ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

https://homes.cs.washington.edu/~mernst/pubs/daikon-tool-scp2007.pdf

The Daikon system for dynamic detection of likely invariants

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time WAS customers have access to their data from anywhere, at any time, and only pay for what they use and store To provide durability for that data and to keep the cost of storage low, WAS uses erasure coding

In this paper we introduce a new set of codes for erasure coding called Local Reconstruction Codes (LRC) LRC reduces the number of erasure coding fragments that need to be read when reconstructing data fragments that are offline, while still keeping the storage overhead low The important benefits of LRC are that it reduces the bandwidth and I/Os required for repair reads over prior codes, while still allowing a significant reduction in storage overhead We describe how LRC is used in WAS to provide low overhead durable storage with consistently low read latencies

/pdf/erasure-coding-in-windows-azure-storage-3hqt1rghxj.pdf

Erasure coding in windows azure storage

Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework.A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism.We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.

/pdf/naiad-a-timely-dataflow-system-1z2cah9a5l.pdf

Naiad: a timely dataflow system

Big Data computing and clouds

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.

/pdf/windows-azure-storage-a-highly-available-cloud-storage-18wio04utc.pdf

Windows Azure Storage: a highly available cloud storage service with strong consistency

Dynamic inference techniques have been demonstrated to provide useful support for various software engineering tasks including bug finding, test suite evaluation and improvement, and specification generation. To date, however, dynamic inference has only been used effectively on small programs under controlled conditions. In this paper, we identify reasons why scaling dynamic inference techniques has proven difficult, and introduce solutions that enable a dynamic inference technique to scale to large programs and work effectively with the imperfect traces typically available in industrial scenarios. We describe our approximate inference algorithm, present and evaluate heuristics for winnowing the large number of inferred properties to a manageable set of interesting properties, and report on experiments using inferred properties. We evaluate our techniques on JBoss and the Windows kernel. Our tool is able to infer many of the properties checked by the Static Driver Verifier and leads us to discover a previously unknown bug in Windows.

/pdf/perracotta-mining-temporal-api-rules-from-imperfect-traces-263k78gb23.pdf

Perracotta: mining temporal API rules from imperfect traces

Partition management for a scalable, structured storage system is provided. The storage system provides storage represented by one or more tables, each of which includes rows that represent data entities. A table is partitioned into a number of partitions, each partition including a contiguous range of rows. The partitions are served by table servers and managed by a table master. Load distribution information for the table servers and partitions is tracked, and the table master determines to split and/or merge partitions based on the load distribution information.

Partition management in a partitioned, scalable, and available structured storage

Embodiments of the present invention relate to systems, methods and computer storage media for facilitating the structured storage of binary large objects (Blobs) to be accessed by an application program being executed by a computing device. Generally, the manipulation of Blobs in a structured storage system includes receiving a request for a Blob, which may be located by way of a Blob pointer. The Blob pointer allows for the data, such as properties, of the Blob to be identified and located. Expired properties are garbage collected as a manipulation of the Blob data within a structured storage system. In an embodiment, the Blob is identified by a key that is utilized within a primary structured index to located the requested Blob. In another embodiment, the requested Blob is located utilizing a secondary hash index. In an additional embodiment, the Blob is locate utilizing a file table.

Blob manipulation in an integrated structured storage system

Embodiments of the present invention relate to systems, methods and computer storage media for facilitating the structured storage of binary large objects (Blobs) to be accessed by an application program being executed by a computing device. Generally, the structured storage of Blobs includes a primary structured storage index for indexing Blobs, a secondary hash index that is integrated into the structured storage system, a Blob log stream, and a Blob data stream for storing blocks that include the Blob data. In an embodiment, a block is created and written to a Blob store along with a block list. The block list facilitates the locating of one or more blocks that store the Blob data. In this embodiment, a primary structured storage index and a secondary hash index are updated to facilitate efficient access of the Blob in a structured storage system.

Deepali Bhardwaj

Papers

Windows Azure Storage: a highly available cloud storage service with strong consistency

Perracotta: mining temporal API rules from imperfect traces

Partition management in a partitioned, scalable, and available structured storage

Blob manipulation in an integrated structured storage system

Scalable blob storage integrated with scalable structured storage