Self-Adaptive QoS Guarantees and Optimization in Clouds Jim (Zhanwen) Li (Carleton University)...

Self-Adaptive QoS Guarantees and Optimization in Clouds

Jim (Zhanwen) Li (Carleton University) Murray Woodside (Carleton University)

John Chinneck (Carleton University) Marin Litoiu (York University)

Outline(CERAS) Cloud OverviewOptimization for Clouds: DefinitionOptimization MethodCase Studies

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Centre of Excellence for Research in Adaptive Systems

ParticipantsIBMOntario GovernmentOntario Cancer InstituteUofWaterloo, UofToronto, Queen’s, Carleton, York University

Three complementary research thrusts to enable cloud computingService and resource virtualization: what do we offer in a cloud?Programming models for web services: how do we add value?Adaptive computing: how do we manage the cloud?

DeliverablesA cloud infrastructure (CERAS Cloud)Algorithms and methods to manage the cloud infrastructureServices

Tools in cloudDesktops Web services and applications

Demonstrate how emerging application can be run more effectively in a cloud infrastructure

Cloud = SaaS+Virtualization …

Delivering software functionality online, similar to the one installed on your machine. Flavours

Infrastructure as a ServiceSoftware as a ServicePlatform as a ServiceDesktop as a Service

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– Pricing models

• pay per usage ( Amazon)

• pay a subscription (Microsoft and

Salesforce)

• pay per transaction (Expedia)

• use it for free (Google)

DesktopOffice

DatabasesOS

Network DatabaseStorageCPU

ServersWeb servers

ERP CRM Software dev tools

Management in CERAS Cloud

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Two Deployment Scenarios

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Optimization for CloudsNecessities

Economic: cost and profit; time: installation, maintenance Quality of maintenance and configurations

ChallengesScalability and complexity:

thousands to millions of various decisions. Service selection, Service deployment, Workloads balanceOptimization must be efficient enough for real-time management.

Guaranteed QoS: SLA to workloads and components, software and hardware delays

Constraints from system and businesscapacity and availability of resources and budgets

Interaction of configurationsDynamic in virtualization

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Feedback Control for QoS and Optimization

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1. Karman Filter for estimation and prediction2. Quantitative model : Performance Model, 3. Qualitative model: an Optimization Model that can be

solved effectively and efficiently4. Execute Optimal decisions

Model-based Optimization Architecture

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Service System Metamodel

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Scalable Network Flow Model

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• NFM presents the states of the system as an Optimization Model• Parameters of the NFM are updated at runtime• Scalable to millions of configurations and decisions

• Applications: Decisions among replication, migration, on or off etc• Workloads: Dynamic workload management• Resources: License/memory/CPU requirements and availability• Costs (or profits): penalization and rewards• QoS management

Optimization Loop

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1. Network optimization allocates the flows to optimize costs and meet average delay constraints, without knowledge of contention delay

2. LQN performance model calculates contention delay

3. Contention delay is inserted into the network model and allocation is iterated

4. Result is an allocation that minimizes costs and meets delay constraint, including contention.


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Simplified Cost ModelAssumption: no request cyclesObjective Function

Constraints

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Case Study I: Min RT, Min Cost

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Case Study II: ScalabilityConsider a cloud with many services all structured like this one

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A fragment of the network flow model

Case Study II cont

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Full optimization can save around 20% hosts in useFull optimization can significantly save costshowever, full optimization may increase the cost of contentions.

Utilizations are increased to the desired upper bound in Full Optimization

ConclusionsThe combination of NFM and nonlinear performance model

Effectively optimizing many interacted configurations subject to quite a few QoS and economic constraints

New optimization algorithmsScalability, Efficiency, Flexibility, Autonomic Tuning

Full optimization is best but it is less practicalRisks and overhead

In practice, cloud administrators will settle with incremental optimization

and launch full optimization when the COST becomes high

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Self-Adaptive QoS Guarantees and Optimization in Clouds Jim (Zhanwen) Li (Carleton University)...

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