Jason Cope and Henry M. Tufo
Department of Computer Science, University of Colorado, 430 UCB, Boulder, CO, 80304-0430, U.S.A.
Data Grids, Urgent Computing, Grid and Web Services, Service-Oriented Architectures.
Emerging urgent computing tools can quickly allocate computational resources for the execution of time criti-
cal jobs. Grid applications and workflows often use Grid services and service-oriented architectures. Currently,
urgent computing tools cannot allocate or manage Grid services. In this paper, we evaluate a service-oriented
approach to Grid service access and provisioning for urgent computing environments. Our approach allows
resource providers to define urgent computing resources and Grid services at a much finer granularity than pre-
viously possible. It accommodates new urgent computing resource types, requires minimum reconfiguration
of existing services, and provides adaptive Grid service management tools. We evaluate our service-oriented,
urgent computing approach by applying our tools to Grid services commonly used in urgent computing work-
flows and evaluate management policies through our urgent service simulator.
Recent research into urgent computing systems has
improved several dynamic data-driven workflows that
perform emergency computations. The goal of ur-
gent computing is to provide a cohesive infrastruc-
ture to support time-critical computations. Examples
of these applications and workflows include severe
weather forecasting workflowssuch as those provided
by Linked Environments for Atmospheric Discov-
ery (LEAD) (Droegemeier et al., 2004), the Southern
California Earthquake Center’s (SCEC) TeraShake
earthquake simulation applications (Cui et al., 2007),
the Southeastern Universities Research Association
(SURA) Coastal Ocean Observing and Prediction
(SCOOP) storm surge modeling applications (Bog-
den et al., 2007), epidemic transmission simulations
using tools like Virginia Tech’s EpiSims application
(Eubank et al., 2006), and the Data Dynamic Sim-
ulation for Disaster Management project’s Coupled
Atmosphere-Fire (CAF) wildfire forecasting work-
flow (Mandel et al., 2007).
The LEAD and SCOOP projects use the Spe-
cial PRiority and Urgent Computing Environment
(SPRUCE) to obtain high-priority access to the
shared computing resources available on the TeraGrid
(Catlett et al., 2007), a distributed supercomputingen-
vironment in the US similar to DEISA (Lederer et al.,
2007). SPRUCE provides project users with elevated
and automated access to TeraGrid computational re-
sources so that high-priority applications run imme-
diately or as soon as possible (Beckman et al., 2007).
SPRUCE currently provides capabilities for allocat-
ing computational resources but does not yet support
urgent storage or data management capabilities. Ur-
gent storage and data management capabilities pro-
vide cohesive infrastructure for prioritized usage of
storage resources, such as file systems, data streams,
and data catalogs. Since several of these applications,
such as LEAD and TeraShake, have significant data
requirements, providing urgent storage and data man-
agement is an essential and currently absent capability
for urgent computing workflows. Supporting end-to-
end urgent computing workflows requires support for
common data capabilities, such as data storage, ac-
cess, search, and manipulation capabilities, required
by these workflows.
We are developing an Urgent Data Management
Framework (UDMF) to address the data requirements
of urgent computing workflows. This framework will
manage data related tasks and provision storage re-
sources for urgent workflow usage. Several urgent
computing workflows use service-oriented architec-
tures for resource and application integration. These
application deployments typically operate in multi-
ple modes and share services between project deploy-
Cope J. and M. Tufo H. (2008).
In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 135-142
DOI: 10.5220/0001893301350142
ments. While the current SPRUCE infrastructure can
negotiate access to compute resources for these sys-
tems, it does not support similar capabilities for the
Grid services utilized by these workflows. In order to
support end-to-end urgent workflow scheduling and
data management, the Grid services that provide data
capabilities to these workflows should also support
authorization and usage capabilities similar to other
urgent computing resources.
In this paper, we describe our use of monitoring,
authorization, and provisioning infrastructure to pro-
vide urgent computing capabilities to Grid services.
We provide these capabilities by leveraging past re-
search on Grid authorization and monitoring infras-
tructure and using Service Level Agreements (SLA)
for establishing and sustaining Quality of Service
(QoS). Providing these urgent Grid service capabili-
ties allows resource providers to directly apply urgent
computing policies to Grid services. These policies
can indirectly provision resources used by a Grid ser-
vice. This feature is especially useful for providing
urgent computing capabilities to software or resources
that can not directly support urgent computing capa-
bilities. Our approach is reusable by other Grid ser-
vices and requires minimal reconfiguration of existing
The remainder of this paper describes our current
work on urgent Grid services. Section 2 describes
the urgent computing paradigm and the current urgent
computing infrastructure. Section 3 describes related
areas of research. Section 4 describes our urgent ser-
vice provisioningand policy management framework.
