self-adaptive sla-driven capacity management for internet services
DESCRIPTION
This work considers the problem of hosting multiple third-party Internet services in a cost-effective manner so as to maximize a provider’s business objective. For this purpose, we present a dynamic capacity management framework based on an optimization model, which links a cost model based on SLA contracts with an analytical queuing-based performance model, in an attempt to adapt the platform to changing capacity needs in real time. In addition, we propose a two-level SLA specification for different operation modes, namely, normal and surge, which allows for per-use service accounting with respect to requirements of throughput and tail distribution response time. The cost model proposed is based on penalties, incurred by the provider due to SLA violation, and rewards, received when the service level expectations are exceeded. Finally, we evaluate approximations for predicting the performance of the hosted services under two different scheduling disciplines, namely FCFS and processor sharing. Through simulation, we assess the effectiveness of the proposed approach as well as the level of accuracy resulting from the performance model approximations.TRANSCRIPT
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
25
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
29
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)