Time-value curves to provide dynamic QoS for time sensitive file transfer

US008146090B2
(12) Unlted States Patent (10) Patent No.: US 8,146,090 B2
Travostino et a]. 45 Date of Patent: Mar. 27 2012 a
(54) TIME-VALUE CURVES TO PROVIDE 6,654,346 B1 * 11/2003 Mahalingaiah et a1. .... .. 370/235
DYNAMIC QOS FOR TIME SENSITIVE FILE 6,665,701 B1 * 12/2003 Combs et al. ........ .. 718/104
TRANSFER 7,093,250 B1 * 8/2006 Rector ......... .. 718/100
7,369,912 B2 * 5/2008 Sherriff et al. . 700/100
_ _ 7,373,410 B2 * 5/2008 Monza et al. .. 709/229
(75) Inventors: Franco Travost1no,Ar11ngton, MA 7,765,028 B2 * 7/2010 Orita ............ .. 700/248
(US); Tal Lavian, Sunnyvale, CA (US); 2002/0082856 A1 * 6/2002 Gray et a1. 705/1
Bruce scho?eld Tyngsboro MA 2003/0046324 A1 * 3/2003 Suzuki et a1. .. 709/100
’ ’ ’ 2003/0061260 A1 * 3/2003 Rajkumar ........ .. 709/104
Inder Monga’ Acton’ MA (Us) 2004/0064817 A1 * 4/2004 Shibayama et al. 718/104
_ _ 2004/0073643 A1 * 4/2004 Hayes et a1. ..... .. 709/223
(73) Asslgneel Rockstar Bldco, LP, New York, NY 2004/0117791 A1 * 6/2004 Prasad et al. ................ .. 718/100
(Us) (Continued)
( * ) Notice: Subject to any disclaimer, the term of this
patent is extended or adjusted under 35 OTHER PUBLICATIONS
U.S.C. 154(b) by 1565 days. Camorlinga, et al., Modeling of Work?oW-Engaged Networks on
Radiology Transfers Across a Metro Network, IEEE Transactions on
(21) Appl- N05 11/469,404 Information Technology in Biomedicine, vol. 10, No. 2, Apr. 2006.
. 275-281. (22) Filed: Aug. 31, 2006 pp
(Continued)
(65) Prior Publication Data
Us Zoos/0040630 A1 Feb 14 2008 Primary Examiner * Emerson Puente
’ Assistant Examiner * Adam Lee
Related US. Application Data (74) Attorney, Agent, or Firm * Christopher & Weisberg,
(60) Provisional application No. 60/721,757, ?led on Sep. P'A'
29’ 2005' (57) ABSTRACT
(51) Int. C]. A method and apparatus has been shoWn and described Which
G06F 9/46 (2006.01) alloWs Quality of Service to be controlled at a temporal
G06F 15/173 (2006.01) granularity. Time-value curves, generated for each task,
(52) us CL ______ __ 718/104; 718/105; 709/223; 709/224; ensure that mission resources are utilized in a manner Which
709/225; 709/226 optimizes mission performance. It should be noted, hoWever,
(58) Field of Classi?cation Search ...................... .. None that although the Present invention has Shown and described
See application ?le for Complete Search history the use of time-value curves as applied to mission Work?oW
tasks, the present invention is not limited to this application;
(56) References Cited rather, it can be readily appreciated by one of skill in the art
that time-value curves may be used to optimize the delivery of
US. PATENT DOCUMENTS any resource to any consumer by taking into account the
5,402,423 A * 3/1995 Van Kersen et a1‘ ' “ 370/419 dynamic environment of the consumer and resource.
5,600,822 A * 2/1997 Grice et a1. ........ .. 712/16
6,446,123 B1 * 9/2002 Ballantine et a1. .......... .. 709/224 14 Claims, 14 Drawing Sheets
P(riPori)ty

US 8,146,090 B2
Page 2
US. PATENT DOCUMENTS
2006/0195847 A1* 8/2006 Amano et al. .............. .. 718/103
2006/0277548 A1* 12/2006 Abe ....... .. . 718/104
2006/0294522 A1* 12/2006 Havens ....................... .. 718/103
OTHER PUBLICATIONS
Lavian, et al., A Platform for Large-Scale Grid Data Service on
Dynamic High-Performance Networks, Nortel Networks Labs, et a1 .,
pp. 1-10.