Section 5 details our initial evaluation of this frame-
work through its use by Grid resource management
and data services. In the final sections of this paper,
we present future work and conclusions.
2.1 The State of Urgent Computing
The Special PRioirty and Urgent Computing Envi-
ronment (SPRUCE) (Beckman et al., 2007) enables
on-demand resource allocation, authorization, and se-
lection for urgent computing applications and work-
flows. This environment provides on-demand access
to shared Grid computing resources with a token-
based authorization framework. By utilizing shared
resources, SPRUCE allows data centers and virtual
organizations to utilize existing computing infrastruc-
ture for emergency computations instead of allocat-
ing dedicated resources for these tasks. Users sub-
mitting SPRUCE jobs specify a color-coded urgency
parameter with their job description. SPRUCE au-
thorizes the urgent job execution request by verifying
that a user is permitted to execute jobs with the spec-
ified urgency on the target resource. Each resource
provider defines policies for how the urgent tasks are
handled on a per-resource basis. For example, a re-
source provider may choose to preempt non-urgent
jobs for high-priority tasks or to give the urgent tasks
next-to-run privileges. The infrastructure is currently
deployed on several TeraGrid resources, including re-
sources at NCAR, UC/ANL, SDSC, and TACC, and
is used by several Grid computing workflows, such as
While SPRUCE provides access to computational
resources, it does not provision or negotiate access to
other common Grid services and capabilities, such as
data management services, resource management ser-
vices, and Grid credential management services. To
completely support urgent computing in Grids, these
other services must be supported so that urgent work-
flows do not bottleneck against these unmanaged ser-
2.2 Service Requirements of Urgent
Computing Workflows
Recent service-oriented systems have provided many
capabilities for users to integrate into their workflows.
For example, LEAD provides a large set of services
hosted across the project partner sites. The available
services include data management services that can
assimilate data, reference metadata catalogs, stream
data from remote sensing equipment, and executedata
mining operations. LEAD leverages the core Globus
Grid services (Foster, 2005), such as the Grid Re-
source Allocation and Management (GRAM) service,
to interact with computational and storage resources.
Similar to LEAD, SCOOP provides a series of data
services exposed as Grid and Web Services. These
services provide capabilities to access catalog data,
transform data into compliant formats, and visualize
data sets. Like LEAD, SCOOP implements these ser-
vices as Web or Grid services.
Since one of the benefits of Web and Grid services
is software reuse, these services are used by projects
and users other than SCOOP or LEAD. SCOOP has
stated that their tools are available for community use.
Furthermore, these systems also provide multiple op-
erational modes and use cases. Not only is LEAD
capable of forecasting severe weather events such as
tornadoes, it is also used as an educational and train-
ing tool. The combination of multimodal use and the
lack of urgent computing infrastructure to provision
these services necessitates infrastructure to manage
ICSOFT 2008 - International Conference on Software and Data Technologies
Storage Resource
Data Manager
Data Processing
Urgent Computing
Urgent Multi-
Resource Manager
Urgent Storage Manager
Urgent Data
Computational Resource
Urgent Computational
Resource Manager
Urgent Service Access
Data Client Task Request
Figure 1: UDMF architecture.
and provision access to these services. In the event of
an urgent workflow, it is not acceptable for the work-
flows performance to be degraded because of conges-
tion at these services and the resources they manage.
2.3 Urgent Data Management
We are developing an Urgent Data Management
Framework (UDMF) that provides the necessary data
management capabilities for urgent computing work-
flows (Cope and Tufo, 2008). This framework pro-
vides users with priority based access to data capa-
bilities used in urgent computing workflows. These
tools provide storage and network resource provision-
ing, autonomic resource management, and resource
management policies that address urgent computing
data requirements. Figure 1 illustrates the compo-
nents and interactions of UDMF. UDMF leverages
existing infrastructure (illustrated as the solid compo-
nents in Figure 1) when possible. UDMF contains
several software managers that can execute the re-
source management policies and negotiate urgent user
access to the data resources. The UDMF components
are illustrated as the dashed components in Figure 1.
