Designing a wide-area distributed hash table (DHT) that provides high-throughput and low-latency network storage is a challenge. Existing systems have explored a range of solutions, including iterative routing, recursive routing, proximity routing and neighbor selection, erasure coding, replication, and server selection. This

Abstract — Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution and application-level multicast applications. These P2P networks try to provide a long list of features such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and, anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing and explicit notion of locality. In this paper, we present a survey and comparison of various Structured and Unstructured P2P networks. We categorize the various schemes into these two groups in the design spectrum and discuss the application-level network performance of each group.

Decentralized storage systems aggregate the available disk space of participating computers to provide a large storage facility. These systems rely on data redundancy to ensure durable storage despite of node failures. However, existing systems either assume independent node failures, or they rely on introspection to carefully place redundant data on nodes with low expected failure correlation. Unfortunately, node failures are not independent in practice and constructing an accurate failure model is difficult in large-scale systems. At the same time, malicious worms that propagate through the Internet pose a real threat of large-scale correlated failures. Such rare but potentially catastrophic failures must be considered when attempting to provide highly durable storage. In this paper, we describe Glacier, a distributed storage system that relies on massive redundancy to mask the effect of large-scale correlated failures. Glacier is designed to aggressively minimize the cost of this redundancy in space and time: Erasure coding and garbage collection reduces the storage cost; aggregation of small objects and a loosely coupled maintenance protocol for redundant fragments minimizes the messaging cost. In one configuration, for instance, our system can provide six-nines durable storage despite correlated failures of up to 60 % of the storage nodes, at the cost of an elevenfold storage overhead and an average messaging overhead of only 4 messages per node and minute during normal operation. Glacier is used as the storage layer for an experimental serverless email system. 1

We present a link layer protocol called the Multi-radio Unification Protocol or MUP. On a single node, MUP coordinates the operation of multiple wireless network cards tuned to non-overlapping frequency channels. The goal of MUP is to optimize local spectrum usage via intelligent channel selection in a multihop wireless network. MUP works with standard-compliant IEEE 802.11 hardware, does not require changes to applications or higher-level protocols, and can be deployed incrementally. The primary usage scenario for MUP is a multihop community wireless mesh network, where cost of the radios and battery consumption are not limiting factors. We describe the design and implementation of MUP, and analyze its performance using both simulations and measurements based on our implementation. Our results show that under dynamic traffic patterns with realistic topologies, MUP significantly improves both TCP throughput and user perceived latency for realistic workloads. 1.

Motivated by the prospect of readily available Content Addressable Storage (CAS), we introduce the concept of file recipes. A file's recipe is a first-class file system object listing content hashes that describe the data blocks composing the file. File recipes provide applications with instructions for reconstructing the original file from available CAS data blocks. We describe one such application of recipes, the CASPER distributed file system. A CASPER client opportunistically fetches blocks from nearby CAS providers to improve its performance when the connection to a file server traverses a low-bandwidth path. We use measurements of our prototype to evaluate its performance under varying network conditions. Our results demonstrate significant improvements in execution times of applications that use a network file system. We conclude by describing fuzzy block matching, a promising technique for using approximately matching blocks on CAS providers to reconstitute the exact desired contents of a file at a client.

Ongoing advancements in technology lead to everincreasing storage capacities. In spite of this, optimizing storage usage can still provide rich dividends. Several techniques based on delta-encoding and duplicate block suppression have been shown to reduce storage overheads, with varying requirements for resources such as computation and memory. We propose a new scheme for storage reduction that reduces data sizes with an effectiveness comparable to the more expensive techniques, but at a cost comparable to the faster but less effective ones. The scheme, called Redundancy Elimination at the Block Level (REBL), leverages the benefits of compression, duplicate block suppression, and delta-encoding to eliminate a broad spectrum of redundant data in a scalable and efficient manner. REBL generally encodes more compactly than compression (up to a factor of 14) and a combination of compression and duplicate suppression (up to a factor of 6.7). REBL also encodes similarly to a technique based on delta-encoding, reducing overall space significantly in one case. Furthermore, REBL uses super-fingerprints, a technique that reduces the data needed to identify similar blocks while dramatically reducing the computational requirements of matching the blocks: it turns comparisons into hash table lookups. As a result, using super-fingerprints to avoid enumerating matching data objects decreases computation in the resemblance detection phase of REBL by up to a couple orders of magnitude.

This paper presents the design and implementation of DOT, a flexible architecture for data transfer. This architecture separates content negotiation from the data transfer itself. Applications determine what data they need to send and then use a new transfer service to send it. This transfer service acts as a common interface between applications and the lower-level network layers, facilitating innovation both above and below. The transfer service frees developers from re-inventing transfer mechanisms in each new application. New transfer mechanisms, in turn, can be easily deployed without modifying existing applications. We discuss the benefits that arise from separating data transfer into a service and the challenges this service must overcome. The paper then examines the implementation of DOT and its plugin framework for creating new data transfer mechanisms. A set of microbenchmarks shows that the DOT prototype performs well, and that the overhead it imposes is unnoticeable in the wide-area. End-to-end experiments using more complex configurations demonstrate DOT’s ability to implement effective, new data delivery mechanisms underneath existing services. Finally, we evaluate a production mail server modified to use DOT using trace data gathered from a live email server. Converting the mail server required only 184 lines-of-code changes to the server, and the resulting system reduces the bandwidth needed to send email by up to 20%. 1

Many objects, such as les, electronic messages, and web pages, contain overlapping content. Numerous past research projects have observed that one can compress one object relative to another one by computing the differences between the two, but these delta-encoding systems have almost invariably required knowledge of a specific relationship between them most commonly, two versions using the same name at different points in time. We consider cases in which this relationship is determined dynamically, by efficiently determining when a sufficient resemblance exists between two objects in a relatively large collection. We look at specific examples of this technique, namely web pages, email, and files in a file system, and evaluate the potential data reduction and the factors that influence this reduction. We find that delta-encoding using this resemblance detection technique can improve on simple compression by up to a factor of two, depending on workload, and that a small fraction of objects can potentially account for a large portion of these savings.

For five years, we collected annual snapshots of file-system metadata from over 60,000 Windows PC file systems in a large corporation. In this article, we use these snapshots to study temporal changes in file size, file age, file-type frequency, directory size, namespace structure, file-system population, storage capacity and consumption, and degree of file modification. We present a generative model that explains the namespace structure and the distribution of directory sizes. We find significant temporal trends relating to the popularity of certain file types, the origin of file content, the way the namespace is used, and the degree of variation among file systems, as well as more pedestrian changes in size and capacities. We give examples of consequent lessons for designers of file systems and related software.

This thesis presents a new design and implementation of the DHash distributed hash table based on erasure encoding. This design is both more robust and more efficient than the previous replication-based implementation [15]. DHash uses