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The LOCKSS project has developed and deployed in a worldwide test a peer-to-peer system for preserving access to journals and other archival information published on the Web. It consists of a large number of independent, low-cost, persistent web caches that cooperate to detect and repair damage to their content by voting in "opinion polls." Based on this experience, we present a design for and simulations of a novel protocol for voting in systems of this kind. It incorporates rate limitation and intrusion detection to ensure that even some very powerful adversaries attacking over many years have only a small probability of causing irrecoverable damage before being detected.

Many emerging Web services, such as email, photo sharing, and web site archives, need to preserve large amounts of quickly-accessible data indefinitely into the future. In this paper, we make the case that these applications' demands on large scale storage systems over long time horizons require us to re-evaluate traditional storage system designs. We examine threats to long-lived data from an end-to-end perspective, taking into account not just hardware and software faults but also faults due to humans and organizations. We present a simple model of long-term storage failures that helps us reason about the various strategies for addressing these threats in a cost-effective manner. Using this model we show that the most important strategies for increasing the reliability of long-term storage are detecting latent faults quickly, automating fault repair to make it faster and cheaper, and increasing the independence of data replicas.

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Abstract-Modern frameworks, such as Hadoop, combined with abundance of computing resources from the cloud, offer a significant opportunity to address long standing challenges in distributed processing. Infrastructure-as-a-Service clouds reduce the investment cost of renting a large data center while distributed processing frameworks are capable of efficiently harvesting the rented physical resources. Yet, the performance users get out of these resources varies greatly because the cloud hardware is shared by all users. The value for money cloud consumers achieve renders resource sharing policies a key player in both cloud performance and user satisfaction. In this paper, we employ microeconomics to direct the allotment of cloud resources for consumption in highly scalable masterworker virtual infrastructures. Our approach is developed on two premises: the cloud-consumer always has a budget and cloud physical resources are limited. Using our approach, the cloud administration is able to maximize per-user financial profit. We show that there is an equilibrium point at which our method achieves resource sharing proportional to each user's budget. Ultimately, this approach allows us to answer the question of how many resources a consumer should request from the seemingly endless pool provided by the cloud.