Many of the existing software components are exten-
sible, such as the Data Storage Interface to GridFTP
(Allcock et al., 2002) or the Authorization and Au-
thentication Framework in the Globus Web service
The focus of this paper is on the Grid service com-
ponentsof UDMF. These Grid service specific UDMF
components provide monitoring and instrumentation
for Grid service invocations, user authorization con-
sistent with other urgent computing tools, and adap-
tive resource provisioning tools and polices to manage
user access and interaction with Grid services.
The research we present in this paper draws upon
the experiences from several related areas of work,
including Grid security, Service Level Agreements
(SLA) for Grid services, and Grid Quality of Service
(QoS). Several authorization and authentication tools
exist for use in Grids. The Globus Toolkit provides
the bulk of the authorization capabilities required
(Foster, 2005). We use the Globus Policy Decision
Point (PDP) and Policy Information Point (PIP) au-
thorization chains to negotiate access to Grid services
during the invocation of a service operation. Several
other projects use the same authorization infrastruc-
ture for integration of security policies and authoriza-
tion tools. GridShib integrates Shibboleth within the
Globus authorization frameworks using custom PDP
and PIP infrastructure (Chadwick et al., 2006; Lang
et al., 2006). The Virtual Organization Management
System (VOMS) also provides similar capabilities to
GridShib using the Globus authorization and authen-
tication framework (Alfieri et al., 2003).
In this paper we leverage past work on Web
service QoS and Service Level Agreements (SLA)
techniques for supporting urgent computing environ-
ments. In the context of service-oriented architec-
tures, our work on Grid service provisioning is most
similar to past research on SLAs. The use of SLAs is
still an active area of research, but they have not yet
been used to support urgent computing applications
and resources. The infrastructure to support SLAs
in UDMF is similar to other SLA management tools
that use brokers to advertise, negotiate, and establish
SLAs between clients and services (Dan et al., 2004).
Grid QoS research is often associated with SLA
research. QoS monitors typically observe the prop-
erties of systems and SLA brokers use these obser-
vations to determine attainable and sustainable QoS
for a specific SLA. Several projects have used this
model for Grid systems (Liu et al., 2004; Wang et al.,
2005; Truong et al., 2006; Al-Ali et al., 2004). Our
work is similar to request prioritization and QoS dif-
ferentiation techniques that adapt service executions
to priority-based policies (Sharma et al., 2003; Zhou
et al., 2007; Benkner et al., 2007; Erradi and Ma-
heshwari, 2007). Other tools, such as the Network
Weather Service (NWS), use collected observations
to forecast expected QoS for a system (Wolski et al.,
2005; Nurmi et al., 2007).
Grid Service Client
Grid Service Container
Grid Service
Managed Resource
Urgent Grid
Service Manager
Urgent Request /
Response Handler
Figure 2: Implemented urgent service authorization frame-
Our urgent Grid service framework consists of in-
formation services to communicate the state, capa-
bilities, and policies associated with available urgent
services. It also monitors the usage of urgent Grid
services and other services collocated with an urgent
Grid service. We provide authorization infrastructure
to determine the level access to a service a user is
permitted. Figure 2 illustrates these components and
their interactions.
4.1 Advertising and Monitoring Urgent
Grid Services
Information services are a critical component of
service-oriented architectures so that end-users or
other software components can recognize the prop-
erties and capabilities of deployed services. To help
identify available urgent computing services hosted
on a resource, we developed a mechanism to reg-
ister and describe urgent computing services with a
local Globus Monitoring and Discovery System v4
(MDS4) (Schopf et al., 2006). A Perl script pro-
duces XML descriptions of supported urgent services
that describe service usage policies. This information
can be used in conjunction with QoS information to
model the behavior of the service under current or ex-
pected conditions. These tools provide valuable in-
formation for time critical applications. It is essential
for end users to determine their authorized urgency
level so they can select the service that best accommo-
dates their needs. The registration of a Grid service’s
urgency parameters with information services allows
end users to verify service properties before a service
is used.
Grid service invocations are monitored through
our tools. All Grid service interactions, regardless
of their urgency, are monitored. We extended a Grid
service message handler distributed with Globus WS
CORE to log Web service communication into our
instrumentation database. We capture several fields
form each SOAP header sent or received by the ser-
vice container, including the messages UUID, the tar-
get service address, the service operation, the caller’s
Distinguished Name (DN), the UUID of a related
message, and the time the message was delivered to
our message handler. This handler is installed on the
service containers input and output message handler
chain so that every SOAP message is logged. This in-
formation is used by our policy and service manage-
ment tools to adapt policies based on current service
usage characteristics.