Lavian, et al., DWDM-RAM: A Data Intensive Gird Service Archi
tecture Enabled by Dynamic Optical Networks, 2004 IEEE Interna
tional Symposium on Cluster Computing and the Grid, pp. 762-764.
Lavian, et al., An Extensible, Programmable, Commercial-Grade
Platform for Internet Service Architecture, IEEE Transactions on
Systems, Man, and CyberneticsiPart C: Applications and Reviews,
vol. 34, No. 1, Feb. 2004, pp. 58-68.
Clark, et al., An Adaptive, Distributed Airborne Tracking System,
MITRE Corporation, et al., pp. 1-10.
Hollingsworth, Work?ow Management Coalition, The Work?ow
Reference Model, Jan. 19, 1995, pp. 1-68.
The Work?ow Management Coalition, Work?ow Management Coa
lition Work?ow Standard Work?ow Process De?nition, Oct. 25,
2002, Version 1.0, pp. 1-114.
Hayes, et al. Work?ow Interoperabiltiy Standards for the Internet,
IEEE Internet Computing, May . Jun. 2000, pp. 37-45.
Hagen, et al., Exception Handling in Work?ow Management Sys
tems, IEEE Transactions on Software Engineering, vol. 26, No. 10,
Oct. 2000, pp. 943-958.
Krishnan, et al., GSFL: A Work?ow Framework for Grid Services,
Mathematics and Computer Science Division, Argonne National
Laboratory, Argonne, IL, et al., pp. 1-13.
Cao, et al., GridF low: Work?ow Management for Grid Computing,
Proceedings of the 3rd IEEE/ACM Internation Symposium on Cluster
Computing and the Grid, 2003, pp. 1-8.
Deelman, et al., Mapping Abstract Complex Work?ows onto Grid
Environments, Journal of Grid Computing, 2003, pp. 25-39.
Simeonidou, et al., Optical Network Infrastructure for Grid, Global
Grid Forum 2002, pp. 1-55.
Pan, et al., Time in OWL-S, University of Southern California/Infor
mation Sciences Institute, pp. 1-8.
Verma, et al., The METEOR-S Approach for con?guring and Execut
ing Dynamic Web Processes, pp. 1-12.
Von LasZewski, et al., GridAnt: A Client-Controllable Grid Work?ow
System., Argonne National Laboratory Preprint ANL/MCS-P1098
1003, Jan. 2004, pp. 1-10.
Arnaud, et a1 ., Customer Controlled and Managed Optical Networks,
Jan. 15, 2003, pp. 1-11.
Mambretti , et al., The Photonic TeraStream: enabling next genera
tion applications through intelligent optical networking at
iGRID2002, Elsevier Science B.V, 2003, pp. 897-908.
* cited by examiner

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US 8,146,090 B2
1
TIME-VALUE CURVES TO PROVIDE
DYNAMIC QOS FOR TIME SENSITIVE FILE
TRANSFER
RELATED APPLICATIONS
This application claims priority under 37 C.F.R. §l.l 19(e)
to provisional patent application 60/721,757 ?led Sep. 29,
2005 and incorporated herein by reference.
STATEMENT REGARDING FEDERALLY
SPONSORED RESEARCH OR DEVELOPMENT
The US. Government has a paid-up license in this inven
tion and the right in limited circumstances to require the
patent oWner to license others on reasonable terms as pro
vided for by the terms of contract No. HR00ll-05-c-0l20
aWarded by DARPA/CMO.
FIELD OF THE INVENTION
This invention relates generally to resource management
and more particularly to a method and apparatus for generat
ing and using time-value curves for resource management.
BACKGROUND OF THE INVENTION
The effectiveness of any mission is heavily reliant upon the
ability of an underlying infrastructure to respond to the
dynamic requirements of the mission. Typically missions are
layered upon an existing resource infrastructure such that the
mission becomes merely a set of tasks that is supported by the
infrastructure. Layering a mission upon an existing infra
structure typically mis-utiliZes key resources and increases
the dif?culty in detecting performance degradation or partial
failures that adversely affect the mission. Allocating speci?c
resources to a mission is technically challenging and error
prone. It Would be desirable to identify a mission architecture
Which Would overcome the problems of the prior art.
