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An Energy-efficient Scheduling Policy for Hypervisors on Asymmetric Multi-core

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An Energy-efficient Scheduling Policy for Hypervisors on

Asymmetric Multi-core

Ching-Chi Lin, Yi-Chung Chen

Institute of Information Science, Academia Sinica

Department of Computer Science and Information Engineering, National Taiwan University

You-Cheng Syu, Pangfeng Liu

Department of Computer Science and Information Engineering, National Taiwan University Graduate Institute of Networking and Multimedia, Nation Taiwan University

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Outline

Introduction

◦ Background

◦ Motivation

Virtual Core Scheduling Problem

◦ Model

◦ Solution

Evaluation

Conclusion

(3)

Background

Asymmetric multi-core architecture.

◦ Consists of cores with the same ISA but different computing capabilities and power characteristics..

ARM (big.LITTLE), Qualcomm (aSMP), Nvidia (vSMP), Samsung, MediaTek…etc.

◦ Aim to achieve both performance and energy- efficient.

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Motivation

Design new scheduling algorithm for asymmetric multi-core platforms.

◦ Exert the advantage of different types of cores.

◦ Maximize power efficiency with modest performance sacrifices.

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Hypervisor Scheduler

Assigns the virtual cores to physical cores for execution.

◦ Determines the execution order and amount of time assigned to each virtual core

according to a scheduling policy.

◦ Current solutions

Xen - credit-based scheduler

KVM - completely fair scheduler

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Current Load-balancing Design

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VM2 VM1

vCore2

Load-balanced

Hypervisor Scheduler

Power- efficient

Core

vCore3

Power- efficient

Core

Performance Core

Performance Core

vCore0 vCore1

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Proposed Design

VM2 VM1

vCore2

Asymmetry-aware Hypervisor Scheduler

Power- efficient

Core

vCore3

Power- efficient

Core

Performance Core

Performance Core

vCore0 vCore1

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Goal

Design and implement a new hypervisor scheduler for asymmetric multi-core

platform.

◦ Achieve energy-saving while satisfying the resource requirement of each virtual core.

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Assumptions

The scheduling policy in the guest OS is already asymmetry-aware.

The hypervisor is aware of the frequency

of each virtual core.

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Outline

Introduction

◦ Background

◦ Motivation

Virtual Core Scheduling Problem

◦ Model

◦ Solution

Evaluation

Conclusion

(11)

Virtual Core Scheduling Problem

For every time period, given the operating frequency of each virtual core, the

scheduler has to generate a scheduling plan such that

◦ The power consumption is minimized.

◦ Satisfy the resource requirements of virtual cores.

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Scheduling plan

The amount of time each virtual core should run on each physical core.

The execution order of virtual cores on each physical core.

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Power Model

Relation between power consumption, core frequency, and load.

◦ bzip2

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Computing Resource

Number of CPU cycles.

◦ Multiplying the frequency by time.

Our scheduling plan must satisfies the

resource requirement of each virtual core.

◦ Unless “fully utilized”.

Resource required from vCPUs > resources provided by pCPUs.

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(15)

Optimization Problem

Objective function:

n: number of physical core

Generate a set of a

i,j

.

◦ ai,j:the amount of time executing virtual core j on physical core i in a time interval.

◦ Some constraints.

) min(

1

= n

i

Poweri

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Solution

Three phase scheduling

◦ Phase 1:generate the amount of time each virtual core should run on physical cores.

◦ Phase 2 & 3: determine the execution order of virtual cores on a physical core.

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Phase 1

Generate the amount of “time slice” each virtual core should run on physical cores.

Linear programming Greedy heuristic

◦ Assign workloads to the most energy-efficient physical core with available resources.

pCPU 1 pCPU 2 pCPU3

vCPU 0 60 0 0

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Phase 2

Determine the execution order of virtual cores on a physical core according to the result of phase 1.

“Open Shop Scheduling Problem”

◦ Can be solved in polynomial time if jobs can preempt each other.

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Phase 3

Reorder the Execution Slices to reduce

migration overhead.

(20)

Outline

Introduction

◦ Background

◦ Motivation

Virtual Core Scheduling Problem

◦ Model

◦ Solution

Evaluation

Conclusion

(21)

Experimental Environment

ARM Juno board

2 performance core A57

4 power-efficient core A53

Xen 4.5.0-rc

Dom0: dual-core VM

Dedicate 2 power-efficient to Dom0 VM.

Workload: Coremark

Light, medium, heavy

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Workload Setting

Two DomU: dual-core VMs.

Two sets of input:

Case 1: Both VMs with light workloads.

Case 2: One VM with medium workloads, the other with heavy workloads.

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Results

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Results(Cont.)

◦ Case 1: asymmetry-aware method is about 50.4% of that of credit-based method.

◦ Case 2:asymmetry-aware method uses 70.8%

of energy used by the credit-base method.

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Energy(J) Time(Sec.)

Case 1 Asymmetry-aware 4.948 27

Credit-based 9.817 25.2

Case 2 Asymmetry-aware 24.775 71

Credit-based 34.890 63

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Outline

Introduction

◦ Background

◦ Motivation

Virtual Core Scheduling Problem

◦ Model

◦ Solution

Evaluation

(26)

Conclusion

We develop an energy-efficient scheduler for asymmetric multi-core platforms.

◦ Generates energy-efficient scheduling plans that satisfy virtual core resource

requirements.

Will keep improving the scheduler and the related issues.

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Thank you!

參考文獻

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