In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.

DepSky: Dependable and Secure Storage in a Cloud-of-Clouds

Semiology of Graphics: Diagrams Networks Maps.

BTRFS is a Linux filesystem that has been adopted as the default filesystem in some popular versions of Linux. It is based on copy-on-write, allowing for efficient snapshots and clones. It uses B-trees as its main on-disk data structure. The design goal is to work well for many use cases and workloads. To this end, much effort has been directed to maintaining even performance as the filesystem ages, rather than trying to support a particular narrow benchmark use-case.Linux filesystems are installed on smartphones as well as enterprise servers. This entails challenges on many different fronts.---Scalability. The filesystem must scale in many dimensions: disk space, memory, and CPUs.---Data integrity. Losing data is not an option, and much effort is expended to safeguard the content. This includes checksums, metadata duplication, and RAID support built into the filesystem.---Disk diversity. The system should work well with SSDs and hard disks. It is also expected to be able to use an array of different sized disks, which poses challenges to the RAID and striping mechanisms.This article describes the core ideas, data structures, and algorithms of this filesystem. It sheds light on the challenges posed by defragmentation in the presence of snapshots, and the tradeoffs required to maintain even performance in the face of a wide spectrum of workloads.

BTRFS: The Linux B-Tree Filesystem

[IEEE 2011 IEEE World Congress on Services (SERVICES) - Washington, DC, USA (2011.07.4-2011.07.9)] 2011 IEEE World Congress on Services - TrustCloud: A Framework for Accountability and Trust in Cloud Computing

We present LLAMA, a graph storage and analysis system that supports mutability and out-of-memory execution. LLAMA performs comparably to immutable main-memory analysis systems for graphs that fit in memory and significantly outperforms existing out-of-memory analysis systems for graphs that exceed main memory. LLAMA bases its implementation on the compressed sparse row (CSR) representation, which is a read-only representation commonly used for graph analytics. We augment this representation to support mutability and persistence using a novel implementation of multi-versioned array snapshots, making it ideal for applications that receive a steady stream of new data, but need to perform whole-graph analysis on consistent views of the data. We compare LLAMA to state-of-the-art systems on representative graph analysis workloads, showing that LLAMA scales well both out-of-memory and across parallel cores. Our evaluation shows that LLAMA's mutability introduces modest overheads of 3–18% relative to immutable CSR for in-memory execution and that it outperforms state-of-the-art out-of-memory systems in most cases, with a best case improvement of 5x on breadth-first-search.

/pdf/llama-efficient-graph-analytics-using-large-multiversioned-ztr8uoo7ol.pdf

LLAMA: Efficient graph analytics using Large Multiversioned Arrays

Having effective visualizations of filesystem provenance data is valuable for understanding its complex hierarchical structure. The most common visual representation of provenance data is the node-link diagram. While effective for understanding local activity, the node-link diagram fails to offer a high-level summary of activity and inter-relationships within the data. We present a new tool, InProv, which displays filesystem provenance with an interactive radial-based tree layout. The tool also utilizes a new time-based hierarchical node grouping method for filesystem provenance data we developed to match the user's mental model and make data exploration more intuitive. We compared InProv to a conventional node-link based tool, Orbiter, in a quantitative evaluation with real users of filesystem provenance data including provenance data experts, IT professionals, and computational scientists. We also compared in the evaluation our new node grouping method to a conventional method. The results demonstrate that InProv results in higher accuracy in identifying system activity than Orbiter with large complex data sets. The results also show that our new time-based hierarchical node grouping method improves performance in both tools, and participants found both tools significantly easier to use with the new time-based node grouping method. Subjective measures show that participants found InProv to require less mental activity, less physical activity, less work, and is less stressful to use. Our study also reveals one of the first cases of gender differences in visualization; both genders had comparable performance with InProv, but women had a significantly lower average accuracy (56%) compared to men (70%) with Orbiter.

/pdf/evaluation-of-filesystem-provenance-visualization-tools-2p7xvinmpx.pdf

Evaluation of Filesystem Provenance Visualization Tools

Provenance systems can produce enormous provenance graphs that can be used for a variety of tasks from determining the inputs to a particular process to debugging entire workflow executions or tracking difficult-to-find dependencies. Visualization can be a useful tool to support such tasks, but graphs of such scale (thousands to millions of nodes) are notoriously difficult to visualize. This paper presents the Provenance Map Orbiter, a tool for interactively exploring large provenance graphs using graph summarization and semantic zoom. It presents its users with a high-level abstracted view of the graph and the ability to incrementally drill down to the details.

/pdf/provenance-map-orbiter-interactive-exploration-of-large-48z9xvidi7.pdf

Provenance Map Orbiter: Interactive Exploration of Large Provenance Graphs

The advent of cloud computing provides a cheap and convenient mechanism for scientists to share data. The utility of such data is obviously enhanced when the provenance of the data is also available. The cloud, while convenient for storing data, is not designed for storing and querying provenance. In this paper, we present desirable properties for distributed provenance storage systems and present design alternatives for storing data and provenance on Amazon's popular Web Services platform (AWS). We evaluate the properties satisfied by each approach and analyze the cost of storing and querying provenance in each approach.

/pdf/making-a-cloud-provenance-aware-3iz6w8jy2x.pdf

Making a cloud provenance-aware

Many file systems reorganize data on disk, for example to defragment storage, shrink volumes, or migrate data between different classes of storage. Advanced file system features such as snapshots, writable clones, and deduplication make these tasks complicated, as moving a single block may require finding and updating dozens, or even hundreds, of pointers to it.We present Backlog, an efficient implementation of explicit back references, to address this problem. Back references are file system meta-data that map physical block numbers to the data objects that use them. We show that by using LSM-Trees and exploiting the write-anywhere behavior of modern file systems such as NetApp® WAFL® or btrfs, we can maintain back reference meta-data with minimal overhead (one extra disk I/O per 102 block operations) and provide excellent query performance for the common case of queries covering ranges of physically adjacent blocks.

/pdf/tracking-back-references-in-a-write-anywhere-file-system-53c4uh0z9c.pdf

Tracking back references in a write-anywhere file system

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