Paper Info
Title
A Resource Provisioning Strategy for Elastic Analytical Workflows in the Cloud
Abstract
With the advent of big data era, the data analytical applications have sprung up which can be modeled using workflow. Such workflow applications are subject to continuously arriving requests and have a rigid requirement on response time. When running the analytical workflow in a cloud platform, one of the critical questions arising here is that how to provision resources in a way that the monetary cost can be reduced under the constraint of the response time. In this paper, we use queueing network theory to address this challenge. First, we present the performance analytic model for the elastic analytical workflows based on queueing network theory. Then, we design a resource provision strategy to determine the number of virtual machines for individual component of the applications with response time constraint. Simulation experiments using the real workload trace data show that our proposed approach provides a simple yet powerful solution to provision resources for analytical workflows under dynamic workload conditions.
Year
DOI
Venue
2016
10.1109/HPCC-SmartCity-DSS.2016.0082
2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Keywords
Field
DocType
cloud computing,resource provisioning,queueing network theory,analytical workflow
Virtual machine,Computer science,Workload,Provisioning,Real-time computing,Queueing theory,Network theory,Big data,Workflow,Cloud computing,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5090-4298-2
0
0.34
References 
Authors
12
5
Name
Order
Citations
PageRank
Yan Yao100.34
Jian Cao255794.92
SRIKUMAR VENUGOPAL34236294.23
Boualem Benatallah46174494.38
Jinjun Chen541919.81