SUMMARY OF THE INVENTION
According to one aspect of the invention a method of
allocating a resource to a plurality of resource consumers
includes the step of generating a time-value curve de?ning a
temporally dynamic priority of a resource consumer over a
time period and using the time-value curve to allocate the
resource to the consumer over the time period. In one embodi
ment, the resource is communication bandWidth, and the
resource consumers include one or more tasks in a mission
Work?oW. Such an arrangement permits the scheduling, pre
empting and trade-off of bandWidth betWeen different mis
sion tasks to optimiZe deployment of mission tasks and con
comitantly optimiZe mission performance.
According to another aspect of the invention, a method of
allocating a resource to a plurality of resource consumers
includes the steps of generating a time value curve for each
one of a plurality of tasks in a Work?oW, the time-value curve
de?ning a temporally dynamic quality of service to be pro
vided to the task.
According to a further aspect of the invention, a policy
engine comprises generic task pro?le information and time
value curve generation logic operably coupled to receive task
speci?c information and to calculate a time-value curve for
the task using the generic task pro?le information and the task
speci?c information.
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According to a further aspect of the invention, a device
comprising a computer readable medium accessible by a
resource allocator is described. The computer readable
medium stores a data structure comprising, for each one of a
plurality of enqueued tasks associated With the resource, a
time value curve de?ning a temporal priority to provide to the
associated task, the data structure being accessed by the
resource controller to control access to the resource. These
and other aspects of the invention Will be described With
regard to the attached ?gures.
BRIEF DESCRIPTION OF THE FIGURES
FIG. 1 is a block diagram of a mission architecture in Which
the present invention may be implemented;
FIG. 2 is a block diagram illustrating several components
that may be included in a policy engine to generate time-value
curves of the present invention;
FIGS. 3A-3H are diagrams illustrating different types of
time-value curves that may used as a basis for time-curve
generation by a policy engine of the present invention;
FIG. 4 is a How diagram illustrating exemplary steps that
may be performed by the policy engine of FIG. 2 to generate
time-value curves for one or more tasks in a Work?oW;
FIG. 5 is a diagram of an exemplary netWork or service
resource manager Which may use the time value curves of the
present invention to control allocation of resources to Work
How tasks;
FIG. 6 is a timing diagram illustrating several exemplary
time-value curves that may be assigned to tasks in a Work
How;
FIGS. 7A and 7B are timing diagrams provided to illustrate
the addition of time value curves as tasks are added to a
Work?oW;
FIGS. 8A and 8B are used to describe an embodiment of
the present invention supporting a surveillance Work?oW;
FIG. 9 is a processing timing diagram Which pictorially
illustrates the bandWidth smoothing capabilities of time
value QoS servicing of a Work?oW;
FIG. 10 is a pictorial representation of task selection using
time-value curves in the example of FIG. 9;
FIGS. 11A and 11B are timing diagrams used to illustrate
a stalled task drop capability of the present invention;
FIG. 12 is a How diagram illustrating exemplary steps that
may be performed by a policy engine performing task drop
ping such as that illustrated in FIGS. 11A and 11B;
FIG. 13 is a graph illustrating the performance of TV QoS
versus Fixed QoS as the offered load increases; and
FIG. 14 is a graph illustrating hoW TV QoS provides for
graceful netWork failure.
DETAILED DESCRIPTION
FIG. 1 illustrates a mission architecture 1 in Which the
present invention may advantageously be used to optimiZe
mission resource utiliZation. In one embodiment the mission
architecture 1 is an adaptive netWork that uses Work?oW
Locked Loops (WLL) for mission resource selection and
access management. The embodiment of FIG. 1 includes a
Mission Resource Manager (MRM) 10 and a SoftWare Con
trolled NetWork (SCN) 12. The MRM 10 essentially orches
trates the use of mission resources by a mission by identifying
mission tasks and allocating mission resources to the mission
tasks. Although the MRM may be described beloW as includ
ing certain components, it should be understood that an MRM
is not a required to implement the present invention. For
example, a set of Mission Goals statements may be input into

US 8,146,090 B2
3
the Policy Engine to help form the Time-Value curves via an
administrator at a GUI or automated via a machine interface
or even a Web Services Interface.
An exemplary MRM 10 includes a Goal to Service trans
lation Unit 12. The Goal to Service translation Unit may be a
semantic tool Which maps a high level goal de?nition, such as
a natural language de?nition, into real-language mission
goals into policy statements. One exemplary method for per
forming Goal to Service translation is described in Ser. No.
1 1/469,416, entitled Mission Goal Statement to Policy State
ment Translation, by Travostino et al, ?led Aug. 31, 2006,
incorporated herein by reference.
