HARNESS project platform demo

1. http://www.harness-project.euhttp://www.harness-project.eu/
The HARNESS Project
Technology Demo
Hardware- and Network-Enhanced Software Systems for Cloud Computing
Mark Stillwell
Imperial College London
Software Carpentry Bootcamp

2. http://www.harness-project.euhttp://www.harness-project.eu/
Introduction
Goal of HARNESS project: allow cloud providers and tenants
take advantage of heterogeneous resources
Key idea: heterogeneous resources as first-class entities
FPGAs, GPGPUs, programmable routers, heterogeneous
storage volumes
Benefits:
offer a wider context to make price/performance trade offs
provide larger scope for resource allocation/optimization
increase application performance
reduce energy consumption and cost profiles

3. http://www.harness-project.euhttp://www.harness-project.eu/
Integration/Testing Testbed@Imperial
axel07
.doc.ic.ac.uk
maia01
.doc.ic.ac.uk
infiniband
GPGPU
SSD
MPC-X
HARNESS
cloud controller
SSD
HDD

4. http://www.harness-project.euhttp://www.harness-project.eu/
The RTM HARNESS Use Case
1. Generates an image of the sub-surface from seismic
survey data
 Processing ~10,000 “shots”
 1 shot = 1 acoustic stimulus +
~16 s recording on ~10,000 hydrophones
2. Embarrassingly parallel between shots
3. Can project full RTM runtime from single shot

5. http://www.harness-project.euhttp://www.harness-project.eu/
HARNESS cloud phases
Two distinctive phases:
–Profiling: application performance model
building
–Production: application runs with objectives
based on performance model from Profiling
phase

6. http://www.harness-project.euhttp://www.harness-project.eu/
Testbed @ Imperial
Axel07
Maia01
AM
Demo Preview: Black-box Profiling
CRS OpenStack
I
R
M
Instance: RTM
VPCU = 2
DFE = 1
Memory = 1GB
Host = Maia01
Front
End
Instance type: RTM
VPCU = 1
Memory = 1GB
Host = Axel07
Director
Configuration 1
• 1 VM: 2VCPUs, 1G RAM,
100MB Disk, 1DFE …
• 1 VM: 1VCPUs, 0.5G
RAM, 200MB Disk, …
Conf 1: Time 1, Cost 1
Conf 2: Time 2, Cost 2
Conf 3: Time 3, Cost 3
Conf 4: Time 4, Cost 4
Conf 5: Time 5, Cost 5
Configuration 2
• 1 VM: 1VCPUs, 2G RAM,
110MB Disk, 1DFE …
• 1 VM: 2VCPUs, 1G RAM,
200MB Disk, …
Configuration 3
• 1 VM: 2VCPUs, 2G RAM,
200MB Disk, 2DFE …
• 1 VM: 1VCPUs, 2G RAM,
100MB Disk, …
Manifest
My zip app
details

7. http://www.harness-project.euhttp://www.harness-project.eu/
DEMO: Cloud Tenant View
Profiling - Performance Model Building
1. User uploads application + manifest via web
frontend
2. Platform creates Application Manager which starts
profiling to build performance model
 Manifest defines the configuration space:
1 and 3 CPU cores, 2 to 4 DFEs
 Each execution runs via CRS + IRMs
3. Tenant can download generated performance model

8. http://www.harness-project.euhttp://www.harness-project.eu/
Step-by-Step Production View
Step1:
User submits
- Application zip
- Application Manifest
- Application performance model
Step2:
Director assesses user’s request,
if ok creates an
Application Manager instance
Step3:
AM requests necessary resources
to CRS
Step4:
CRS requests the resources
through the appropriate IRMs
Step5:
IRM creates instance(s) in the
relative physical resource and
provides appropriate handles to AM
Step6:
AM deploys the application in the
instances created
Step7:
AM, once application up and
running, provides end point to user

9. http://www.harness-project.euhttp://www.harness-project.eu/
Testbed @ Imperial
Axel07
Maia01
Demo Preview
CRS
OpenStack
I
R
M
Instance: RTM
VPCU = 2
DFE = 1
Memory = 1GB
Host = Maia01
Instance type:
Application
Manager
VPCU = 1
Memory = 0.5GB
Host = Axel07
Front
End
Director
• 1 VM: 2VCPUs, 1G RAM,
100MB Disk, 1DFE …
• 1 VM: 1VCPUs, 0.5G
RAM, 200MB Disk, …
SLO
I want run my
zip app with X
performance
and Y budget

10. http://www.harness-project.euhttp://www.harness-project.eu/
Demo: Cloud Tenant View
1. User uploads application + manifest via web
frontend
2. User uploads application performance model
27 configurations: 1 to 4 DFEs, 1GB and 2GB RAM,
10MB/s and 180MB/s storage performance, 1 to 3 CPU
cores
3. User specifies objectives for run
4. Platform indicates whether objectives are satisfiable
5. User triggers execution
6. Application Manager selects resource configuration
and executes process

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Demo: Provider view

12. http://www.harness-project.euhttp://www.harness-project.eu/
Cross-Resource Scheduler: CRS
1. CRS starts with no knowledge about resources
2. IRMs connect to CRS and announce their resources
 IRM-NOVA => server machines
 IRM-SHEPARD => accelerators
 IRM-XtreemFS => storage
3. We issue 4 different reservation requests:
 homogeneous compute resources
 hardware accelerators
 storage volume with performance guarantee
 heterogeneous resources (1 VM + 1 DFE)

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Demo: RTM Application

14. http://www.harness-project.euhttp://www.harness-project.eu/
Conclusion
Goal of HARNESS project: allow cloud providers and tenants
take advantage of heterogeneous resources
Key idea: heterogeneous resources as first-class entities
FPGAs, GPGPUs, programmable routers, heterogeneous
storage volumes
Benefits:
offer a wider context to make price/performance trade offs
provide larger scope for resource allocation/optimization
increase application performance
reduce energy consumption and cost profiles