Proceedings ArticleDOI
Apache Hadoop YARN: yet another resource negotiator
Vinod Kumar Vavilapalli,Arun C. Murthy,Chris Douglas,Sharad Agarwal,Mahadev Konar,Robert Evans,Thomas Graves,Jason Lowe,Hitesh Shah,Siddharth Seth,Bikas Saha,Carlo Curino,Owen O'Malley,Sanjay Radia,Benjamin Reed,Eric Baldeschwieler +15 more
- pp 5
TLDR
The design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN is summarized, which decouples the programming model from the resource management infrastructure, and delegates many scheduling functions to per-application components.Abstract:
The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agora---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler. In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.read more
Citations
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Proceedings ArticleDOI
Large-scale cluster management at Google with Borg
TL;DR: A summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it are presented.
Proceedings ArticleDOI
Resource Management with Deep Reinforcement Learning
TL;DR: This work presents DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem, and shows that it performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.
Proceedings ArticleDOI
Storm@twitter
Ankit Toshniwal,Siddarth Taneja,Amit Shukla,Karthik Ramasamy,Jignesh M. Patel,Sanjeev Kulkarni,Jason Jackson,Krishna Gade,Maosong Fu,Jake Donham,Nikunj Bhagat,Sailesh Mittal,Dmitriy Ryaboy +12 more
TL;DR: The architecture of Storm and its methods for distributed scale-out and fault-tolerance are described, how queries are executed in Storm is described, and some operational stories based on running Storm at Twitter are presented.
Journal ArticleDOI
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing
TL;DR: A survey of integration components: Cloud platforms, Cloud infrastructures and IoT Middleware is presented and some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.
Proceedings ArticleDOI
Twitter Heron: Stream Processing at Scale
Sanjeev Kulkarni,Nikunj Bhagat,Maosong Fu,Vikas Kedigehalli,Christopher Kellogg,Sailesh Mittal,Jignesh M. Patel,Karthik Ramasamy,Siddarth Taneja +8 more
TL;DR: Heron is now the de facto stream data processing engine inside Twitter, and in this paper the design and implementation of this new system, called Heron are presented and the experiences from running Heron in production are shared.
References
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Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Proceedings ArticleDOI
The Hadoop Distributed File System
TL;DR: The architecture of HDFS is described and experience using HDFS to manage 25 petabytes of enterprise data at Yahoo! is reported on.
Proceedings Article
Spark: cluster computing with working sets
TL;DR: Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.
Book
The Mythical Man-Month
TL;DR: The Mythical Man-Month, Addison-Wesley, 1975 (excerpted in Datamation, December 1974), gathers some of the published data about software engineering and mixes it with the assertion of a lot of personal opinions.