FutureGrid Computing Testbed as a Service

https://portal.futuregrid.org
FutureGrid
Computing Testbed as a Service
EGI Technical Forum 2013
Madrid Spain September 17 2013
Geoffrey Fox and Gregor von Laszewski
for FutureGrid Team
gcf@indiana.edu
http://www.infomall.org http://www.futuregrid.org
School of Informatics and Computing
Digital Science Center
Indiana University Bloomington

FutureGrid Testbed as a Service
• FutureGrid is part of XSEDE set up as a testbed with cloud focus
• Operational since Summer 2010 (i.e. has had three years of use)
• The FutureGrid testbed provides to its users:
– Support of Computer Science and Computational Science research
– A flexible development and testing platform for middleware and
application users looking at interoperability, functionality,
performance or evaluation
– FutureGrid is user-customizable, accessed interactively and
supports Grid, Cloud and HPC software with and without VM’s
– A rich education and teaching platform for classes
• Offers OpenStack, Eucalyptus, Nimbus, OpenNebula, HPC (MPI) on
same hardware moving to software defined systems; supports both
classic HPC and Cloud storage
• Mainly support staff limited

Use Types for FutureGrid TestbedaaS
• 339 approved projects (2009 users) Sept 16 2013
– Users from 53 Countries
– USA (77.3%), Puerto Rico (2.9%), Indonesia (2.2%) Italy (2%) (last
3 large from classes) India (2.2%)
• Computer Science and Middleware (55.4%)
– Core CS and Cyberinfrastructure (52.2%); Interoperability (3.2%)
for Grids and Clouds such as Open Grid Forum OGF Standards
• Domain Science applications (21.1%)
– Life science high fraction (9.7%), All non Life Science (11.2%)
• Training Education and Outreach (13.9%)
– Semester and short events; interesting outreach to HBCU; 48.6%
users
• Computer Systems Evaluation (9.7%)
– XSEDE (TIS, TAS), OSG, EGI; Campuses
3

FutureGrid Operating Model
• Rather than loading images onto VM’s, FutureGrid supports
Cloud, Grid and Parallel computing environments by
provisioning software as needed onto “bare-metal” or
VM’s/Hypervisors using (changing) open source tools
– Image library for MPI, OpenMP, MapReduce (Hadoop, (Dryad), Twister),
gLite, Unicore, Globus, Xen, ScaleMP (distributed Shared Memory),
Nimbus, Eucalyptus, OpenNebula, KVM, Windows …..
– Either statically or dynamically
• Growth comes from users depositing novel images in library
• FutureGrid is quite small with ~4700 distributed cores and a
dedicated network
Image1 Image2 ImageN…
LoadChoose Run

https://portal.futuregrid.org 6
Name System type # CPUs # Cores TFLOPS
Total RAM
(GB)
Secondary
Storage
(TB)
Site Status
India IBM iDataPlex 256 1024 11 3072 512 IU Operational
Alamo Dell PowerEdge 192 768 8 1152 30 TACC Operational
Hotel IBM iDataPlex 168 672 7 2016 120 UC Operational
Sierra IBM iDataPlex 168 672 7 2688 96 SDSC Operational
Xray Cray XT5m 168 672 6 1344 180 IU Operational
Foxtrot IBM iDataPlex 64 256 2 768 24 UF Operational
Bravo
Large Disk &
memory
32 128 1.5
3072
(192GB per
node)
192 (12 TB
per Server)
IU Operational
Delta
Large Disk &
memory With
Tesla GPU’s
32 CPU
32 GPU’s
192 9
3072
(192GB per
node)
192 (12 TB
per Server) IU Operational
Lima SSD Test System 16 128 1.3 512
3.8(SSD)
8(SATA)
SDSC Operational
Echo
Large memory
ScaleMP
32 192 2 6144 192 IU Beta
TOTAL
1128
+ 32 GPU
4704
+14336
GPU
54.8 23840 1550
Heterogeneous Systems Hardware

