This paper considers replication strategies for storage systems that aggregate the disks of many nodes spread over the Internet. Maintaining replication in such systems can be prohibitively expensive, since every transient network or host failure could potentially lead to copying a server’s worth of data over the Internet to maintain replication levels. The following insights in designing an efficient replication algorithm emerge from the paper’s analysis. First, durability can be provided separately from availability; the former is less expensive to ensure and a more useful goal for many wide-area applications. Second, the focus of a durability algorithm must be to create new copies of data objects faster than permanent disk failures destroy the objects; careful choice of policies for what nodes should hold what data can decrease repair time. Third, increasing the number of replicas of each data object does not help a system tolerate a higher disk failure probability, but does help tolerate bursts of failures. Finally, ensuring that the system makes use of replicas that recover after temporary failure is critical to efficiency. Based on these insights, the paper proposes the Carbonite replication algorithm for keeping data durable at a low cost. A simulation of Carbonite storing 1 TB of data over a 365 day trace of PlanetLab activity shows that Carbonite is able to keep all data durable and uses 44 % more network traffic than a hypothetical system that only responds to permanent failures. In comparison, Total Recall and DHash require almost a factor of two more network traffic than this hypothetical system. 1

Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using an erasure code, in fragments spread across nodes, requires less redundancy than simple replication for the same level of reliability. However, since fragments must be periodically replaced as nodes fail, a key question is how to generate encoded fragments in a distributed way while transferring as little data as possible across the network. For an erasure coded system, a common practice to repair from a node failure is for a new node to download subsets of data stored at a number of surviving nodes, reconstruct a lost coded block using the downloaded data, and store it at the new node. We show that this procedure is sub-optimal. We introduce the notion of regenerating codes, which allow a new node to download functions of the stored data from the surviving nodes. We show that regenerating codes can significantly reduce the repair bandwidth. Further, we show that there is a fundamental tradeoff between storage and repair bandwidth which we theoretically characterize using flow arguments on an appropriately constructed graph. By invoking constructive results in network coding, we introduce regenerating codes that can achieve any point in this optimal tradeoff. I.

Many wide-area storage systems replicate data for durability. A common way of maintaining the replicas is to detect node failures and respond by creating additional copies of objects that were stored on failed nodes and hence suffered a loss of redundancy. Reactive techniques can minimize total bytes sent since they only create replicas as needed; however, they can create spikes in network use after a failure. These spikes may overwhelm application traffic and can make it difficult to provision bandwidth. This paper explores a proactive approach that creates additional copies not in response to failures, but periodically at a fixed low rate. We introduce Tempo, a distributed hash table that allows each user to specify a maximum maintenance bandwidth and uses it to perform proactive replication. Results from a simulation study suggest that Tempo can deliver high durability despite only using several kilobytes per second of bandwidth, comparable to state-ofthe-art reactive systems. 1.

Storage is often a fundamental service provided by peer-topeer systems, where the system stores data objects on behalf of higher-level services, applications, and users. A primary challenge in peer-to-peer storage systems is to efficiently

Highly available cloud storage is often implemented with complex, multi-tiered distributed systems built on top of clusters of commodity servers and disk drives. Sophisticated management, load balancing and recovery techniques are needed to achieve high performance and availability amidst an abundance of failure sources that include software, hardware, network connectivity, and power issues. While there is a relative wealth of failure studies of individual components of storage systems, such as disk drives, relatively little has been reported so far on the overall availability behavior of large cloudbased storage services. We characterize the availability properties of cloud storage systems based on an extensive one year study of Google’s main storage infrastructure and present statistical models that enable further insight into the impact of multiple design choices, such as data placement and replication strategies. With these models we compare data availability under a variety of system parameters given the real patterns of failures observed in our fleet. 1

Contributory applications allow users to donate unused resources on their personal computers to a shared pool. Applications such as

Markov models

Abstract not found

Abstract — A fundamental paradigm in peer-to-peer (P2P) content distribution is that of a large community of intermittentlyconnected nodes that cooperate to share files. Because nodes are intermittently connected, the P2P community must replicate and replace files as a function of their popularity to achieve satisfactory performance. In this paper, we develop an analytical optimization theory for benchmarking the performance of replication/replacement algorithms, including algorithms that employ erasure codes. We also consider a content management algorithm, the Top-K Most Frequently Requested algorithm, and show that in most cases this algorithm converges to an optimal replica profile. Finally, we present two approaches for achieving an evenly balanced load over all the peers in the community. I.

Distributed storage systems provide data availability by means of redundancy. To assure a given level of availability in case of node failures, new redundant fragments need to be introduced. Since node failures can be either transient or permanent, deciding when to generate new fragments is non-trivial. An additional difficulty is due to the fact that the failure behavior in terms of the rate of permanent and transient failures may vary over time. To be able to adapt to changes in the failure behavior, many systems adopt a reactive approach, in which new fragments are created as soon as a failure is detected. However, reactive approaches tend to produce spikes in bandwidth consumption. Proactive approaches create new fragments at a fixed rate that depends on the knowledge of the failure behavior or is given by the system administrator. However, existing proactive systems are not able to adapt to a changing failure behavior, which is common in real world. We propose a new technique based on an ongoing estimation of the failure behavior that is obtained using a model that consists of a network of queues. This scheme combines the adaptiveness of reactive systems with the smooth bandwidth usage of proactive systems, generalizing the two previous approaches. Now, the duality reactive or proactive becomes a specific case of a wider approach tunable with respect to the dynamics of the failure behavior. 1.