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  1. Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP (DOLAP '15)
  2. SpotADAPT: Spot-Aware (re-)Deployment of Analytical Processing Tasks on Amazon EC2
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Eco-DMW: Eco-Design Methodology for Data warehouses
Homogenizing Data and Metadata Retrieval in Scientific Applications
Nearest Neighbour Join with Groups and Predicates
Towards OLAP Analysis of Multidimensional Tweet Streams
SWOT: A Conceptual Data Warehouse Model for Semantic Trajectories
Big Data Design
S3J: A Parallel Semi-Stream Similarity Join
Resolving Common Analytical Tasks in Text Databases
Towards a Programmable Semantic Extract-Transform-Load Framework for Semantic Data Warehouses
SpotADAPT: Spot-Aware (re-)Deployment of Analytical Processing Tasks on Amazon EC2
Efficient Visualisation of the Relative Distribution of Keyword Search Results in a Corpus Data Cube
Supporting Data Integration Tasks with Semi-Automatic Ontology Construction

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SpotADAPT: Spot-Aware (re-)Deployment of Analytical Processing Tasks on Amazon EC2

Content Provider ACM Digital Library
Author Çetintemel, Ugur Kraska, Tim Kaulakienė, Dalia Thomsen, Christian Pedersen, Torben Bach
Abstract Having constantly increasing amounts of data, the analysis of it is often entrusted for a MapReduce framework. The execution of an analytical workload can be cheapened by adopting cloud computing resources, and in particular by using spot instances (cheap, fluctuating price instances) offered by Amazon Web Services (AWS). The users aiming for the spot market are presented with many instance types placed in multiple datacenters in the world, and thus it is difficult to choose the optimal deployment. In this paper, we propose the framework SpotADAPT (Spot-Aware (re-)Deployment of Analytical Processing Tasks) which is designed to help users by first, estimating the workload execution time on different AWS instance types, and, second, proposing the deployment (i.e., specific availability zone, instance type, pricing model) aligned with user-provided optimization goals (fastest or cheapest execution within boundaries). Moreover, during the execution of the workload, SpotADAPT suggests a redeployment if the current spot instance gets terminated by Amazon or a better deployment becomes possible due to fluctuations of the spot prices. The approach is evaluated using the actual execution times of typical analytical workloads and real spot price traces. SpotADAPT's suggested deployments are comparable to the theoretically optimal ones, and in particular, it shows good cost benefits for the budget optimization - on average SpotADAPT is at most 0.3% more expensive than the theoretically optimal deployments.
Starting Page 59
Ending Page 68
Page Count 10
File Format PDF
ISBN 9781450337854
DOI 10.1145/2811222.2811227
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-10-22
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Execution time estimation Ec2 Amazon web services Hadoop Spot instances
Content Type Text
Resource Type Article
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