FutureGrid Partners
• Indiana University (Architecture, core software, Support)
• San Diego Supercomputer Center at University of California San Diego
(INCA, Monitoring)
• University of Chicago/Argonne National Labs (Nimbus)
• University of Florida (ViNE, Education and Outreach)
• University of Southern California Information Sciences (Pegasus to
manage experiments)
• University of Tennessee Knoxville (Benchmarking)
• University of Texas at Austin/Texas Advanced Computing Center
(Portal, XSEDE Integration)
• University of Virginia (OGF, XSEDE Software stack)
• Red institutions have FutureGrid hardware

Sample FutureGrid Projects I
• FG18 Privacy preserving gene read mapping developed hybrid
MapReduce. Small private secure + large public with safe data. Won
2011 PET Award for Outstanding Research in Privacy Enhancing
Technologies
• FG132, Power Grid Sensor analytics on the cloud with distributed
Hadoop. Won the IEEE Scaling challenge at CCGrid2012.
• FG156 Integrated System for End-to-end High Performance Networking
showed that the RDMA over Converged Ethernet (InfiniBand made to
work over Ethernet network frames) protocol could be used over wide-
area networks, making it viable in cloud computing environments.
• FG172 Cloud-TM on distributed concurrency control (software
transactional memory): "When Scalability Meets Consistency: Genuine
Multiversion Update Serializable Partial Data Replication,“ 32nd
International Conference on Distributed Computing Systems (ICDCS'12)
(good conference) used 40 nodes of FutureGrid
8

Sample FutureGrid Projects II
• FG42,45 SAGA Pilot Job P* abstraction and applications. XSEDE
Cyberinfrastructure used on clouds
• FG130 Optimizing Scientific Workflows on Clouds. Scheduling Pegasus
on distributed systems with overhead measured and reduced. Used
Eucalyptus on FutureGrid
• FG133 Supply Chain Network Simulator Using Cloud Computing with
dynamic virtual machines supporting Monte Carlo simulation with
Grid Appliance and Nimbus
• FG257 Particle Physics Data analysis for ATLAS LHC experiment used
FutureGrid + Canadian Cloud resources to study data analysis on
Nimbus + OpenStack with up to 600 simultaneous jobs
• FG254 Information Diffusion in Online Social Networks is evaluating
NoSQL databases (Hbase, MongoDB, Riak) to support analysis of
Twitter feeds
• FG323 SSD performance benchmarking for HDFS on Lima
9

FG-226 Virtualized GPUs and Network
Devices in a Cloud (ISI/IU)
• Need for GPUs and Infiniband Networking on Clouds
– Goal: provide the same hardware at a minimal overhead to build a clean
HPC Cloud
• Different competing methods for virtualizing GPUs
– Remote API for CUDA calls  rCUDA, vCUDA, gVirtus
– Direct GPU usage within VM  our method
• GPU uses Xen 4.2 Hypervisor with hardware directed I/O virt (VT-d
or IOMMU)
– Kernel overheads <~2% except for Kepler FFT at 15%
• Implement Infiniband via SR-IOV
• Work integrated into OpenStack “Havana” release
– Xen support for full virtualization with libvirt
– Custom Libvirt driver for PCI-Passthrough
10

Performance of GPU enabled VMs
0
500
1000
1500
2000
2500
3000
3500
NAT READ VM READ NAT WRITE VM WRITE
Bandwidth(MB/s)
InfiniBand Bandwidth
0
500
1000
1500
2000
2500
3000
3500
maxspflops maxdpflops
GFLOPS
Benchmark
GPU Max FLOPS
Delta Native
Delta VM
ISI Nat
ISI VM
0
1
2
3
4
5
6
7
8
bspeed_download bspeed_readback
BusSpeed(GB/s)
GPU Bus Speed
C2075 Native
C2075 VM
K20m Native
K20m VM
rCUDA v3 GigE
rCUDA v4 GigE
rCUDA v3 IPoIB
rCUDA v4 IPoIB
rCUDA v4 IBV
0
10
20
30
40
50
60
70
80
GFLOPS
Benchmark
GPU Stencil 2D and S3D
C2075 Native
C2075 VM
K20m Native
K20m VM

