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A SLA-based Spark cluster scaling method in cloud environment

Authors
Oh Y.Choi J.Song E.Kim M.Kim Y.
Issue Date
Nov-2016
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
auto-scaling; resource management; SLA; Spark Cluster
Citation
18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings
Journal Title
18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/3600
DOI
10.1109/APNOMS.2016.7737242
Abstract
As the development of Internet and mobile device increases, there is a correspondingly increasing amount of data produced by users of such technology worldwide. It is thus essential to analyze such massive amounts of data reflective of the big data era. Recently, Apache Spark has become popular for analyzing big data, since it can process streaming data and support real-time in-memory computing. Also, it is known to execute applications faster than traditionally used Hadoop. Also cloud technology provides flexible resource utilization environment on-demand. When analyzing big data using Spark in existing environments, it is difficult to provision resources according to the system's changing environment and the influence of other users' executions. Using cloud technology however, it is possible to provision resources more effectively for the execution of jobs through dynamic resource provision methods. In this paper, we propose an auto-scaling framework with corresponding algorithms to manage resources dynamically in virtual environments, in order to meet user-specified SLA (Service Level Agreement) given a set of limited resources. Our experimental results on Spark in OpenStack demonstrate the effectiveness of scaling resources to satisfy user SLAs. © 2016 IEICE.
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공과대학 (소프트웨어학부(첨단))
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