Self-Adaptive SLA-Driven Capacity Management for Internet Services
Bruno Abrahao, Virgilio Almeida, Jussara AlmeidaFederal University of Minas Gerais, Brazil
Alex Zhang, Dirk Beyer, Fereydoon SafaiHewllet-Packard Labs Palo Alto, CA
IEEE NOMS 20066 April, 2006
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Motivation• IT outsourcing for Internet Services − Contracts with a provider− Multiple service shared Internet Data Centers (IDC)
• Providers’ challenging task− cost effectiveness while satisfying the customers’ SLA
requirements
• Complexity− Keep track of different application requirements, systems
characteristics, and simultaneous workload variations, as well as (and more importantly!) to consider the business goal of the provider
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Challenges
Probabilistic performance requirements
Per use service
accounting
Multiple metric requirements
High workload fluctuations
Unexpected workload peaks
Application Heterogeneity
• New customer demands
• Application characteristics
• manual management becomes impractical
• even more complex business and systems models
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Goal• To present a self-adaptive capacity
management scheme for IDCs which aims at maximizing the service revenue of the provider
−Take into account the new challenges of the modern IT business and infra-structure
−Allows providers to offer customers flexible service plans
−Minimize management costs for service providers
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IDC Environment
• VMs provide admission control mechanisms
• Virtualization• Transparent and flexible
capacity expansion/ contraction.
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Self-Adaptive Framework
• Control Interval
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Capacity Manager Scheme• Provides IDC configurations that maximize the
business objective of the provider
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Cost Model
• Allows per-use service accounting− Customers pay for extra capacity (than that normally
required) only when needed
• Service accounting− performance achieved by virtual machines instead of
simply accounting for resource utilization
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Cost Model• Allows probabilistic response time requirements
• Allows multiple metric service level
− Throughput, subjected to a guarantee in the response time of the processed transactions
})(|{ SLARRPX
iSLAii RRP )(
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Cost Model
Two-level SLA contracts- Normal operation mode
- Surge operation mode
Penalty/Reward model
Provider’s business objetive
Maximize the net result from the penalties and rewards
Extra processing limit
Normal processing requirement
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Performance Model
• application system characteristics
• performance requirements
• current workload intensity
Performance Model
Capacity allocation decision
• Throughput
• Utilization
• Response time probability distribution
• Based on queuing-theory
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Performance Model
• Utilization and Throughput can be estimated using well-known queuing-based formulas
• Approximations are often needed to estimate Response time probability distribution
− Markov
− Chebyshev
− Percentile (M/M/1)
SLAi
iSLAii R
RERRP
][)(
2])[(
]var[)(
iSLA
iSLAi RER
RRRP
)1])([/()( iiiSLAi SEfRSLA
ii eRRP
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Optimization model
{Cost Model
{Perf. Model
Capacity allocation
Provider’s business objective
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Experimental Analysis• Self-adaptive versus static configuration
− Examine the resulting provider’s payoff − Examine whether performance requirements are met and
queue stability is maintained
• Compare the degree of accuracy provided by each of the performance approximations
• how− Simulate and analyze the behavior of two competing
applications that receive different workloads levels over time
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Experimental Analysis
• Net result of the provider (M/M/1)
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Experimental Analysis
• Theoretical value:
• Queue size M/M/1
05.1895.01
)95.0(
1
22
i
iiQ
17
Experimental Analysis
• Requirement:
• Response time M/M/1
10.0)1.0( RP
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Experimental Analysis• Penalty/Rewards M/M/1
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Conclusions• The self-adaptive capacity management
model with any of the approximations is able to − increase the business potential of the provider
− Higher payoffs
−maintain the application stability− Stable service queues− Response time requirement satisfaction
−Markov’s approximation overestimates capacity needs−Chebyshev e Percentile result in a equivalent
degree of precision in M/M/1 model• Allows for the new challenges of the
problem
Self-Adaptive SLA-Driven Capacity Management for Internet Services
Bruno Abrahao, Virgilio Almeida, Jussara AlmeidaUniversidade Federal de Minas Gerais, Brazil
Alex Zhang, Dirk Beyer, Fereydoon SafaiHewllet-Packard Labs Palo Alto, CA
IEEE NOMS 20066 April, 2006
Time for questions
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Backup slides
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Experimental Analysis
• Two similar applications
• Service demand: sec10][ 3iSE
• Experimental setup
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Environment
• utilization = busy time / total time
• Virtualization
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Cost Model
Y
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Cost Model
NSLAXY
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Cost Model
NSLAXZ
27
Cost Model
NSLASSLA XXZ
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Net result M/M/1 and M/G/1 PS
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Experimental Analysis• Queue size M/G/1 (PS)
05.1895.01
)95.0(
1
22
i
iiQ
• Theoretical value:
30
Experimental Analysis
• Requirement:
• Response time M/G/1 (PS)
10.0)1.0( RP
31
Experimental Analysis
• Penalty/Reward M/G/1 (PS)