Policy statements are translated into Work?oWs using the
Goal to Service Translation Unit (GSTU) 12, the Constraint
Analyzer (CA) 15, the Discovery Engine and Scheduler
(DES) 13 and the Directory 16. Together these components
identify, quantify and qualify services that are reserved for a
mission as described in patent application Ser. No. 11/469,
422 for Work?oW Locked Loops to Enable Adaptive Net
Works, by Travostino et al, ?led Aug. 31, 2006.
For example in one embodiment the directory 16 identi?es
netWork resources and services that may be allocated to mis
sions. The DES 13 identi?es resources and services that are
available for use as an infrastructure of the mission. The CA
15 evaluates the infrastructure identi?ed by the DES 13 in
vieW of mission constraints. The CA 15 and DES 13 Work in
concert With the goal translator to identify the mission infra
structure. An Engine uses the infrastructure and mission
information to generate mission Work?oWs, Which are for
Warded to the SCN 14.
The SCN 14 generally controls the execution of the mis
sion and the use of the mission resources during execution of
the mission. The SCN 14 includes an Execution Manager
(EM) 22, a Policy Engine (PE) 24, a Network Services Man
ager (26), a Network Resource Manager (NRM) 28 and a
NetWork Resource and Topology Discovery resource 27.
Together these components orchestrate the execution of the
mission Work?oW using the identi?ed mission resources.
For the purpose of this application a mission Work?oW is a
collection of tasks that are performed by a collection of
actors, Wherein an actor may be any entity that uses a mission
dedicated resource. A mission dedicated resource is any
resource that is at least partially dedicated to a mission.
Mission tasks are dynamic, and therefore during the course
of the mission different tasks are initiated and terminated. At
any given time during the mission, mission tasks may seek
access to a shared mission resource. According to one aspect
of the invention, each task has an associated time-value curve
indicating a temporal priority of the task over the task life
time. The time-value curves are used during resource alloca
tion to intelligently allocate resources to tasks to optimiZe the
performance of the mission. Each point in a time-value curve
identi?es a priority of QoS to be associated With the task for
the point in time. This alloWs QoS to be adjusted in real time
based on conditions in the ?eld, thereby adding the degree of
temporal granularity to the QoS structure. Controlling tem
poral granularity of QoS helps resolve the con?icts of con
gestion and While supplying all the resources to the mission
Within the mission time-frame, Without having to expand the
siZe of the netWork.
In one embodiment the PE 24 generates a time-value curve
for each mission task. The time value curves are forWarded to
the EM 22.At resource allocation intervals the EM references
the relative values of the time-value curve of each tasks that
are active at that time instance to retrieve a time-value for each
task, Wherein an active task is any task that has been initiated
and not terminated. Using any one of a variety of selection
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methods, such as highest value, highest Weighted value, etc.,
the time values are compared to select one of the active tasks.
FIG. 2 illustrates several exemplary components that may
be included in a PE to generate time-value curves for tasks.
The PE 24 includes a Work?oW parser 31, a generic task
pro?le store 34, and time-value (TV) curve generation logic
32. The Work?oW parser receives Work?oW tasks from the
Execution Machine (EM). The task may have been initiated in
response to a mission trigger; for example, in a surveillance
mission, a mission trigger may be the capturing of an image at
a sensor point in a surveillance area. The tasks may be trig
gered by partial or thorough completion of another task.
HoWever the task is triggered, it is forWarded to the Work?oW
parser 31 Which extracts task information that may be used to
build the time-value curve. Any obtainable information Which
may affect a priority or quality of service to provide to the task
may be provided to the time-value curve generation logic,
including, for example, information regarding an actor of the
task, a location of the task, a security level of the task, con
straints of the tasks, dependencies of the task, poWer require
ments of the task, execution time for the task, etc. Information
regarding services and netWork elements that are associated
With the task may also be used When calculating the time
value curve. The present invention is in no Way limited to the
use of any particular task information in generating a time
value curve for priority scheduling of the task.
The generic task pro?le store 34 may be used to store
pre-determined task pro?les. The predetermined task pro?les
are tailored to the resource requirements of various pre-de
?ned tasks. For example, a surveillance task that performs
high resolution image capture Would have a time-value curve
that temporally aligns the resource priority With the resource
usage characteristics of the task. The resource requirements
of a task may be selected from a set of pre-de?ned pro?les, or
obtained by monitoring and pro?ling resource usage in
advance to generate a time-value pro?le for the task. FIGS.