4.2 Urgent Grid Service Authorization
The framework that authorizes urgent access to Grid
services consists of custom Globus Grid service au-
thorization framework that integrates with urgent
computing authorization tools, such as SPRUCE. Our
authorization infrastructure consists of a custom Pol-
icy Decision Point (PDP) and a custom Policy In-
formation Point (PIP) that supports urgent access to
these services. The PDP is responsible for authoriz-
ing client access to Grid services and communicates
with our service management tools. When the PDP
evaluates the incoming service request, the DN for
the user invoking the service is verified and the ap-
propriate urgency is applied to their invocation of the
service. This verification occurs through execution
of the Urgent Authenticator, which is similar to the
SPRUCE Token Authentication script. Once the ur-
gency level has been verified, the PDP contacts the
Urgent Service Manager. The Urgent Service Man-
ager determines which policy to execute for the ser-
vice and returns this information to the PDP. The PDP
then executes the appropriate policy and permits the
service to execute.
ICSOFT 2008 - International Conference on Software and Data Technologies
4.3 Policy Enforcement and Adaptation
The Urgent Service Manager is responsible for deter-
mining which service management policy associates
with a particular user and urgency level. Once the ap-
propriate policy is identified and adapted to the cur-
rent operating environment, the Urgent Service Man-
ager communicates the policy to the Urgent PDP. The
Urgent Service Manager can simulate the executionof
Web services. We integrated the SimPy discrete event
simulation package into the Urgent Service Manager
client so that we can quickly simulate and experiment
with anticipated Web service requests ows. We en-
vision that the service management and simulation
components can integrate with other autonomic com-
puting tools to foster an automated, self-managing,
urgent computing environment.
We have currently defined and implemented three
service management policies:
Unmanaged. Requests are permitted as they are
received regardless of the urgency of the request.
Exclusive. Only the most urgent requests are al-
lowed. This policy is similar to strict priority
Shared. A mix of requests are permitted at vari-
able rates per urgency level.
The unmanaged policy allows all requests to com-
plete. The exclusive access policy only permits the
most urgent requests and stalls all other requests un-
til the urgent requests complete. The stalled requests
are not denied so that concurrent, less urgent work-
flows do not fail. The shared policy allows multiple
levels of urgent requests to execute and arbitrates the
frequency that the requests are permitted to execute.
Like the exclusive access policy, requests are not out-
right denied but are stalled to achieve allowable ser-
vice execution frequencies. Our PDP throttles service
requests at the authorization point and shapes the Grid
service request traffic.
Each of the defined policies has several configura-
tion parameters. For example, all policies have an ex-
ecution window parameter to define how long the pol-
icy should remain active since the last request of that
policy occurred. This can block non-urgentrequests if
more urgent requests are expected. The shared policy
defines traffic percentages and rates for each urgency
level. This parameter prevents non-urgent traffic from
starving urgent traffic. Multiple policies can be mixed
per service. For example, the most urgent policy for a
service can implement an exclusive policy for a spe-
cific user while the other policies can be applied to
other users.
Trace of Job Submissions to SDSC DataStar
Time (min)
104700 104900 105100 105300 105500
User ID
Normal Request
High Priority Request
Figure 3: SDSC DataStar trace illustrating jobs submitted
per user over a 16 hour interval.
Our evaluation of this service provisioning infrastruc-
ture occurred in two phases. In the first phase, we
evaluated service provisioning and request prioriti-
zation in our service management simulator. In the
second phase, we deployed our service provisioning
and management tools into the Globus Web service
container and evaluated the capabilities of the service
management tools with several services.
5.1 Policy Simulation
We performed more detailed analyses of our provi-
sioning software using our simulator and data from
the Parallel Workloads Archive (Feitelson, 2008a).
