An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

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

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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.

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Naiad: a timely dataflow system

Big Data computing and clouds

Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. Reed-Solomon codes are the standard design choice and their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability.This paper shows how to overcome this limitation. We present a novel family of erasure codes that are efficiently repairable and offer higher reliability compared to Reed-Solomon codes. We show analytically that our codes are optimal on a recently identified tradeoff between locality and minimum distance.We implement our new codes in Hadoop HDFS and compare to a currently deployed HDFS module that uses Reed-Solomon codes. Our modified HDFS implementation shows a reduction of approximately 2× on the repair disk I/O and repair network traffic. The disadvantage of the new coding scheme is that it requires 14% more storage compared to Reed-Solomon codes, an overhead shown to be information theoretically optimal to obtain locality. Because the new codes repair failures faster, this provides higher reliability, which is orders of magnitude higher compared to replication.

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XORing elephants: novel erasure codes for big data

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.

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

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

The present systems and methods disclose a system for personalizing computer functionality. End-users are provided with tools to easily write rich and complex preferences, for example, by using a plurality simple IF-THEN propositional logic. The preferences are then transformed into queries and executed efficiently on structured data. Preferences that are satisfied then execute actions such as providing notification or storing data in a particular folder. Furthermore, according to an aspect of the invention, data, logic, events, inter alia, are all schematized, thereby enabling sharing of data between application components and across applications.

System and method for preference application installation and execution

FIELD: physics; computer facilities. ^ SUBSTANCE: proposition concerns agents of computer systems personification. A tool for simple writing of the manifold text and difficult preferences, for example, with use of set of simple logic of statements IF-THEN is given to end users. Preferences are then conversed to inquiries and executed concerning the structured data. Satisfying preferences then execute activities, such, as representation of the notice or saving of data in a concrete folder. Besides, according to aspect of the invention, all data, logic, events, besides other, are schematised. ^ EFFECT: increase of productivity of operation of the user at the expense of possibility of data sharing between components of the application program and between application programs. ^ 23 cl, 43 dwg, 1 ex

System and way of installation and execution of application programs of preferences

Embodiments of the present invention relate to asynchronously replicating data in a distributed computing environment. To achieve asynchronous replication, data received at a primary data store may be annotated with information, such as an identifier of the data. The annotated data may then be communicated to a secondary data store, which may then write the data and annotated information to one or more logs for eventual replay and committal at the secondary data store. The primary data store may communicate an acknowledgment of success in committing the data at the primary data store as well as of success in writing the data to the secondary data store. Additional embodiments may include committing the data at the secondary data store in response to receiving an instruction that authorizes committal of data through a identifier.

Niranjan Nilakantan

Papers

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

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

System and method for preference application installation and execution

System and way of installation and execution of application programs of preferences

Asynchronous replication in a distributed storage environment