Cloud platforms are missing out on the revolution in new hardware and network technologies for realising vastly richer computational, communication, and storage resources. Technologies such as field-programmable gate arrays (FPGA), general-purpose graphics processing units (GPGPU), network middleboxes, and solid-state disks promise increased performance, reduced energy consumption, and lower cost profiles. However, their heterogeneity and complexity makes integrating them into the standard cloud Platform as a Service (PaaS) framework a fundamental challenge. This talk introduces the HARNESS project, whose aim is to explore how to bring these innovative and heterogeneous resources into cloud platforms.

1. http://www.harness-project.eu/
The HARNESS Project:
Hardware- and Network-Enhanced
Software Systems for Cloud Computing
Prof. Alexander Wolf
Imperial College London
(Project Coordinator)

2. http://www.harness-project.eu/
HARNESSHARNESS
Software as a ServiceSoftware as a Service
Cloud Market Strata
PaaS
SaaS
Infrastructure as a ServiceInfrastructure as a Service
IaaS
Platform as a ServicePlatform as a Service

3. http://www.harness-project.eu/
 Provider prospective
– minimise ownership costs
– maximise usage
– maximise market growth
PaaS Design Drivers
 Application perspective
– minimise development costs
– minimise operating costs
– maximise performance
standardised APIs
optimal deployment
commoditised resources
virtualised resources
scale out / scale up
data-centre expansion

4. http://www.harness-project.eu/
 Provider prospective
– minimise ownership costs
– maximise usage
– maximise market growth
PaaS Design Drivers
 Application perspective
– minimise development costs
– minimise operating costs
– maximise performance
standardised APIs
optimal deployment
commoditised resources
virtualised resources
scale out / scale up
data-centre expansion
Application requirements Examples
Fast job completion time with
interdependent “big data”
scientific computing
time-series analysis
Fresh results within seconds on-line information retrieval
on-line data analytics
State-of-the-art:
 Optimised for horizontal scale-out
over homogeneous resources
 But insufficient for many applications

5. http://www.harness-project.eu/
Software
switches
FPGAsGPUs ASICs SSDs
Network
middleboxes
. . .
 Provider prospective
– minimise ownership costs
– maximise usage
– maximise market growth
An Innovative Approach to PaaS
the HARNESS project premise
 Application perspective
– minimise development costs
– minimise operating costs
– maximise performance
standardised APIs
optimal deployment
commoditised resources
virtualised resources
scale out / scale up
data-centre expansion
specialised resources
specialised resources

6. http://www.harness-project.eu/
Goal: Programmable and Manageable
GPU-based
parallel-thread
engines
FPGA-based
shared dataflow
engines
Solid-state
disk drives
ASIC-based
OpenFlow
switching fabric
Middleboxes for
in-network aggregation
and storage

7. http://www.harness-project.eu/
Approach: Enrich IaaS and PaaS
 Provide an IaaS layer that can manage
heterogeneous resources
– computation, communication and storage
– resource allocation and scheduling
 Provide a PaaS layer that can exploit
heterogeneous resources
– multi-tenancy
– application development
– cross-resource allocation and scheduling

8. http://www.harness-project.eu/
Driving Use Cases
basis for demonstration and validation
shared memory
cache cache cache
CPUs CPUs CPUs…
I/O
Delta Merge for SAP HANA
in-memory OLTP and OLAP
queries for “big data” analytics
Reverse Time Migration (RTM)
scientific computation for the
geosciences
……
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y E {−1,1}y E {−1,1}
predictpredict
updateupdate
…
f1
fn
y E {−1,1}
predict
update
…
Share state
Aggregate
Iterate
…
Parallelize
…
Preprocess
AdPredictor Machine Learning
open-source “map/reduce”
data-flow distributed computation

9. http://www.harness-project.eu/
Driving Use Cases
basis for demonstration and validation
shared memory
cache cache cache
CPUs CPUs CPUs…
I/O
Delta Merge for SAP HANA
in-memory OLTP and OLAP
queries for “big data” analytics
Reverse Time Migration (RTM)
scientific computation for the
geosciences
……
f1f1
fnfn
y E {−1,1}y E {−1,1}
predictpredict
updateupdate
…
f1
fn
y E {−1,1}
predict
update
…
Share state
Aggregate
Iterate
…
Parallelize
…
Preprocess
AdPredictor Machine Learning
open-source “map/reduce”
data-flow distributed computationO(109) entries in daily Web visit logO(109) entries in daily Web visit log
two weeks on 300 multi-core nodestwo weeks on 300 multi-core nodes
20% of cycles and 10s of seconds locking20% of cycles and 10s of seconds locking

10. http://www.harness-project.eu/
AdPredictor Training Process

11. http://www.harness-project.eu/
HetMR: Heterogeneous MapReduce
 MapReduce deployment and execution system
for hybrid CPU/accelerator environments
Map on GPGPU
Reduce on CPU
Map on GPGPU
Reduce on CPU
Map on CPU
Reduce on FPGA
Map on CPU
Reduce on FPGA

12. http://www.harness-project.eu/
Research Focus Areas
Application design
Performance prediction
and cross-resource mgmt.
Resource virtualisation
Individual-resource mgmt.

13. http://www.harness-project.eu/
Mike’s Story
http://www.harness-project.eu/