12UCC 2018

Contents

Dec 18, 2018

Hierarchical and Frequency-Aware
Model Predictive Control for
Bare-Metal Cloud Applications

Yukio Ogawa

Go Hasegawa and Masayuki Murata

Muroran Institute of Technology,
Hokkaido, Japan

Osaka University,Osaka, Japan•

Research Goal, Approach, and Proposals•

Scaling Models•

Evaluation•

Conclusion

34

Background and Goal

Background and Goal•

On-premises business applications use over-provisioned dedicated physicalmachines (PMs), which can be improved by migrating them to a bare-metal cloud.

•On-premises business applications use over-provisioned dedicated physicalmachines (PMs), which can be improved by migrating them to a bare-metal cloud.

•Goal: Development of a scaling mechanism for both PMs and virtual machines(VMs) in a bear-metal cloud•

Goal: Development of a scaling mechanism for both PMs and virtual machines(VMs) in a bear-metal cloudApplication

Application

FW/LB

FW/LB

FW/LB

VM

VM

VM

Physical server
serverpool
pool (( ))Physical
PM cluster(VM pool)
Data base server

Scaled in/out

Application

FW/LB

FW/LB

FW/LB

VM

VM

VM

Physical server
serverpool
pool (( ))Physical
PM cluster(VM pool)

Fixed

Data base server

Databaseservers
server
Database

Scaled in/out
Fixed

FW/LBVM

VMVM

Data base server

Scaled in/out
Scaled in/out

Databaseservers
serverData
base

On-premises private cloud

LB: load balancer, FW: fire wall, VM: virtual machine, PM: physical machine

FW/LB

Physical server
serverpoolpool (( ))Physical
PM cluster(VM pool)Databaseservers
serverData
base

On-premises private cloud

FW/LB

Bare-metal cloud

LB: load balancer, FW: fire wall, VM: virtual machine, PM: physical machine

56Resource Scaling Approach

Resource Scaling Approach•

Proactive resource allocation needs to predict the future demands, butprediction errors result in resource inefficiency.

•

Proactive resource allocation needs to predict the future demands, butprediction errors result in resource inefficiency.

•High control frequency make resource reconfigurations adapt more quickly
to demand changes, but it also increase reconfiguration overheads.

•High control frequency make resource reconfigurations adapt more quickly
to demand changes, but it also increase reconfiguration overheads.

•Model predictive control (MPC ) is applied to balance resource efficiency
against reconfiguration overheads.

•Model predictive control (MPC ) is applied to balance between resource
efficiency and reconfiguration overheads.

(Flash crowd )Time slot→ Extra costs

Increase control frequency

Allocated resources→ SLA violations

Allocated resources

Insufficient resources

Capacity requirements

Time slot

Redundant resources
Capacity requirements→ SLA violations→ Extra costs
Allocated resources

Capacity requirements

Redundant resources
Insufficient resources

Time slot

Control frequency: frequency of making reconfiguration decisions78

Challenges and Proposals

Scaling Model: Physical Machines•

PMs have larger reconfiguration overheads and need a longer leadtime than VMs.

••Challenges

The controller solves the optimization problem to balance theredundant PMs and the reconfiguration overhead.
Objective: minimize

The controller should initiate the reconfiguration process of PMs
before initiating that of VMs•

Excessive reconfigurations should be suppressed for PMs.
Number of PMs

Request arrivals•

Application

ProposalController

Two-level hierarchical MPC with twodifferent reconfiguration weight factorsAllocate/Deallocate

Provision VMs

FW/LB

FW/LB

VM

VM

VM

Physical server
server pool (( ))Physical
PM cluster (VMpoolpool)

Start/Stop

Provision PMs

FW/LB

ControlFrequency fPredict requests

Data base server

Databaseservers
server
Database

Control horizon

Bare-metal cloud
Monitor requests

Redundant PMs(VM pool capacity)•tTime slot

t+Lu +kh

Lead time to deploy PMs

LuObserve request arrival rates

Reconfigured PMs

Prediction horizon for PMsh

LB: load balancer, FW: fire wall, VM: virtual machine, PM: physical machine910

Scaling Model: Physical Machines
The controller solves the optimization problem to balance theredundant PMs and the reconfiguration overhead.
Redundant PMs