3A-3H illustrates several representative time value curve pro
?les. A time value-curve pro?le such as that of FIGS. 3A and
3B may be applied to a speci?c task identi?ed as having a
relatively ?at QoS. Pro?les 3C and 3D may be used to apply
a linear QoS to a task. Impulse pro?les 3E and 3E may be used
to provide high priority to a task for a short time. Time value
curves of 7G and 7H illustrate curves that are constructed for
a particular QoS pro?le.
FIG. 4 illustrates an exemplary process that may be per
formed by the PE 24 to construct a time-value curve for a task.
At step 42 a generic pro?le associated With the task is
retrieved from the pro?le store 34. At step 44, the time-value
curve is piece-Wise constructed using the base pro?le in con
junction With task speci?c variables, constraints and resource
information using techniques knoWn to those in the art. At
step 46 the time-value curve is stored With the task. The TV
augmented task is then forWarded to the EM 22.
FIG. 5 illustrates exemplary components that may be
included in an Execution Manager 22 that uses the time-value
curves of the invention. The EM 22 Would generally include
task queues 52 for storing active mission tasks. The task
queues may be organiZed by mission resource. A resource
controller 56 may include or otherWise have access to the
Time-Value curves for the tasks, for example in a time-value
queue associated With the task queue. At each mission
resource allocation instance the resource controller examines
the time-values of each active task associated With a resource
at that instance, and selects one of the tasks based on the
time-value curve.

US 8,146,090 B2
5
FIG. 6 is a timing diagram illustrating illustrative time
value curves for 3 tasksA (62), B (64) and C (66). The generic
task pro?le for each of the curves is shown by below Equation
I:
P(T):Min(mT+b,MaxP) Equation I
Where P is the priority, or quality of service, associated
with the task at time T, m is derived based on the value of the
task, the lifetime of the task and the execution time of the task
and b is determined according to a value of the task. The ?at
portion on each of the time-value curves in this embodiment
represents the time needed to complete the task. Max (P)
represents the highest priority to allocate to the task.
Thus in FIG. 6 time value curve C is a lower value task,
which takes a relatively short period of time to complete.
Curve B has the highest initial priority task, with a shorter
lifetime than curve C. Curve A has the second highest initial
priority, but the shortest lifetime. Using strict priority alloca
tion technique, task B will obtain access to the resource until
time T1. At time T1, access begins to be granted to task A,
with taskA reaching its maximum priority at time 2 to allow
it to be at maximum priority for the time period associated
with executing the task. The time-value curves may thus be
used provide a temporal priority of the task which ensures that
the task can complete in its lifetime.
FIGS. 7A and 7B are pictorial representations of a mission
work?ow, provided to illustrate the dynamic nature of work
?ow tasks; tasks are continually instantiated and terminated
during the work?ow. For example, at time T4, a low priority
task 68 is triggered. At time T7 a higher priority task is
triggered. At any resource allocation period, the time value
curves may be used to real-time, dynamic resource manage
ment for a dynamic mission work?ow.
FIGS. 8A and 8B will be used to describe an exemplary
embodiment of the present invention for use in a particular
mission. The mission of FIG. 8 is a surveillance mission
which seeks to identify individuals within a de?ned radius.
The mission comprises dependent tasks, with a ?rst set of
tasks being associated with the detection and collection of
information for each an individual that comes within a radius,
a second set of tasks being associated with collecting high
resolution information regarding individuals within the
radius, a third set of tasks being associated with collecting
increasingly images at very high resolution. Each of the tasks
of in the mission use network bandwidth for the exchange of
information associated with the target. The individuals are
grouped into low value targets (initial priority 0); mid value
targets (initial priority value 1) and high value targets (initial
priority value 2). The targets are detected using mission
resource sensors distributed within the radius. A detection of
the target results in an attempt to collect information about the
target to determine if the target is a target of interest. The
collection of information may include the transmission of
identi?cation information, such as a low resolution or high
resolution image. The type of collection to be performed for
each target is a task in the above described architecture. The
task is assigned a time-value curve which provides a temporal
priority to the collection of information for the particular
target.
The time value curves of FIG. 8B are associated with tasks
to be performed on three targets. A ?rst task 72 is associated
with a mid priority target which has been sensed in area DS3.
There are only 3 minutes to collect information regarding the
target before the target leaves the area. Thus the time-value
curve is adapted for this target to ensure that data collection
for this target can occur within the target tracking task life
time. A second task 74 is associated with a target in area DSa.