Experimental Deployment:
FutureGrid Delta
• Mid October 2013
• 16x 4U nodes in 2 Racks
– 2x Intel Xeon X5660
– 192GB Ram
– Nvidia Tesla C2075 Fermi
– QDR InfiniBand - CX-2
• Management Node
– OpenStack Keystone, Glance,
API, Cinder, Nova-network
• Compute Nodes
– Nova-compute, Xen, libvirt
• Submit your project requests
now!
12

Education and Training Use of FutureGrid
• FutureGrid supports many educational uses
– 36 Semester long classes (9 this semester): over 650 students from over 20
institutions
– Cloud Computing, Distributed Systems, Scientific Computing and Data
Analytics
– 3 one week summer schools: 390+ students
– Big Data, Cloudy View of Computing (for HBCU’s), Science Clouds
– 7 one to three day workshop/tutorials: 238 students
• We are building MOOC (Massive Open Online Courses) lessons to
describe core FutureGrid Capabilities so they can be re-used as
classes by all courses https://fgmoocs.appspot.com/explorer
– Science Cloud Summer School available in MOOC format
– First high level MOOC is Software IP-over-P2P (IPOP)
– Overview and Details of FutureGrid
– How to get project, use HPC and use OpenStack

• MOOC is
short
prerecorded
segments
(talking head
over
PowerPoint)
of length 3-15
minutes
• MOOC
software
dynamically
assembles
lessons to
courses
• Twelve such
lesson objects
in this lecture

FutureGrid hosts many classes per semester
How to use FutureGrid is shared MOOC

Support for classes on FutureGrid
• Classes are setup and managed using the FutureGrid
portal
• Project proposal: can be a class, workshop, short course,
tutorial
– Needs to be approved as FutureGrid project to become active
• Users can be added to a project
– Users create accounts using the portal
– Project leaders can authorize them to gain access to resources
– Students can then interactively use FG resources (e.g. to start
VMs)
• Note that it is getting easier to use “open source clouds”
like OpenStack with convenient web interfaces like
Nimbus-Phantom and OpenStack-Horizon replacing
command line Euca2ools
16

Infra
structure
IaaS
 Software Defined
Computing (virtual Clusters)
 Hypervisor, Bare Metal
 Operating System
Platform
PaaS
 Cloud e.g. MapReduce
 HPC e.g. PETSc, SAGA
 Computer Science e.g.
Compiler tools, Sensor
nets, Monitors
FutureGrid offers
Computing Testbed as a Service
Network
NaaS
 Software Defined
Networks
 OpenFlow GENI
Software
(Application
Or Usage)
SaaS
 CS Research Use e.g.
test new compiler or
storage model
 Class Usages e.g. run
GPU & multicore
 Applications
FutureGrid Uses
Testbed-aaS Tools
 Provisioning
 Image Management
 IaaS Interoperability
 NaaS, IaaS tools
 Monitoring
 Expt management
 Dynamic IaaS NaaS
 Devops
FutureGrid Cloudmesh
(includes RAIN) uses
Dynamic Provisioning and
Image Management to
provide custom
environments for general
target systems
Involves (1) creating,
(2) deploying, and
(3) provisioning
of one or more images in
a set of machines on
demand

Inca
Software functionality and performance
Ganglia
Cluster monitoring
perfSONAR
Network monitoring - Iperf measurements
SNAPP
Network monitoring – SNMP measurements
Monitoring on FutureGrid
Important and even more needs to be done

Selected List of Services Offered
19
Cloud PaaS
Hadoop
Iterative
MapReduce
HDFS
Hbase
Swift Object
Store
IaaS
Nimbus
Eucalyptus
OpenStack
ViNE
GridaaS
Genesis II
Unicore
SAGA
Globus
HPCaaS
MPI
OpenMP
CUDA
TestbedaaS
FG RAIN,
CloudMesh
Portal
Inca
Ganglia
Devops (Chef,
Puppet, Salt)
Experiment
Management e.g.
Pegasus