Number of PMs(VM pool capacity)

Allocated PMs

Control horizon
Lead time to deploy PMst

Minimum PMs

hLu

Objective: minimize

Reconfigured PMs

Redundant PMs

Allocated PMs

f : Control frequency

Time slot

t+Lu +k

The controller solves the optimization problem to balance theredundant PMs and the reconfiguration overhead.

Number of PMs

Objective: minimize•(VM pool capacity)•Scaling Model: Physical Machines

Control horizon
Prediction horizon for PMs

Lead time to deploy PMs

hReconfigured PMs

Minimum PMs

Reconfiguration weight:
f Wu/Cu

tt+Lu +khLuTime slot
Prediction horizon for PMsh1112

Scaling Model: Virtual Machines•

The controller solves the optimization problem to balance the riskof VM insufficiency the reconfiguration overhead.
Objective: minimize••f : Control frequency
N: Number of VMs per PMs
Control horizon
Lead time to deploy PMs:
Lead time to deploy VMs:

Risk of VM insufficiency

Available VMs

Allocated VMsReconfigured VMs

Reconfiguration weight:
f NWu/Cu
Time slot

Prediction horizon for PMs
Prediction horizon for VMs

Evaluation•

Evaluate the proposed MPC using three HTTP traces from real-world web
application: World Cup, Campus and Video.

•Predict future request arrival rates with ARIMA model.

•Find the optimal numbers of allocated resources using Dynamic Programming.

•Focus on clarifying the effect of high-frequency control with various
reconfiguration weights for PMs.
•Experimental setup

1314

Evaluation

Evaluation•

Evaluate the proposed MPC using three HTTP traces from real-world web
application: World Cup, Campus and Video.

•Evaluate the proposed MPC using three HTTP traces from real-world web
application: World Cup, Campus and Video.

•

Predict future request arrival rates with ARIMA model.

•

Predict future request arrival rates with ARIMA model.

•

Find the optimal numbers of allocated resources using Dynamic Programming.

•

Find the optimal numbers of allocated resources using Dynamic Programming.

•Focus on clarifying the effect of high-frequency control with various
reconfiguration weights for PMs.

•Focus on clarifying the effect of high-frequency control with various
reconfiguration weights for PMs.

Experimental setup
Control frequency•

Lead time1 - 16 time(s) per hour

PMs

Lu

1 - f time slots

VMs

Lv

Length of control and prediction horizon
Reconfiguration weight
Number of VMs per PM

Experimental setup
Control frequency•fhf time slots (fixed)

PMs

Wu/Cu

0.1 - 1.4

VMs

Wv/Cu

f

Lead timeLu

1 - f time slots

VMs

Lv

1 time slot (fixed)

hf time slots (fixed)

Length of control and prediction horizon
Reconfiguration weightN

1 - 16 time(s) per hour

PMs

PMs

Wu/Cu

0.1 - 1.4VMs

Wv/Cu

0.01 (fixed at the lower)

N4 (fixed)

Number of VMs per PM

15Prediction Errors

Example of Allocated Resources

The prediction errors are mainly caused

•

16•

•

by a large spike lasting a few hours in the case of World Cup•

by a fluctuation during several tens of minutes in the cases ofCampus and Video

Capacity requirements and allocated resources over a 1-day period(f = 2, Wu/Cu = 0.7, Lu = 1)
VM pool on
PM cluster

VideoWorld Cup(fewest insuffiecnt VMs)

CampusWorld Cup

Lead time for PM:
f time slots (1 hour)

Best application

Lead time for PM:
1 time slots

Campus(most insufficient VMs)
Worst application

17

Example of Allocated Resources•

Capacity requirements and allocated resources over a 1-day period(f = 2, Wu/Cu = 0.7, Lu = 1)

18

Effect of Control Frequency•

When control frequency (f ) increases (from 1 to 16)

VM pool on
PM cluster

Insufficient VMs caused
by a large spike
World Cup
(fewest insuffiecnt VMs)

,Reconfiguration weight for PMs (Wu /Cu)
Lower (Wu /Cu = 0.1)
Upper (Wu /Cu = 1.4)
VMs
VMs
PMsPMsReconfigurations
VM pool capacity
Insufficient VMs(Best)(Worst)B

C(Best)(Worst)A→ Decreased by highfrequency control.

