Copyright 2010 ITRI 工業技術研究院 11 ITRI Cloud OS & Virtual Resource Management Patrick Fu...

Copyright 2010 ITRI 工業技術研究院 11

ITRI Cloud OS & Virtual Resource Management

Patrick FuSystem Software Division, CCMA/[email protected]


Agenda

• Cloud OS introduction• Physical resource provisioning• Virtual resource management• Adaptive provisioning and power management


What is Cloud OS ?

Physical Node

Physical Node

Storage

Server

Storage

Server

Physical Node

Physical Node

Storage Server

Storage Server

Mail Virtual Cluster

Compute Nodes

Backup Virtual Cluster

HC Virtual Cluster

AppX Virtual Cluster

Data NodesService Nodes

System

Service daemons

System

Service daemons

Cloud OS agents

Cloud OS agents

• System Management Software layer– Physical Resource Provisioning– Virtual Resource Management

• Improve manageability of massive Cloud Data Center

• Enhance self-provisioning• Optimize physical resource utilization• High Availability for any single point

of failure• Energy management

– Highly Available Distributed Storage Management

– Service Load Balancing– Security– High Speed Networking

• What is it not?– It’s not Operating System– It’s not Virtualization Hypervisor


Service/Technology Mapping

IaaS

PaaS

Servers Storage Arrays Power DistributionSwitches

+Scalable System Architecture System Management Cooling

Cloud Hardware Platform

Hypervisor Virtualization Mgmt Storage Mgmt Security

Backup/Replication Data Center Automation Energy Management

Cloud System Software Platform

LAMP .NET WebSphere WebLogic Google App Engine

Cloud Application Middleware Platform

SaaS Automated Cloudification TechnologyApplications


Software Stack for Cloud OS

Physical ClusterDeploymentTool

Physical ClusterDeploymentTool

Virtual Machine ManagementVirtual Machine Management

Virtual Cluster ProvisioningVirtual Cluster Provisioning

PowerManagement

PowerManagement

Intra-Virtual-ClusterLoad Balancing

Intra-Virtual-ClusterLoad Balancing

System/NetworkManagement

System/NetworkManagement

SecuritySecurity

Virtual DataCenter Mgmt ConsoleVirtual DataCenter Mgmt Console

Physical Compute ServersPhysical Compute Servers

All-layer-2 Network All-layer-2 Network Distributed Main/Secondary StorageDistributed Main/Secondary Storage


Cloud OS Service Model

• Provisioning & Runtime monitoring of Virtual Resources– Virtual Instance

Hypervisor construct An image of a guest OS

– Virtual Cluster A group of VM instances providing same service, front-ended by a network

load balancer Configuration

- # of virtual machines and its configuration- Storage space requirement- External network bandwidth requirement- Load balancing policy- Firewall/IDS setting- Network configuration, including DNS and DHCP- OS image and application image

– Virtual Data Center One or more virtual cluster working in coordination (multi-tier web services,

EMR’s, VDI’s, etc)


CloudOS Virtualization Level

… PM

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

…

PM

OS

AP

s

vmO

SA

Ps

vmO

SA

Psvm

OS

AP

s

vm

…

PM

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

…

PM

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

OS

AP

s

vm

…

VCluster VCluster VCluster

VDC VDC

CloudOS


Resource Provisioning

• To prepare VMs with appropriate resources and make them ready for user applications– Allocating resources to VMs to match the workloads

• To prepare a virtual cluster with appropriate instances and make it ready for virtual cluster computation– Consolidating VMs onto physical servers

• Goals: – High resource utilization– Energy efficiency– Low performance interference


Provisioning Challenges• VM size estimation

– Static SLA model/forecasting future use• Placement

– Deploy a VM onto physical servers (initially)– Policy: immediate, best effort, advance reservation, etc.

• Consolidation and Load Balancing– High consolidation ration and resource utilization – low cost of running data center– Statically

• Heuristic based• Average resource utilization

– Dynamic replacement• Measure-Forecast-Remap (MFR)• Live migration• Balancing overloaded and underloaded nodes

– Constrained bin packing problem w/ SLA• Performance isolation

– Cohosting VMs on a server creates performance interference– How to model and prevent the interference


RPM Static Resource Provisioning

• Statically provision from SLA• SLA w/ historical data?

