100 % of messages with reasonable aggregate resource consumption in a number of interesting scenarios. 1