1. Metascheduling on the Grid
In the grid environment, a metascheduler is also known as global scheduler, which
coordinates communications between multiple heterogeneous schedulers (local
schedulers) that operate at the local or cluster level.
By cooperating with Grid Information Services, which abstract the information of
all the available resources on the grid into a sing le resource perspective, the grid
metascheduler presents the end user a single virtual resource pool, which hides all
the details of scheduling and monitoring jobs in the dynamic and heterogeneous
environment.
From the end user's viewpoint, a fully virtualized grid is like a single
supercomputer, and as a proxy or entry point to the grid, the metascheduler should
hide grid­specific technical details to the most extent. A good metascheduler should
only ask the end user (without any grid specific knowledge) three questions (see
Figure 3 for an example description)
WHAT DO YOU NEED? (Requirements)WHAT DO YOU NEED? (Requirements) This usually involves
requirement matchmaking between known resources on the grid and the resources
required by the end user, such as a particular software, disk space, and the forth.
WHAT DO YOU HAVE? (Input)WHAT DO YOU HAVE? (Input) This usually needs the end user to
specify the input of the job, such as input files on the local or remote machine, or
just the arguments of the executable, or both.
WHO ARE YOU? (VO Identity*)WHO ARE YOU? (VO Identity*) This usually needs the user to declare
his or her VO identity, which enables the scheduler to analyse resource information
from the user's view point.
* A grid user's VO identity is not used for authentication or authorization, instead, by providing
the VO identity, we can retrieve user­specific resource information like the accessible free cups,
disk space and software tools.
Improving the Functionality and Customization of Scheduling
Services for Grid Computing
Jingjing Sun, Supervised by
Dr. Paul Coddington, Dr. Andrew Wendelborn
School of Computer Science, University of Adelaide
Figure 1. Computing resources across ANG Grid, discovered by the customized metascheduler Figure 3. A typical customized job template
including all kinds of user requirements
Acknowledgements
This project would not have been possible without Dr. Paul Coddington and Dr. Andrew
Wendelborn's wise and patient guidance. Special thanks to the cooperation of the excellent
SAPAC staff: Daniel Cox, Gerson Galang and Shunde Zhang, they made the whole things
happen.
For more information about our project, please refer to the ANG­GridWay wiki:
http://www.grid.apac.edu.au/repository/trac/APACGridway/wiki
Overview and Aims
By aggregating computing power, software tools, data storage systems and scientific
instruments that are distributed in heterogeneous systems across multiple locations,
Grid Computing promises a global virtual supercomputer where users at different
physical locations can cooperate for a specific problem in a high performance, secure,
reliable and cost­effective way.
Via grid resource virtualization technologies, Grid hides the large­scale, distributed
and dynamic nature of the grid computing environment, thus creating a single system
image, i.e., a single yet powerful virtual computer, and enabling the end­users (i.e.,
domain experts) to fully focus on problem solving rather than the underlying
technical details.
As the underlying infrastructure of the Australian National Grid (ANG) is becoming
increasingly complex and dynamic, it is no longer suitable and efficient for its users to
manually perform computational tasks on the large­scale and heterogeneous ANG
Grid, therefore a metascheduling system is needed by the ANG Grid to provided its
end­users with an easy­to­use and automatic job execution environment. However,
none of current major metaschedulers (such as Condor­G, GridBus Broker and
GridWay, etc) support the latest grid information standard, i.e., Globus MDS 4
(Monitoring and Discovery Services) with GLUE Schema Specification 1.2, which is
used by ANG to describe and publish resource information. Moreover, none of the
these metaschedulers offer the functionalities that enable the end­users to specify
software requirements and other desired resources that they are allowed to use
according to the local domain policies (which can be considered as a quota system),
and the ability which helps the grid administrator to appropriately allocate the
resource such as software and storage usage priority to different ANG user group.
These functionalities are highly desirable for building a completely virtualized grid
environment in which the end­users are able to fully focus on problem solving rather
than the grid­specific technical details.
This project aims to customize and deploy a metascheduling system (GridWay) for
the ANG Grid by cooperating with the ANG Grid resource information service, thus
virtualizing the high performance computing resources across the ANG Grid and
providing its users with an automatic and intelligent job execution environment.
Based on GridWay's basic scheduling framework, we added very important features
to achieve the real virtualization of the ANG Grid resource(see Section
Implementation for details). By using the GridWay modified and customized for
ANG(i.e., ANG­GridWay), the end user just needs to specify the input data (what I
have), the required resources (What I need) and optionally the user's user group(i.e.,
Virtual Organization, a.k.a., VO*) identity (Who I am), the scheduler then
appropriately schedules the tasks through ANG grid and hides all the technical
details such as requirements matchmaking, VO View checking, and failure handling.
Our scheduling policies also allows the scheduler to choose a “preferred” queue for a
specific software. *VO: An administrative domain with a separate and distinct set of
administrative policies such as access control and resource usage quota allocation
Implementation
As mentioned in the first section, our implementation focused on adapting the original GridWay scheduling framework to
current ANG Grid infrastructure, and virtualizing all computing resources across the ANG Grid to a unified resource pool.