– No, conservatively allocation– Yes, forecasting joint-VM provisioning

• Immediate provisioning model (before instantiation of virtual machine)

• Placement policy– Proprietary – Virtual cluster affinity placement policy

• Performance gain from locality • Place VMs from the same virtual cluster as possible• Need experiments to support

CloudOS


Our Motivation

Data Center Monthly Cost

54%

8%

21%

13% 5%

Servers

NetworkingEquipment

Power Distribution &Cooling

Power

Other Infrastructure

Source: Cost of power in Large-Scale Data Center, James Hamilton Blog, 11/28/2008


Joint Provisioning via VM Multiplexing

• Dataset from a commercial data center– 15,897 VMs– 1325 physical hosts

• 94% of the hosts have more than one VM

• Joint provisioning averagely saves 40% of the capacity

Meng, X. et al. Efficient Resource Provisioning in Compute Clouds via VM Multiplexing. ICAC ‘10


Joint-VM Provisioning at Runtime

Hyp

ervisor

PM

VM

Hyp

ervisor

PM

VM

100%100%

Under provisioning Over provisioning

CloudOS

CapacityCapacity

tt

tt

CapacityCapacity


Load balancing and DVMM

Consolidation manager/ DVMM

PM PM PM PM PM PM

Over provisioningUnder provisioning

Joint-VM histogramVM victim histogram

Resource Provisioning Manager (RPM)

PM reconfiguration

Utilization ratio

Reach reconfiguration point

CloudOS


Adaptive physical resource provisioning

PRM

Power on/off PMs

Reconfiguration map

New PM map

Utilization rate reaches threshold, sending reallocation request

Static joint-VM provisioning

DVMM

Consolidation manager

Placement

VM monitoring

Runtime joint-VM

provisioning

Performance interferenceUtilization changeVictims

RPM core

New PM map

Cloud OS RPM Software Components

Cloud OS


Load balancing


Consolidation plan


Migration plan


Runtime Reallocation

VM3

VM2

VM1

PM1

VM3

VM2

VM1

PM2

VM3

VM2

VM1

PM3

VM1

PMi

VM1

PMj

VM1

PMk

VM3

VM1

PM1

VM3

VM1

PM2

VM3VM2

VM1

PM3

VM1

PMi

VM1

PMj

VM1

PMk

VM2

VM2 VM2


Adaptive Physical Resource Provisioning

Power ManagementPM Pool

Provisioned

Utilization threshold

Low utilization High utilization

Over provisioning Under provisioning

PM reallocation algorithm

PRMPower on/off PMs

Reconfiguration mapVM placement

DVMM live migrationLoad balancer

Consolidation manager Utilization monitor New PM map

Utilization rate reaches threshold, sending reallocation request

CloudOS


Challenges• Triggering mechanism

– No workload consolidation “recently” (e.g. past hour)– No physical machine load balancing going on– No physical server was powered on “recently” (e.g. past hour)– Avoid oscillation

• Cost of migration– Network load– Cache effects– Domain in suspension

• Multi-dimensional bin packing– CPU, Memory, Network, Disk I/O

• Migration plan– Only 1 migration per Physical server @ a time– # of cores vs. # of VMs


Backup


Software architecture


Procedure of Power management in Monitoring thread

Receive data from Dom0Calculate the data

WorkloadTrigger?

Yes

Perform Consolidation Plan

M*K < N ?

No

No

Receiving CCinstance data per 30 secCalculateinstMonitorThreadData->cputotalinstMonitorThreadData->memorytotalinstMonitorThreadData->count

Do Consolidation

Change instance stateinstDvmmBloc->state=doingpwminstMonitorThreadData->state=doingpwminstDvmmBloc->destHost=resource->hostNameChange external state

Call PRM to shut down Machine

Change instance stateinstDvmmBloc->pwmtime=nowinstDvmmBloc->state=pwmdoneinstMonitorThreadData->state=pwmdoneinstMonitorThreadData->pwmtime=now

Stop receiving data from Dom0

Check instMonitorThreadData->stateinstMonitorThreadData->pwmtimeinstMonitorThreadData->lbtimeinstMonitorThreadData->cputotalinstMonitorThreadData->memorytotalinstMonitorThreadData->count

Done

Yes

Do Load balancing

Change instance stateinstDvmmBloc->state=doinglbinstMonitorThreadData->state=doinglbinstDvmmBloc->destHost=resource->hostNameChange external state

Call PRM to turn on Machine

If necessary


Data Mining:VM Resource Usage Patterns of each VC

• Find VM resource usage patterns for each VC

• Aid to predict the trend of resource usages

Medium

LLow

High (or unpredictable)

Time

CPU usage Monday


Q&AThank you!

Copyright 2010 ITRI 工業技術研究院 11 ITRI Cloud OS & Virtual Resource Management Patrick Fu...

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