A note on resource management techniques and systems for big data workflow processing

A note on resource management techniques and systems for big data workflow processing Computing (2018) 100:1–2 https://doi.org/10.1007/s00607-018-0586-9 EDITORIAL A note on resource management techniques and systems for big data workflow processing 1 2 Rajiv Ranjan · Prem Prakash Jayaraman · 3 2 Massimo Villari · Dimitrios Georgakopoulos Published online: 13 February 2018 © Springer-Verlag GmbH Austria, part of Springer Nature 2018 The continuous shift towards data-driven enterprises and the necessity of getting real-time insights from streaming data (e.g. tweets, web clicks) has expedited the development of dozens of big data analytics workflows (e.g. click-stream analyt- ics). Resource management of such analytics workflows is vital, since it enables cost-effective usage of cloud services against unpredictable time-varying workloads (characterised by 3Vs—volume, velocity and veracity). A typical streaming data ana- lytics workflow consists of three layers: data ingestion, analytics, and storage, each of which is backed by different data processing platforms (e.g. Amazon Kinesis, Apache Storm, DynamoDB, respectively) and is served by different cloud services (e.g. VM, Queues). Moreover, the application workloads processed by the data analytics work- flows are heterogeneous and demand different performance and quality of service measures. Hence, elasticity management of various resources for such big data ana- lytics workflow is both difficult and challenging. B Rajiv Ranjan raj.ranjan@ncl.ac.uk Prem Prakash Jayaraman http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computing Springer Journals

A note on resource management techniques and systems for big data workflow processing

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Austria, part of Springer Nature
Subject
Computer Science; Computer Science, general; Information Systems Applications (incl.Internet); Computer Communication Networks; Software Engineering; Artificial Intelligence (incl. Robotics); Computer Appl. in Administrative Data Processing
ISSN
0010-485X
eISSN
1436-5057
D.O.I.
10.1007/s00607-018-0586-9
Publisher site
See Article on Publisher Site

Abstract

Computing (2018) 100:1–2 https://doi.org/10.1007/s00607-018-0586-9 EDITORIAL A note on resource management techniques and systems for big data workflow processing 1 2 Rajiv Ranjan · Prem Prakash Jayaraman · 3 2 Massimo Villari · Dimitrios Georgakopoulos Published online: 13 February 2018 © Springer-Verlag GmbH Austria, part of Springer Nature 2018 The continuous shift towards data-driven enterprises and the necessity of getting real-time insights from streaming data (e.g. tweets, web clicks) has expedited the development of dozens of big data analytics workflows (e.g. click-stream analyt- ics). Resource management of such analytics workflows is vital, since it enables cost-effective usage of cloud services against unpredictable time-varying workloads (characterised by 3Vs—volume, velocity and veracity). A typical streaming data ana- lytics workflow consists of three layers: data ingestion, analytics, and storage, each of which is backed by different data processing platforms (e.g. Amazon Kinesis, Apache Storm, DynamoDB, respectively) and is served by different cloud services (e.g. VM, Queues). Moreover, the application workloads processed by the data analytics work- flows are heterogeneous and demand different performance and quality of service measures. Hence, elasticity management of various resources for such big data ana- lytics workflow is both difficult and challenging. B Rajiv Ranjan raj.ranjan@ncl.ac.uk Prem Prakash Jayaraman

Journal

ComputingSpringer Journals

Published: Feb 13, 2018

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