Best application

Lead time for PM: f
Lead time for PM: f

Insufficient VMs caused
by a fluctuation

BCampus(most insufficient VMs)
Worst applicationA
VM pool capacity

Lead time for PM: 1

Lead time for PM: 1(Best )Insufficient VMs

C(Worst )19

Effect of Control Frequency•Effect of Reconfiguration Weight for PMs(Continued)

When control frequency (f ) increases (from 1 to 16)(Worst)Reconfigurations
VM pool capacity
Insufficient VMs

How much should weight fo factor (Wu/Cu) be given to high-frequency control?

•,

Reconfiguration weight for PMs (Wu /Cu)
Lower (Wu /Cu = 0.1)
Upper (Wu /Cu = 1.4)
VMs
VMs
PMs
PMs
(Best)

20(Best)(Worst)EF

PMs
VMs (best)
VMs (worst)
VM pool capacity(best)
Insufficient VMs(worst)Reconfigurations

D

Reconfiguration weight for PMs (Wu /Cu)
0.2
0.4
0.6
0.8
1.0
1.2
1.4+++++++++++++++++++ Difference of-high frequency-control (f=16)
to the existing settings (i.e., f =1)++
f = 16
f = 16

D
f=1

f=1

f=1
f = 16

FE(Best )VM pool capacity

Insufficient VMs(Worst )PM Reconfigurations(Best )VM Reconfigurations(Worst )21

Effect of Reconfiguration Weight for PMs

22

Conclusion(Continued)•How much should reconfiguration weight for PMs (Wu/Cu) be given?

PMsReconfigurations VMs (best)
VMs (worst)
VM pool capacity(best)
Insufficient VMs(worst)•

Reconfiguration weight factor (Wu /Cu)
0.2
0.4
0.6
0.8
1.0
1.2
1.4+++++++++++++++++++++•f = 16, Lu = 16
f = 16, Lu = 16
f=1f=1

f = 16

VM pool capacity(Best )•Improve the timing of the PM reconfigurations, and increase
the VM reallocations to adjust the redundant capacity among
the applications•Lead to the reduction of VM insufficiency without increasingthe resource redundancy level

Future work•

Evaluations with various control options•

Evaluations with different combinations of the three
applications; each of the combinations has different request
arrival characteristics

f=1
f = 16, Lu = 1

f = 16, Lu = 1

High-frequency control of hierarchical and frequency-aware MPC

Insufficient VMs(Worst )23

Thank you!
Questions?

24

Backup Slides

25

Effect of Control Frequency•

26

Effect of Control Frequency

When control frequency (f ) increases (from 1 to 16),•

When control frequency (f ) increases (from 1 to 16)

Reconfiguration weight for PMs (Wu /Cu)
Lower (Wu /Cu = 0.1)
Upper (Wu /Cu = 1.4)
VMs
VMs
PMsPMsReconfigurations
VM pool capacity
Insufficient VMs

1(Best)(Worst)23(Best)(Worst)(Best)(Worst)(Best)

Reconfigurations
VM pool capacity
Insufficient VMs

45(Worst)

6

VM pool capacity is held, but VM
reallocations become active.

61

532
4

PM Reconfigurations,

Reconfiguration weight for PMs (Wu /Cu)
Lower (Wu /Cu = 0.1)
Upper (Wu /Cu = 1.4)
VMs
VMsPMsPMs

VM Reconfigurations

PM Reconfigurations

VM Reconfigurations