Rain: Dynamically Provisioning Clouds within FutureGrid
Geoffrey Fox, Andrew J. Younge, Gregor von Laszewski, Archit Kulshrestha, Fugang Wang
Pervasive Technology Institute, Indiana University, Bloomington, IN USA
Introduction
This work presents a novel method for dynamically provisioning
Cloud services onto HPC resources provided through the
FutureGrid project. The dynamic provisioning process enables
scientific researchers to build advanced platforms and services
using FutureGrid infrastructure to leverage the power of HPC in a
way that was previously impossible with traditional Grid
infrastructure. The Runtime Adaptable INsertion (RAIN) service
allows for researchers to leverage Cloud infrastructure to deploy
their own platform and operating environment with ease, a task
which was previously impossible with any HPC system available.
Design
RAIN Service
Architecture
Research has shown entire resource re-provisioning can be
accomplished in a very short time, which warrants the use of
RAIN due to its added benefits and ease of use to researchers.
This advancement may finally lower the entry barrier into HPC for
a large number of scientists who's requirements were too large and
means too small take advantage of HPC resources.
Dynamic
Provisioning
Dynamic provisioning embodies a key building block in the
overall design of the FutureGrid project. Within FutureGrid,
there is the concept of provisioning or "raining" both
infrastructure and platforms on demand to commodity hardware
based on user requirements for what software stack and
operating environment suits them best. This concept of dynamic
provisioning is a pivotal process in making the FG deployment
unique and desirable to any set of scientific researchers requiring
high performance computing today. In essence we are hosting
both infrastructure and platforms within a service oriented
architecture. We define raining infrastructure as the rapid
deployment of Infrastructure as-a-Service (IaaS), which delivers
a service that allows users to gain access to and fully manage a
compute infrastructure suitable for their needs. Most common is
the management of a virtualized set of images of Virtual
machines. Such virtualization allows the datacenter to host a
number of virtualized servers on the same hardware. Examples
of IaaS, particularly within FG, are Nimbus and Eucalyptus
clouds. However raining platforms with Platforms as a Service
(PaaS), takes on the next level. It delivers services to the users
integrating a computing platform and/or a solution stack to
support the development of cloud applications. The platform
therefore provides significant enhancements to the infrastructure
building a cloud and reducing the cost and complexity associated
with developing software on a simple cloud infrastructure.
Examples of PaaS deployments that FG plans to support include
Hadoop, Twister, and possibly other Message Queue systems as
demand rises.
Implementation
Discussion
In order to provide dynamic provisioning to users, not one, but
multiple different tools will need to be integrated together to create
a seamless experience for the user. As such, we have identified
xCAT as the best fit for bare-metal level OS deployment. With
xCAT, we can provision a wide array of Operating Systems on the
available resources, thereby providing the environment a user
wants with ease. While xCAT is remarkably well suited for OS
deployment, an additional layer is needed to manage the
provisioning of such resources and the scheduling of work to them.
Adaptive Computing's MOAB Suite provides an elevated queue to
accept tasks and control xCAT to provision the resources to
effectively meet the needs of the queue. Using MOAB with xCAT
and our own RAIN services could provide dynamic provisioning
and adaption of resources within a particular site-wide deployment
on the FutureGrid. With the creation and utilization of a wide
variety of UNIX-based Operating Systems, a configuration
management system is needed to keep everything working
properly. This includes managing and updating installed software,
adding security patches, maintaining configuration files and adding
host keys and certificates on the fly to xCAT provisioned nodes and
newly created virtual machines. This provides a seamless
environment for both the users as well as the system administrators.
With the vast array of virtual clusters and private clouds, a number
of head nodes are required to manage each system. While these
head nodes are not computationally intensive, they do need
dedicated resources on their own. This includes head nodes for
Nimbus clouds, Eucalyptus clouds, a PBS queue, and any other
user-determined distributed systems. It is important to note that
neither Moab, XCAT, BCFG2, or other tools which are often
referred to by members of the project are able to provide the
functionality needed for FG alone. In an implementation view they
provide portions of the functionality and we will see how these
tools can assist building the Architecture of FG. Together these
tools comprise the building blocks for our RAIN service.
The dynamic provisioning software architecture was deployed
onto a FutureGrid test platform called Gravel as well as the
production-level Sierra and India clusters. On Gravel the dynamic
provisioning scenario was tested using VirtualBox virtual
machines. The xCAT VirtualBox plugin was used to manage the
power attributes of the VMs and the Moab Service Manager’s
xCAT plugin was modified to add support for VirtualBox VMs and
emulated real FutureGrid infrastructure providing an ideal
development platform. The system was tested with various
RHEL5, CentOS5 and Fedora images using stateful and stateless
installs of each to obtain preliminary performance results. In a
stateless setup the time taken to have a node provisioned and ready
to accept jobs is affected by the time it takes to transfer the root
image over the network in addition to the boot up time of the node
with the image. When similar images were deployed using
stateless and stateful modes we found no documentable difference
between the boot times and the results varied between tests. The
size of the image is a large part of the boot times in both cases and
we plan to run further tests with smaller satellite images where the
core image is small and most of the tools and software are
mounted read only onto the image and study the best mode of
deployment.
Process View
About FutureGrid
The FutureGrid is an NSF-funded project which provides an
experimental platform that accommodates batch, grid and cloud
computing, allowing researchers to attack a range of research
questions associated with optimizing, integrating and scheduling
the different service models. FutureGrid will provide a significant
new experimental computing grid and cloud computing test-bed to
the research community, together with user support for third-party
researchers conducting experiments on FutureGrid. The test-bed
includes a geographically distributed set of heterogeneous
computing systems, a data management system that will hold both
metadata and a growing library of software images, and a
dedicated network allowing isolatable, secure experiments.
More more information, please visit our website at:
http://futuregrid.org