Performance_and_Cost_Evaluation
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Transcript of Performance_and_Cost_Evaluation
ResearchResearch
University of StuttgartUniversitätsstr. 3870569 StuttgartGermany
Phone +49-711-685 88337 Fax +49-711-685 88472
Santiago Gómez Sáez, Vasilios Andrikopoulos, Michael Hahn, Dimka Karastoyanova, Frank Leymann, Marigianna Skouradaki, Karolina Vukojevic-Haupt
Institute of Architecture of Application [email protected]
Performance and Cost Evaluation for the Migration of a Scientific
Workflow Infrastructure to the Cloud
CLOSER 2015
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Research
Agenda
Motivation The OPAL Simulation Environment Experiments
Methodology & Setup Evaluation Results
Conclusion & Future Work
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Motivation – Simulation Workflows
Workflows comprise set of tasks by means of defining their control-flow data-flow dependencies
Automated & flexible execution of simulation-based experiments Long-running and irregular executions Often comprise data provisioning tasks & complex calculations Wide amount of resources during execution
(1) SimTech Cluster of Excellence: http://www.iaas.uni-stuttgart.de/forschung/projects/simtech/index.php
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Motivation (2) – VM Selection Alternatives
OPAL_SImulation: Sim_Workflow
Apache_Tomcat:Servlet_Container
Simulation_Service:
Web_Service
MySQL: Engine_and_Auditing_DB
AWS_EC2_m3.large: AWS_EC2
Ubuntu13.0:Virt_Linux_OS
interacts-with
(2) Andrikopoulos et al.: Optimal Distribution of Applications in the Cloud. In: Proceedings of CAiSE’14
Apache_ODE:WF_Engine
interacts-with
AWS_EC2_t2.micro: AWS_EC2
AWS_EC2_c3.large: AWS_EC2
AWS_EC2_r3.large:
AWS_EC2
Auditing_Service:
Web_Service
interacts-with ActiveMQ:Message_Broker
alt_hosted_on application specific node application non-specific node alternative nodehosted_on
interacts-with
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OPAL Simulation Environment
KMC-Simulation for Solid Bodies Thermal aging of copper-alloyed steel on an atomistic scale Simulation workflow orchestrates Fortran-based OPAL simulation
services
(3) Sonntag et al.: Workflow-based Distributed Environment for Legacy Simulation Applications. In: Proceedings of ICSOFT’11
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Experiments – Methodology & Setup
Performance & monetary cost trade-off Impact on outsourcing the OPAL Simulation
Environment to the Cloud (IaaS) Evaluate different IaaS VM instances types
Micro General Purpose Compute Optimized Memory Optimized
Impact when scaling the load concurrent scientists (users) equal simulation requirements
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Experiments – Methodology & Setup
Complete simulation stack hosted in one VM On-premise
in-house virtualized environment vs. Off-premise scenarios
Amazon EC2 Windows Azure Rackspace
10 concurrent users sending 10 random & uniformly distributed requests
JMeter 2.9 as load driver Measured latency (ms.) perceived by the end user (scientist) Extrapolated to 1K experiments for monetary cost analysis
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Experiments – VM Instances Type & Prices Jan. 2015
European region (on-premise, AWS & Azure) US region (Rackspace)
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Experimental Results (2) – Cost for 1K experiments
-27% -6%-35%
-3% +9%+8%+60% +57%
+83%-22% -7%-31%
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Findings
Average latency reduced when using compute optimized instances
Increase monetary cost of 9% and 61% in average when using compute optimized and memory optimized VM instances
The monetary cost tends to increase when using Microsoft Azure optimized VM instances
Due to low costs of Rackspace IaaS services and the enhanced performance w.r.t. other scenarios, the total monetary cost is nearly 40% less in average
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Conclusion & Future Work
Report a performance & price analysis for migrating the OPAL Simulation Infrastructure to the Cloud using different IaaS providers and different VM types
Analysis of PaaS & DBaaS offerings Multi-cloud environment Use experimental results to assess application
developers in the (re-)distribution of their application components in Cloud environments
Santiago Gómez SáezE-mail: [email protected] of Architecture of Applications Systems (IAAS)University of Stuttgart (Germany)
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Experiments – On-premise Pricing Calculation (1)
: acquisition cost: maintenance costY: number of years of the server clusterk: cost of the invested capitalTC:
TCPU: total number of CPU cores in the clusterH: expected number of operational hours: expected utilization
(4) Walker: The Real Cost of a CPU Hour. In: IEEE Computer, 42:35-41
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Experiments – On-premise Pricing Calculation Micro Instance
: 8500 U$: 7500 $/year, including personnel, cooling, power, etc.Y: 2.5 years oldk: assumed a cost of 5% on the invested capitalTC:
96153 CPU hoursTCPU: 16H: 6 days/week; 960K CPU hours/year: 80%
= 0.133 U$/h
(4) Walker: The Real Cost of a CPU Hour. In: IEEE Computer, 42:35-41