0
5
10
15
20
25 10Q3
10Q4
11Q1
11Q2
11Q3
11Q4
12Q1
12Q2
12Q3
12Q4
13Q1
13Q2
13Q3
HPC
Eucalyptus
Nimbus
OpenNebula
OpenStack
Avg of the rest 16
Poly. (HPC)
Poly. (Eucalyptus)
Poly. (Nimbus)
Poly. (OpenNebula)
Poly. (OpenStack)
Poly. (Avg of the rest 16)
Technology Requests per Quarter
20
Poly is a polynomial fit

Education Technology Requests
21

Essential and Different features of FutureGrid in Cloud area
• Unlike many clouds such as Amazon and Azure, FutureGrid allows
robust reproducible (in performance and functionality) research (you
can request same node with and without VM)
– Open Transparent Technology Environment
• FutureGrid is more than a Cloud; it is a general distributed Sandbox;
a cloud grid HPC testbed
• Supports 3 different IaaS environments (Nimbus, Eucalyptus,
OpenStack) and projects involve 5 (also CloudStack, OpenNebula)
• Supports research on cloud tools, cloud middleware and cloud-based
systems
• FutureGrid has itself developed middleware and interfaces to
support FutureGrid’s mission e.g. Phantom (cloud user interface) Vine
(virtual network) RAIN (deploy systems) and security/metric
integration
• FutureGrid has experience in running cloud systems
22

FutureGrid is an onramp to other systems
• FG supports Education & Training for all systems
• User can do all work on FutureGrid OR
• User can download Appliances on local machines (Virtual Box) OR
• User soon can use CloudMesh to jump to chosen production system
• CloudMesh is similar to OpenStack Horizon, but aimed at multiple
federated systems.
– Built on RAIN and tools like libcloud, boto with protocol (EC2) or programmatic
API (python)
– Uses general templated image that can be retargeted
– One-click template & image install on various IaaS & bare metal including
Amazon, Azure, Eucalyptus, Openstack, OpenNebula, Nimbus, HPC
– Provisions the complete system needed by user and not just a single image;
copes with resource limitations and deploys full range of software
– Integrates our VM metrics package (TAS collaboration) that links to XSEDE
(VM's are different from traditional Linux in metrics supported and needed)
23

Cloudmesh Functionality View
24
Initial Open Source
Release Mid October
2013

Cloudmesh Layered Architecture View
Provisioner Abstraction
OS Provisioners
Teefaa, Cobbler, OpenStack Bare Metal
Interfaces
Portal, CMD shell, Commandline, API
Provision Management
RAIN
VM Image Generation,
VM Provisioning
Image Management
Provisioner
Queue
AMQP
Cloud
Metrics
REST
InfrastructureMonitor
Security
Infrastructure
Scheduler
REST
Data
User On-Ramp
Amazon, Azure,
Eucalyptus,
OpenCirrus, ...
IaaS Abstraction
25

Performance of Dynamic Provisioning
• 4 Phases a) Design and create image (security vet) b) Store in
repository as template with components c) Register Image to VM
Manager (cached ahead of time) d) Instantiate (Provision) image
26
0
50
100
150
200
250
300
1 2 4 8 16 37
Time(s)
Number of Machines
Provisioning from RegisteredImages
OpenStack
xCAT/Moab
0
100
200
300
400
500
CentOS 5 Ubuntu 10.10
Time(s)
Generate an Image
Upload image to the
repo
Compress image
Install user packages
Install u l packages
Create Base OS
Boot VM
0
200
400
600
800
1 2 4
Time(s)
Number of Images Generated
at the Same Time
Generate Images
CentOS 5
Ubuntu 10.10
Phase a) b)
Phase a) b)Phase d)

