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 Overview Optimization for Clouds: Definition Optimization Method Case Studies 2

Centre of Excellence for Research in Adaptive Systems Participants IBM Ontario Government Ontario Cancer Institute UofWaterloo, UofToronto, Queen’s, Carleton, York University Three complementary research thrusts to enable cloud computing Service 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? Deliverables A cloud infrastructure (CERAS Cloud) Algorithms and methods to manage the cloud infrastructure Services Tools in cloud Desktops 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 Service Software as a Service Platform as a Service Desktop as a Service 4 – Pricing models pay per usage ( Amazon) pay a subscription (Microsoft and Salesforce) pay per transaction (Expedia) use it for free (Google) DesktopOffice Databases OS Network Database Storage CPU Servers Web servers ERP CRM Software dev tools

Management in CERAS Cloud 5

Two Deployment Scenarios 6

Outline (CERAS) Cloud Overview Optimization for Clouds: Definition Optimization Method Case Studies 7

Optimization for Clouds Necessities Economic: cost and profit; time: installation, maintenance Quality of maintenance and configurations Challenges Scalability and complexity: thousands to millions of various decisions. Service selection, Service deployment, Workloads balance Optimization must be efficient enough for real-time management. Guaranteed QoS: SLA to workloads and components, software and hardware delays Constraints from system and business capacity and availability of resources and budgets Interaction of configurations Dynamic in virtualization 8

Feedback Control for QoS and Optimization 9 1.Karman Filter for estimation and prediction 2.Quantitative model : Performance Model, 3.Qualitative model: an Optimization Model that can be solved effectively and efficiently 4.Execute Optimal decisions

Model-based Optimization Architecture 10

Service System Metamodel 11

Outline (CERAS) Cloud Overview Optimization for Clouds: Definition Optimization Method Case Studies 12

Scalable Network Flow Model 13 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 14 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.

Outline (CERAS) Cloud Overview Optimization for Clouds: Definition Optimization Method Case Studies 15

Simplified Cost Model Assumption: no request cycles Objective Function Constraints 16

Case Study I: Min RT, Min Cost 17

Case Study II: Scalability Consider a cloud with many services all structured like this one 18 A fragment of the network flow model

Case Study II cont 19 Full optimization can save around 20% hosts in useFull optimization can significantly save costs however, full optimization may increase the cost of contentions. Utilizations are increased to the desired upper bound in Full Optimization

Conclusions The combination of NFM and nonlinear performance model Effectively optimizing many interacted configurations subject to quite a few QoS and economic constraints New optimization algorithms Scalability, Efficiency, Flexibility, Autonomic Tuning Full optimization is best but it is less practical Risks and overhead In practice, cloud administrators will settle with incremental optimization and launch full optimization when the COST becomes high 20

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