Managing elasticity across Multi-cloud providers
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Transcript of Managing elasticity across Multi-cloud providers
Managing Elasticity Accross
Multi-Cloud Providers
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Fawaz Paraïso, Philippe Merle, Lionel Seinturier1st International workshop on multi-cloud applications and federated clouds
(2013)
University Lille1 & Inria Lille – Nord Europe (France)
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Introduction & Motivation
Challenges
Contribution
Implementation
Validation
Conclusion
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Agenda
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Introduction & Motivation
Elasticity is the capability to rapidly provision, in some
cases automatically, to quickly scale out, and rapidly
release resourcesAPP
APP
APP
APP
APP
APP
APP
APP
APP
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Introduction & Motivation
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Introduction & Motivation
Cloud provider What happened & why What impact When
Amazon [8] The Amazon Elastic
Load Balancing (ELB)
Service down in US-East
region affected the
applications using the
ELB.
21 April 2011 Offline for more
than 10 hours.
Companies
affected:
reddit, Quora, Hoot
Suite.
Windows Azure [9] A networking problem
during a routine software
update interfered with
hosted project
deployment.
13 March 2009 Offline for 22
hours.
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Introduction & Motivation
Cloud provider Electricity provider
Unavailability: 7.5 hours average per year Unavailability: 15 minutes average per year
Total of 585 hours cost > 71.7 million [1]
[1] International Working Group on Cloud Computing Resiliency. http://www.iwgcr.org
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Introduction & Motivation
Multiple servers #1
Data centre
Cloud provider
Load balancer
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Introduction & Motivation
Multiple data centres #2
Load balancer
Data centre A Data centre B Data centre C
Cloud provider
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Usage of multiple cloud providers in a uniform way.
Multi-Cloud
service
Cloud B Cloud CCloud A
Multiple Clouds #3
Introduction & Motivation
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Introduction & Motivation
Challenges
Contribution
Implementation
Validation
Conclusion
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Agenda
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Challenges
How to guarantee high availability?
How to automate elasticity through multiple clouds?
How to provide transparency?
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Introduction & Motivation
Challenges
Contribution
Implementation
Validation
Conclusion
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Agenda
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Contribution
Manage elasticity problem in Multi-Cloud environment
Provide necessary resources when the system needs
Full instrumentation for monitoring workloads
Unpredictible environment
Why ?What ?
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Contribution
Overview of the Multi-Cloud-PaaS Architecture
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Contribution
Deployment of the Multi-Cloud-PaaS Architecture
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Contribution
Independent of Cloud
Flexible architecture
Automation
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Introduction & Motivation
Challenges
Contribution
Implementation
Validation
Conclusion
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Agenda
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Implementation
Implementation
FraSCAti (SCA Model)
Multi-Cloud-PaaS SCA-Based Components
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The Multi-Cloud-PaaS (MCP) has been deployed on
ten IaaS/PaaS providers.
Deployment
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Use case
Application responsible for checking if the JPG format is correct.
App
First case
App
Second case
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EvaluationOverhead
Implementation Avg. exec. Time LB overhead
APP 13.93 sec -
APP + LB 14.10 sec 1.45%
There is a negligeable overhead introduced by the LB
First case
Second case
To evaluate the overhead of the LB instance, 10,000 pictures were
sent to the application.
Performance
We generate 134, 021 requests and continuously connects to one
instance of LB
Session rate Concurrency Data rate Failures Avg. Time
850 283 4560 kB/s 0 3 ms
Given the low resources used by the LB, the results obtained in are satisfactory
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Conclusion
This paper provides solution to manage elasticity
across multiple cloud providers
Independent of Cloud
Flexible architecture
We plan to
Evaluate the other Multi-Cloud-PaaS architecture
components
Investigate for optimization opportunities of cloud
application deployment