Security issues in FutureGrid Operation
• Security for TestBedaaS is a good research area (and Cybersecurity
research supported on FutureGrid)!
• Authentication and Authorization model
– This is different from those in use in XSEDE and changes in different releases of VM
Management systems
– We need to largely isolate users from these changes for obvious reasons
– Non secure deployment defaults (in case of OpenStack)
– OpenStack Grizzly and Havana have reworked the role based access control
mechanisms and introduced a better token format based on standard PKI (as used
in AWS, Google, Azure); added groups
– Custom: We integrate with our distributed LDAP between the FutureGrid portal
and VM managers. LDAP server will soon synchronize via AMIE to XSEDE
• Security of Dynamically Provisioned Images
– Templated image generation process automatically puts security restrictions into
the image; This includes the removal of root access
– Images include service allowing designated users (project members) to log in
– Images vetted before allowing role-dependent bare metal deployment
– No SSH keys stored in images (just call to identity service) so only certified users
can use
27

Related Projects
• Grid5000 (Europe) and OpenCirrus with managed flexible
environments are closest to FutureGrid and are collaborators
• PlanetLab has a networking focus with less managed system
• Several GENI related activities including network centric EmuLab,
PRObE (Parallel Reconfigurable Observational Environment),
ProtoGENI, ExoGENI, InstaGENI and GENICloud
• BonFire (Europe) European cloud Testbed supporting OCCI
• EGI Federated Cloud with OpenStack and OpenNebula aimed at EU
Grid/Cloud federation
• Private Clouds: Red Cloud (XSEDE), Wispy (XSEDE), Open Science
Data Cloud and the Open Cloud Consortium are typically aimed at
computational science
• Public Clouds such as AWS do not allow reproducible experiments
and bare-metal/VM comparison; do not support experiments on
low level cloud technology
28

Lessons learnt from FutureGrid
• Unexpected major use from Computer Science and Middleware
• Rapid evolution of Technology Eucalyptus  Nimbus  OpenStack
• Open source IaaS maturing as in “Paypal To Drop VMware From 80,000 Servers
and Replace It With OpenStack” (Forbes)
– “VMWare loses $2B in market cap”; eBay expects to switch broadly?
• Need interactive not batch use; nearly all jobs short but can need lots of nodes
• Substantial TestbedaaS technology needed and FutureGrid developed (RAIN,
CloudMesh, Operational model) some
• Lessons more positive than DoE Magellan report (aimed as an early science
cloud) but goals different
• Still serious performance problems in clouds for networking and device (GPU)
linkage; many activities in and outside FG addressing
• We identified characteristics of “optimal hardware”
• Run system with integrated software (computer science) and systems
administration team
• Build Computer Testbed as a Service Community
29

EGI Cloud Activities v. FutureGrid
• https://wiki.egi.eu/wiki/Fedcloud-tf:FederatedCloudsTaskForce
30
EGI Phase 1. Setup: Sept 2011 - March 2012 FutureGrid
# Workbenches Capabilities
1
Running a pre-defined VM
Image
VM Management
Cloudmesh. Templated image
management
2 Managing users' data and VMs Data management
Not addressed due to multiple FG
environments/lack of manpower
3
Integrating information from
multiple resource providers
Information discovery
Cloudmesh, FG Metrics, Inca, FG
Glue2, Ubmod, Ganglia
4
Accounting across Resource
Providers
Accounting FG Metrics
5
Reliability/Availability of
Resource Providers
Monitoring
Not addressed (as Testbed not
production)
6
VM/Resource state change
notification
Notification Provided by IaaS for our systems
7 AA across Resource Providers
Authentication and
Authorisation
LDAP, Role-based AA
8
VM images across Resource
Providers
VM sharing Templated image Repository