We used the trace of jobs submitted to SDSC’s DataS-
tar TeraGrid computational resource from March
2004 to April 2005. For our evaluation, we assume
that all jobs submitted to DataStar were using the
Globus WS GRAM Grid service so that we could ap-
proximate a workload trace for a Grid resource using
Grid services. WS GRAM manages Grid applications
running on Grid computational resources and invokes
other Grid services to transfer data between Grid re-
sources. Figure 3 illustrates job submission times for
a segment of the trace. Over the 16 hour period of
the trace, 1010 jobs were submitted to DataStar from
40 different users. From this trace, we studied the
performance and impact of our software by varying
the urgency of users within the trace. As an example,
when user 17 has high priority and exclusive access to
the resource while all other users have low priority ac-
cess, our software limits the low priority requests as
illustrated in Figure 4. In this example, the window
parameter of the high priority policy was set to one
hour and prevented low priority tasks from executing
a service within one hour of the last high priority re-
quest. Low priority tasks were also limited to four
Trace of Rate Limited Job Requests for SDSC DataStar
Time (min)
104700 104900 105100 105300 105500
Request rate (Job requests / min)
High Priority Request
High Priority Response
Normal Request
Normal Response
Figure 4: SDSC DataStar trace illustrating the effects of
urgent request throttling on job throughput over a 16 hour
service accesses per hour for all users.
The parameters that define higher priority poli-
cies impact the traffic throughput of the lower priority
policies. Figure 5 illustrates how the window param-
eter of the high priority policy affects all lower pri-
ority Grid service traffic. Expanding or contracting
the high priority window affects what traffic is per-
mitted to execute. Smaller windows allow lower pri-
ority requests to execute closer to the original service
request. For example, a non-urgenttraffic stream exe-
cuted concurrently with a high priority request con-
figured with a 30 minute window exclusive access
policy. Lower priority stream interleaved the higher
priority request stream. Larger windows delay non-
urgent requests longer and can prevent the interleav-
ing behavior of the shorter windows.
While the window parameter can reserve access
to the service if more requests are expected, it de-
grades the response time of the lower priority re-
quests. Figure 6 illustrates the response time of low
priority request over the hourly intervals in the sim-
ulation. Without a window, small delays of less than
one second still occur since the high-priority service
requests have exclusive access to the service until the
request completes. As the window values increase
in size for this simulation, the response time for the
lower priority requests increases as the requests ex-
ecute later in the simulation. Adapting the request
scheduling parameters, such as the access window, to
urgent workflows is necessary so that the services are
still highly utilized by all priorities.
Configuring the policies is left to the service host-
ing site. Since workloads and service usage patterns
can change as the workflows change, we are develop-
ing tools to automatically configure these policy pa-
rameters. We have begun to implement several ser-
vice request rate, load, and usage pattern forecasters
within the Urgent Service Manager. These forecasters
use time-series analysis techniques (Feitelson, 2008b)
and more recent forecasting techniques (Nurmi et al.,
2007) to predict the expected usage or response of a
Grid service for urgent computing workflows. These
Response Time of Non−Urgent Requests
Time (min)
104700 104900 105100 105300 105500
Average Response Time (s)
Response rate (0 min window)
Response rate (30 min window)
Response rate (60 min window)
Response rate (90 min window)
Response rate (120 min window)
Figure 6: Response time of normal priority SDSC DataStar
traffic delayed by higher priority traffic.
prediction tools are still under development, but the
initial results from these tools for tuning policy pa-
rameters are promising.
5.2 Deployment Experiences
To evaluate our urgent service provisioning frame-
work, we integrated our tools into several services,
evaluated our service models and provisioning poli-
cies with our simulator, and performed end-to-end
tests using our authorization and provisioning tools
on several deployed services. We first integrated our
authorization framework into several services, includ-
ing the Globus DefaultIndexService, a data catalog
service that utilizes the Globus Replica Location Ser-
vice (RLS), and a database query Grid service. With
all of these services, we were able to control access
to the Grid service with only minimal configuration.
The reconfiguration of the services included adding
the PDP and PIP to the service security configuration
so that the priority of the request could be validated.
The service manager was also configured so that the
priority validation requests from services running in
the container could check and schedule access to the
We are currently developing forecasting tools to au-
tomatically tune the service policies. This automatic
tuning and configuration work leverages techniques
that mine for patterns in Grid service usage and fore-
cast expected Grid service behavior or load. The work
we present in this paper does not address supporting
non-service oriented access to urgent data resources
and we leave this topic for future work. Addition-
ally, we are developing an autonomic data manage-
ment system to adapt and reconfigure storage man-
agers for urgent data requirements.