Based on GridWay’s original scheduling framework, we developed advanced functionalities to provide our end­users with an
easy­to­use and intelligent job execution environment. We summarized our work that has been done so far into the following
six categories:
Information ModelInformation Model Work in this part mainly focused on adapting GridWay to smoothly cooperate with the information
published by the ANG MDS information service. The ANG­GridWay was customized to cooperate with an extended version of
MDS4(GLUE1.2) Schema (see Figure 4 for the information model) according to real experience of ANG users, the extended
MDS4(GLUE1.2) resource information model allows our scheduler to accurately locate the target resource according to user
requirements and grid domain policies. Figure 1 and Figure 2 show all the ANG computing resources discovered by the
customized scheduler and a complete view of a particular resource, respectively.
Software RequirementsSoftware Requirements One of the most important functionalities the ANG­GridWay provides is enabling the end
user to specify required software (then generating corresponding module extension in the corresponding RSL file).
VOView RequirementsVOView Requirements The VOView entity of GLUE Schema describes the resource information from a specific grid
user's viewpoint, by making use of the VOView information, our scheduler presents the end user a “personalized” resource
perspective, enabling more precise metascheduling.
Requirement Matchmaking AlgorithmRequirement Matchmaking Algorithm Based on the above work, an advanced matchmaking algorithm was
developed to enable the scheduler to automatically search the resources that accurately satisfy user requirements. Our
matchmaking algorithm allows user to specify multiple software requirements in a single task, which is a highly desirable
feature which enables our user to design simple workflow requiring multiple softwares without the need of knowing where
these softwares are located on the grid.
Software Priority on the ResourceSoftware Priority on the Resource According to the real cases of resource allocation and software usage on the
ANG Grid, ANG­GridWay introduces a special functionality which enables software to run on a “preferred” resource according
to a specified priority assigned to the given grid user.
Resource Failure HandlingResource Failure Handling Our new failure handling policy performs on queue level for a given user, which avoids
waste of resources and enables fine­grained failure handling control on the resources.
The Grid and Grid Information Service
The most common description of Grid Computing is often compared with an electric
power grid, through which we consume the electrical power on demand, without
knowing where and how the energy is generated. Similarly, Grid Computing
technologies hide the details of the underlying computing resources and the
complexity of how these resources are organized and how computation jobs are
scheduled, thus creating a single and unified system image, as a result, the end
users are able to perform computational tasks on the grid as if they were using a
single yet powerful virtual computer.
The Globus MDS services provide a standardized approach for grid resource
discovery and monitoring, thus playing a key role in computing resource
virtualization. More detailly, Grid Laboratory Uniform Environment (GLUE)
schema is used by MDS service to describe the grid resources in a precise and
systematic manner, thus enabling them to be discovered for subsequent
management or use such as task scheduling. By defining an information model at
the conceptual level, GLUE schema specification abstracts the real world computing
resources into constructs which can be represented in computer systems.
Our metascheduler was customized to cooperate with MDS4(GLUE1.2) Schema
extended by the ANG Grid according to the real user experience. The extended
model of resource information is illustrated in Figure 4, the minimum discovery unit
is SubCluster (a homogeneous computing environment), which help the
metascheduler precisely locate the target resource according to user requirements. A
complete view of a SubCluster (from ANG­GridWay's view point) is also shown in
Figure 4.
Conclusion and Future Work
We successfully adapted GridWay to current ANG Grid infrastructure, our customizations
have enabled GridWay to smoothly cooperate with the ANG MDS information service. The
customized scheduler has very important and useful features that are not provided by the
original one but needed by the ANG Grid, such as supporting the latest information
standards and the heterogeneous information model, multiple software requirements, VO
policies, software priority and fine­grained failure handling.
The result of our work is a metascheduling system which fully virtualizes resources
across the ANG Grid. All the details of resource discovery, requirements matchmaking, job
submission and execution, usage and scheduling policy control and failure handling are
successfully hidden from the ANG users. The bottom line of what a user needs to do is just
to declare the required software(s) and input data through the grid portal. Through the
resource virtualization of our metascheduling system, the ANG Grid presents its end users
a unified super virtual computer behind which are various dynamic, heterogeneous and
geographically distributed grid sites.
As the underlying grid infrastructure is becoming increasingly complex and the MDS
GLUE is evolving towards a mature and flexible resource description model, there are a lot
of potential improvements based on current ANG­GridWay, like adaptive scheduling by
using the VOView information, advanced resource reservation and scientific workflow
planning and scheduling.
Figure 2. The complete view of a homogeneous
cluster presented by ANG­GridWay, including
environment information, queues and user VO
views, available software tools and storage
information.
Figure 4. The whole picture: The information model used by the ANG Grid and the
resource virtualization via ANG­GridWay