Future Directions for FutureGrid
• Poised to support more users as technology like OpenStack matures
– Please encourage new users and new challenges
• More focus on academic Platform as a Service (PaaS) - high-level
middleware (e.g. Hadoop, Hbase, MongoDB) – as IaaS gets easier to
deploy with increased Big Data challenges but we lack staff!
• Need Large Cluster for Scaling tests of Data mining environments (also missing
in production systems)
• Improve Education and Training with model for MOOC laboratories
• Finish Cloudmesh (and integrate with Nimbus Phantom) to make
FutureGrid as hub to jump to multiple different “production” clouds
commercially, nationally and on campuses; allow cloud bursting
• Build underlying software defined system model with integration
with GENI and high performance virtualized devices (MIC, GPU)
• Improved ubiquitous monitoring at PaaS IaaS and NaaS levels
• Improve “Reproducible Experiment Management” environment
• Expand and renew hardware via federation
31

Summary Differences between
FutureGrid I (current) and FutureGrid II
32
Usage FutureGrid I FutureGrid II
Target environments Grid, Cloud, and HPC Cloud, Big-data, HPC, some Grids
Computer Science Per-project experiments Repeatable, reusable experiments
Education Fixed Resource
Scalable use of Commercial to FutureGrid II
to Appliance per-tool and audience type
Domain Science Software develop/test
Software develop/test across resources
using templated appliances
Cyberinfrastructure FutureGrid I FutureGrid II
Provisioning model IaaS+PaaS+SaaS
CTaaS including
NaaS+IaaS+PaaS+SaaS
Configuration Static Software-defined
Extensibility Fixed size Federation
User support Help desk Help Desk + Community based
Flexibility Fixed resource types Software-defined + federation
Deployed Software
Service Model
Proprietary, Closed Source, Open
Source
Open Source
IaaS Hosting Model Private Distributed Cloud
Public and Private Distributed Cloud
with multiple administrative domains

Federated Hardware Model in FutureGrid I
• FutureGrid internally federates heterogeneous cloud and HPC
systems
– Want to expand with federated hardware partners
• HPC services: Federation of HPC hardware is possible via Grid
technologies (However we do not focus on this as this done well at
XSEDE and EGI)
• Homogeneous cloud federation (one IaaS framework).
– Integrate multiple clouds as zones.
– Publish the zones so we can find them in a service repository.
– introduce trust through uniform project vetting
– allow authorized projects by zone (zone can determine is a project is allowed
on their cloud)
– integrate trusted identity providers => trusted identity providers & trusted
project management & local autonomy
33

Federated Hardware Model in FutureGrid II
• Heterogeneous Cloud Federation (multiple IaaS)
– Just as homogeneous case but in addition to zones we also have
different IaaS frameworks including commercial
– Such as Azure + Amazon + FutureGrid federation
• Federation through Cloudmesh
– HPC+Cloud extended outside FutureGrid
– Develop "drivers license model" (online user test) for RAIN.
– Introduce service access policies. CloudMesh is just one of such
possible services e.g. enhance previous models with role based
system allowing restriction of access to services
– Development of policies on how users gain access to such
services, including consequences if they are broken.
– Automated security vetting of images before deployment
34

Link FutureGrid and GENI
• Identify how to use the ORCA federation framework to
integrate FutureGrid (and more of XSEDE?) into ExoGENI
• Allow FG(XSEDE) users to access the GENI resources and
vice versa
• Enable PaaS level services (such as a distributed Hbase or
Hadoop) to be deployed across FG and GENI resources
• Leverage the Image generation capabilities of FG and the
bare metal deployment strategies of FG within the GENI
context.
– Software defined networks plus cloud/bare metal dynamic
provisioning gives software defined systems
• Not funded yet!
35

Typical FutureGrid/GENI Project
• Bringing computing to data is often unrealistic as repositories
distinct from computing resource and/or data is distributed
• So one can build and measure performance of virtual
distributed data stores where software defined networks bring
the computing to distributed data repositories.
• Example applications already on FutureGrid include Network
Science (analysis of Twitter data), “Deep Learning” (large scale
clustering of social images), Earthquake and Polar Science,
Sensor nets as seen in Smart Power Grids, Pathology images,
and Genomics
• Compare different data models HDFS, Hbase, Object Stores,
Lustre, Databases
36

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