ICSOFT 2008 - International Conference on Software and Data Technologies
Impact of Varying Exclusive Access Policy Parameters on Non−Urgent Requests
Time (min)
104500 104700 104900 105100 105300 105500
Request and Response Rates (1 / min)
Request rate
Response rate (30 min window)
Response rate (60 min window)
Response rate (90 min window)
Response rate (120 min window)
Figure 5: SDSC DataStar trace illustrating the effects of urgent policy parameters on non-urgent request traffic.
We presented our approach to provisioning Grid ser-
vices for urgent computing applications and work-
flows. Our approach requires minimal service recon-
figuration for urgent use cases and can support a va-
riety of resources types managed by Grid services.
We evaluated our service management tools using the
core Globus data and resource management Grid ser-
vices. We demonstrated that these services can adapt
and respond to dynamic urgent computing require-
ments using our service and policy simulator. The
current scheduling parameters for urgent services can
provide near immediate access to these services at the
cost of increased response times for lowerpriority ser-
vice requests.
Support for this research and University of Colorado
computer time was provided by National Science
Foundation (NSF) MRI Grant #CNS-0421498, NSF
MRI Grant #CNS-0420873, NSF MRI Grant #CNS-
0420985, and a grant from the IBM Shared Univer-
sity Research (SUR) program. This research was sup-
ported in part by the NSF through support for the
National Center for Atmospheric Research and Ter-
aGrid resources provided by UC/ANL and NCAR.
We would like to thank the members of the SPRUCE
project, including Pete Beckman and Suman Nadella,
for their guidance and support of this research.
Al-Ali, R., Hafid, A., Rana, O., and Walker, D. (2004). An
approach for quality of service adaptation in service-
oriented Grids. Concurrency and Computation: Prac-
tice and Experience, 16(5):401–412.
Alfieri, R., Cecchini, R., Ciaschini, V., Dell’Agnello, L.,
Frohner, A., A. Gianoli, K. L., and Spataro, F. (2003).
VOMS, an Authorization System for Virtual Organi-
Allcock, B., Bester, J., Bresnahan, J., Chervenak, A.,
Foster, I., Kesselman, C., Meder, S., Nefedova, V.,
Quesnal, D., and Tuecke, S. (2002). Data Manage-
ment and Transfer in High Performance Computa-
tional Grid Environments. Parallel Computing Jour-
nal, 28(5):749 – 771.
Beckman, P., Beschatnikh, I., Nadella, S., and Trebon, N.
(2007). Building an Infrastructure for Urgent Com-
puting. High Performance Computing and Grids in
Benkner, S., Engelbrecht, G., Middleton, S., Brandic, I.,
and Schmidt, R. (2007). End-to-end qos support for
a medical grid service infrastucture. New Genera-
tion Computing, Computing Paradigms and Computa-
tional Intelligence, Special Issue on Life Science Grid
Computing, 25(4):355–372.
Bogden, P., Gale, T., Allen, G., MacLaren, J., Almes,
G., Creager, G., Bintz, J., Wright, L., Graber, H.,
Williams, N., Graves, S., Conover, H., Galluppi, K.,
Luettich, R., Perrie, W., Toulany, B., Sheng, Y., Davis,
J., Wang, H., and Forrest, D. (2007). Architecture of a
Community Infrastructure for Predicting and Analyz-
ing Coastal Inundation. Marine Technology Society
Journal, 41(1):53–71.
Catlett, C., Andrews, P., Bair, R., and et al. (2007). Ter-
aGrid: Analysis of Organization, System Architec-
ture, and Middleware Enabling New Types of Appli-
cations. High Performance Computing and Grids in
Chadwick, D., Novikov, A., and Otenko, A. (2006). Grid-
Shib and PERMIS Integration. Campus-Wide Infor-
mation Systems, 23(4):297–308.
Cope, J. and Tufo, H. (2008). A Data Management Frame-
work for Urgent Geoscience Workflows. In Proceed-
ings of the International Conference on Computa-
tional Science (ICCS 2008).
Cui, Y., Moore, R., Olsen, K., Chourasia, A., Maechling,
P., Minster, B., Day, S., Hu, Y., Zhu, J., Majumdar,
A., and Jordan, T. (2007). Enabling Very–Large Scale
Earthquake Simulations on Parallel Machines. In Pro-
ceedings of the International Conference on Computa-
tional Science (ICCS) 2007, Beijing, China. Springer.
