Download - Providing Performance Guarantees to Virtual Machines using Real-Time Scheduling

Tommaso Cucinotta – ReTiS Lab – Scuola Superiore Sant'Anna – Pisa – Italy 1/24

VHPC 2010VHPC 2010August 31August 31stst, Ischia, Italy, Ischia, Italy

Providing Performance Guarantees to Providing Performance Guarantees to Virtual Machines Virtual Machines using Real-Time using Real-Time

SchedulingScheduling

Tommaso CucinottaTommaso Cucinotta, Dhaval Giani,, Dhaval Giani,Dario Faggioli, Fabio ChecconiDario Faggioli, Fabio Checconi

Real-Time Systems LaboratoryReal-Time Systems LaboratoryScuola Superiore Sant'AnnaScuola Superiore Sant'Anna

Pisa, ItalyPisa, Italy


Introduction and MotivationsIntroduction and Motivations


IntroductionIntroduction

““Traditional” worlds of computingTraditional” worlds of computing General-Purpose Computing (GPC) and servers

Low-cost, low parallelism degree equipment Focused on mixing batch and interactive workloads

High-Performance Computing (HPC) High-cost, massively parallel and vector-based equipment Focused on batch computing, tightly coupled parallel tasks,

scientific applications

New trendsNew trends Affordable many-core systems also for GPC Cloud-Computing: world-level scalability and replicability

Use of high-performance hardware in CC applications Interest in mixing interactive/real-time and HPC workloads



Virtualization is a key technologyVirtualization is a key technology For IaaS providers (Cloud Computing) For server consolidation

Physical HostPhysical Host

OSOS

. . .


OSOS



Virtualization is a key technologyVirtualization is a key technology For IaaS providers (Cloud Computing) For server consolidation


OSOS

. . .


OSOS


VM/OSVM/OS

VM/OSVM/OS

. . .


Need for Performance IsolationNeed for Performance Isolation

Resource sharingResource sharing→ Temporal interference


VMVM

VMVM

VMVM

VMVM





VMVM

VMVM

VMVM

VMVM

VM Alone

~30ms





VMVM

VMVM

VMVM

VMVM

VM Alone

2 VMs

~30ms

~120ms


Possible SolutionPossible Solution

Hardware replication andHardware replication andstatic partitioningstatic partitioning Computing

Multi-core (1 core per VM) Networking

Multiple network adapters(1 network adapter per VM)

Multi-queue adapters

DrawbacksDrawbacks Limitation of flexibility Under-utilization of resources

e.g., with real-time/interactive workloads(cloud computing)


VMVM

VMVM

VMVM

VMVM


Possible SolutionPossible Solution

Another approachAnother approach Let multiple VMs use the same resources Use proper resource scheduling strategies

For exampleFor example Computing

Xen credit-based and SEDF schedulers Networking

QoS-aware protocols (IntServ, MPLS)


What is still missing ?What is still missing ?

Most approachesMost approaches Investigate on high-level load balancing techniques

Without the necessary care for fine-grain resource scheduling Focus on fairness among multiple VMs

Only a few worksOnly a few works Focus on providing precise QoS guarantees to VMs For example

Gupta et al., “[...] Performance Isolation […] in Xen” Cherkasova et al., “Comparison of the 3 CPU Schedulers in Xen”

Our focusOur focus Network performance isolation among concurrent VMs Virtualization with a host OS

Specifically, on Linux as host OS with KVM


Proposed ApproachProposed Approach


Proposed ApproachProposed Approach

Use of real-time CPU schedulingUse of real-time CPU scheduling As basic mechanism for isolating VMs concurrently

running on the same CPU and core

IRMOS Real-Time SchedulerIRMOS Real-Time Scheduler For the Linux kernel Provides hierarchical EDF/FP scheduling

EDF-based resource reservations– (Q, P): a budget Q is granted every period P– Both a guarantee and a limitation

FP scheduling within each EDF reservation Provides temporal isolation


What can we achieve ?What can we achieve ?(by CPU real-time scheduling)(by CPU real-time scheduling)

CPU real-time scheduling achievesCPU real-time scheduling achieves Performance isolation of

compute-intensive VMs Shown in other papers

What about network-intensive VMs ?

ProblemsProblems On the host OS (Linux)

network-intensive VMs impose a big interrupt workload– difficult to quantify and keep under control– steals CPU from reservations of other VMs

How can we fix this ?


Preliminary SolutionPreliminary Solution

Preliminary solution (subject of this paper)Preliminary solution (subject of this paper) Temporal isolation of VMs by real-time scheduling

Scheduling parameters tuned according to benchmarked figures– Reservation period set according to responsiveness requirements– Budget needed for computations, plus– Budget needed for sustaining network traffic

Budget over-provisioning According to the foreseen interference due to

network-intensive VMs


Experimental ResultsExperimental Results


Experimental ResultsExperimental Results(Q6600 @ 2.4 GHz, 1-Gbit Card)(Q6600 @ 2.4 GHz, 1-Gbit Card)

Achievable network throughput as a function of Achievable network throughput as a function of the CPU share reserved to the VMthe CPU share reserved to the VM Measured via iperf: almost linear, as expected


Experimental ResultsExperimental Results(2 VMs on the same core)(2 VMs on the same core)

Throughput as a function of its own budgetThroughput as a function of its own budget 13% drop when

own reservation is 35% (from ~300 Mbps to ~260 Mbps) and reservation of other VM increased from 10% to 40%



Throughput as a function of its own budgetThroughput as a function of its own budget ~14% budget over-provisioning needed

for sustaining a ~300 Mbps throughput when reservation of other VM increased from 10% to 40%



Throughput as a function of the budget reserved Throughput as a function of the budget reserved to the other interfering VMto the other interfering VM Drop due to compute-intensive interfering VM lower than

the one due to network-intensive interfering VM


Experimental ResultsExperimental Results(application-level benchmark)(application-level benchmark)

Download time for a 100 KB file from ApacheDownload time for a 100 KB file from Apache Periodic download requests every 20ms Response-times may be kept much more stable

by real-time scheduling


Conclusions and Future WorkConclusions and Future Work


Conclusions and Future WorkConclusions and Future Work

ConclusionsConclusions We showed how to achieve network

performance isolation by CPUreal-time scheduling

Technique to be used jointly withtraffic-shaping techniques

Planned Future WorkPlanned Future Work Experiment with PREEMPT_RT

IRQs handled in kernel threads Investigate on task-level isolation of network traffic of

different VMs (via real-time scheduling) Make KVM a QoS-aware hypervisor Investigate on the use of Adaptive Reservations


Thanks for your attention!Thanks for your attention!

Questions ?Questions ?