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

By Brad Calder, Ju Wang, Aaron Ogus, Niranjan Nilakantan, Arild Skjolsvold, Sam McKelvie, Yikang Xu, Shashwat Srivastav, Jiesheng Wu, Huseyin Simitci and 17 more. Symposium on Operating Systems Principles, 2011

946 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/75032b6df2f3988d2cb6988c73cbc01c7b9e80cb

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Citations in the corpus

Analyzing the download time of availability codes (2015, methodology): “We use a typical set of parameters as observed in Windows Azure Storage [24] to construct the Markov chain.”
Rethinking erasure codes for cloud file systems: minimizing I/O for recovery and degraded reads (2012, methodology): “Our model for a cloud file system using erasure codes is inspired by Microsoft Azure [10].”
Erasure Coding in Windows Azure Storage (2012, background): “Our choice to implement erasure coding inside the stream layer is based on the fact that it fits the overall WAS architecture where the stream layer is responsible for keeping the data durable within a stamp and the partition layer is responsible for geo-replicating the data between data centers (se”
f4: Facebook's Warm BLOB Storage System (2014, background): “Microsoft’s Windows Azure Storage [10] is designed to be a generic cloud service while ours is a more specialized application, with more unique challenges as well as optimization opportunities.”
Biscuit: A Framework for Near-Data Processing of Big Data Workloads (2016, background): “Fortunately, in large storage and data processing platforms, software-driven inter-server replication and erasure coding become increasingly popular [7, 8, 38, 39].”
Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google's Datacenter Network (2015, background): “A combination of remote storage access [7,14], large-scale data processing [10,18], and interactive web services [4] drive our bandwidth demands.”
Paragon: QoS-aware scheduling for heterogeneous datacenters (2013, background): “Additionally, Paragon maintains performance degradation moderate (no cliffs in performance such as for NH in applications [1-1000]) and limits degradation to less than 10% for an additional 33% of applications.”
Stronger Semantics for Low-Latency Geo-Replicated Storage (2013, neutral): “Many existing systems [5, 13, 16, 54], in fact, provide all assumed properties when used inside a single datacenter.”
Naiad: a timely dataflow system (2013, background): “Naiad can consume inputs from a reliable message queue and write its outputs to a distributed key-value store [8].”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Users of cloud services already make similar decisions; for example, in Azure Storage [32], they separately select data redundancy (local, zone, or geo-distributed) and data access pattern (hot vs.”
Big Data computing and clouds: Trends and future directions (2013, methodology): “Users manipulate data sets on Excel and plugins use Microsoft’s Azure infrastructure [26] to run MapReduce applications.”
RDMA over Commodity Ethernet at Scale (2016, background): “However, it is increasingly clear that the traditional TCP/IP stack cannot meet the demands of the new generation of DC work-loads [4, 9, 16, 40], for two reasons.”
Serverless Computing: Design, Implementation, and Performance (2017, methodology): “Azure Storage was chosen because it provides highly scalable and low-latency storage primitives through a simple API, aligning well with the goals of this implementation [27].”
The Snowflake Elastic Data Warehouse (2016, background): “Storage is provided through Amazon S3 [5], though in principle any type of blob store would suﬃce (Azure Blob Storage [18, 36], Google Cloud Storage [20]).”
Trends in big data analytics (2014, neutral): “Microsoft’s Windows Azure Storage (WAS) [20]”