Dan, A., Davis, D., Kearney, R., Keller, A., King, R., Kue-
bler, D., Ludwig, H., Polan, M., Spreitzer, M., and
Youssef, A. (2004). Web services on demand: WSLA-
driven automated management. IBM Systems Journal,
Droegemeier, K., Chandrasekar, V., Clark, R., Gannon, D.,
Graves, S., Joesph, E., Ramamurthy, M., Wilhelmson,
R., Brewster, K., Domenico, B., Leyton, T., Morris,
V., Murray, D., Pale, B., Ramachandran, R., Reed,
D., Rushing, J., Weber, D., Wilson, A., Xue, M.,
and Yalda, S. (2004). Linked Environments for at-
mospheric discovery (LEAD): A Cyberinfrastructure
for Mesoscale Meteorology Research and Education.
In Proceedings of the 20th Conference on Interac-
tive Information Processing Systems for Meteorology,
Oceanography, and Hydrology, Seattle, WA. Ameri-
can Meteorological Society.
Erradi, A. and Maheshwari, P. (2007). Enhancing web ser-
vices performance using adaptive quality of service
management. In Proceedings of the 8th International
Conference on Web Information Systems Engineering
(WISE 2007).
Eubank, S., Kumar, V. A., Marathe, M., Srinivasan, A., and
Wang, N. (2006). Structure of Social Contact Net-
works and Their Impact on Epidemics. AMS-DIMACS
Special Volume on Epidemiology, 70:181–213.
Feitelson, D. (2008a). Parallel workloads archive,
Feitelson, D. (2008b). Workload modeling for computer
systems performance evaluations.
Foster, I. (2005). Globus Toolkit Version 4: Soft-
ware for Service-Oriented systems. In IFIP Interna-
tional Conference on Network and Parallel Comput-
ing. Springer-Verlag.
Lang, B., Foster, I., Siebenlist, F., Ananthakrishnan, R.,
and Freeman, T. (2006). A Multipolicy Authorization
Framework for Grid Security. In Proceedings of the
Fifth IEEE Symposium on Network Computing and
Application, Cambridge, MA, USA.
Lederer, H., Pringle, G. J., Girou, D., Hermanns, M. A.,
and Erbacci, G. (2007). Deisa: Extreme computing in
an advanced supercomputing environment. Parallel
Computing: Architectures, Algorithms and Applica-
tions, 38:687–688.
Liu, Y., Ngu, A., and Zeng, L. (2004). QoS Computation
and Policing in Dynamic Web Service Selection. In
Proceedings of the 13th International Conference on
World Wide Web 2004 (WWW2004).
Mandel, J., Beezley, J., Bennethum, L., Chakraborty, S.,
Coen, J., Douglas, C., Hatcher, J., Kim, M., and Vo-
dacek, A. (2007). A Dynamic Data Driven Wildland
Fire Model. In Proceedings of the International Con-
ference on Computational Science (ICCS) 2007, pages
1024–1049, Beijing, China.
Nurmi, D., Brevik, J., and Wolski, R. (2007). QBETS:
Queue Bounds Estimation from Time Series. In Pro-
ceedings of the 13th Workshop on Job Scheduling
Strategies for Parallel Processing.
Schopf, J., Pearlman, L., Miller, N., Kesselman, C., Foster,
I., D’Arcy, M., and Chervenak, A. (2006). Monitoring
the Grid with the Globus Toolkit MDS4. In Proceed-
ings of SciDAC 2006.
Sharma, A., Adarkar, H., and Sengupta, S. (2003). Manag-
ing qos through prioritization in web services.
Truong, H., Samborski, R., and Fahringer, T. (2006). To-
wards a Framework for Monitoring and Analyzing
QoS Metrics of Grid Services. In Proceedings of the
Second IEEE International Conference on e-Science
and Grid Computing.
Wang, G., Wang, C., Chen, A., Wang, H., Fung, C.,
Uczekaj, S., Chen, Y., Guthmiller, W., and Lee, J.
(2005). Service level managment using QoS moni-
toring, diagnostics, and adaptation for networked en-
terprise systems. In Proceedings of the Ninth IEEE In-
ternational EDOC Enterprise Computing Conference.
Wolski, R., Obertelli, G., Allen, M., Numri, D., and Brevik,
J. (2005). Predicting grid resource performance on–
line. Handbook of Innovative Computing: Models,
Enabling Technologies, and Applications.
Zhou, X., Wei, J., and Xu, C. (2007). Quality-of-service
differentiation on the internet: a taxonomy. Journal of
Network and Computer Applications, 30(1):354–383.
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