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The target has been identi?ed as a high value target, perhaps
as a result of a low-resolution image mapping task. The target
is exiting the area in 4 minutes. The slope and intercept of the
time-value curve re?ect the perceived target value and
detected target location, and ensures that adequate processing
of the target may be completed before the target is lost. Time
value curve 76 is associated with a task generated in response
to target detection in area DS2b. There are 5 minutes until the
target quits the area, and thus time-value curve 76 re?ects the
lower quality of service level to be provided to the collection
of information regarding this target.
FIG. 9 is a data ?ow diagram intuitively illustrating the
bene?ts of using the time-value curves of the present inven
tion to provide real-time temporally prioritized resource allo
cation in to mission tasks. As will be seen from the below
description, the time-value curves effectively smooth and
optimiZe resource allocation among the tasks. Optimizing the
resource allocation improves the completion rate of mission
tasks and increases the chances of mission success.
Data series 80 illustrates a stream of resource requests that
are received in a data stream. The performance of the mission
depends upon being able to process each task in the data
stream during its lifetime; if tasks are dropped or delayed,
mission information is lost. In FIG. 9, each data slot repre
sents a one minute interval, and assume that the Burst traf?c
received in interval 1 includes high, medium and low priority
tasks having respective lifetimes of three, four and ?ve min
utes.
Data ?ow 90 illustrates an exemplary result of resource
allocation for a mission using Fixed QoS resource allocation
methods. In a typical Fixed QoS resource allocation method,
a ?xed bandwidth percentage is allocated to each task. During
interval 1 of Data ?ow 90, Burst Traf?c for three tasks is
received. The prior art resource allocator, receiving no infor
mation from the ?eld regarding a temporal priority of the
tasks, uses a Fixed QoS to allocate resources to the tasks.
Each of the three tasks is provided one third of the bandwidth.
At the end of interval 1 (or one minute into the task), each of
the three pending tasks in group A is 1/3 complete. During
interval 2, an additional task group B is received which
includes two additional tasks. Assume group B includes a
medium and high priority task with respective lifetimes of
four and ?ve minutes. During interval 2 the resource allocator
allocates the resource equally among the tasks in group A and
group B, providing one ?fth of the bandwidth to each task.
The Fixed QoS structure has no way to determine which task
is temporally more important due to conditions in the ?eld.
After two minutes, the tasks in group A are now just over half
completed. During interval 3, an additional task is received,
and each task in interval 3 is assigned one sixth of the band
width. At the end of three minutes the high priority task of
groupA is dropped. Using the example of FIG. 8A, this would
equate with tasks initiated for individuals in DS3 failing to
complete before the individual had left the radius.
In contrast, when time value curves are applied to the QoS
paradigm, resource allocation would proceed as is shown by
data ?ow 95; FIG. 10 is a pictorial representation of the
time-value curves per task for the data ?ow of FIG. 9. During
interval 1, when burst task set A is received a resource allo
cator of the present invention compares the time-value curves
of each task to identify the task with temporal priority. As
shown in FIG. 10, during interval 1 the high level task is given
control of the resource. During interval 2, new tasks are
added. However, during time interval 2 the time value curve
of the mid-range task of task group A has temporal priority.
Thus the mid-level task of group A is completed at two min
utes. At interval 3, at least a portion of the mid-level task of

US 8,146,090 B2
7
group B is allocated access to the resource. Thus it can be seen
that integrating time value curves into the policy and quality
of service enforcement decisions increases the completion
rate of high value tasks.
Time-value curves can also used to identify tasks that may
not successfully complete in their lifetime. These tasks are
hereinafter referred to as stalled tasks. Identifying stalled
tasks allows them to be removed from resource allocation
consideration, thereby reducing performance and providing a
graceful degradation of performance in the face of conges
tion.
FIG. 11A illustrates how dynamic time-value curve analy
sis may be used to identify stalled tasks. In FIG. 11A, task 126
is initiated at time. However, given the temporal priorities of
the other tasks in the work?ow, it is unlikely that the low
priority task 129 will be able to complete in its lifetime. In the
present invention, the Execution Manager identi?es stalled
?ows and removes them from the task queue, thereby simpli
fying the decision making process of the EM and reducing
congestion. FIG. 11B is a pictorial representation of the time
value curves that are under consideration following the abor
tion of the stalled task.
FIG. 12 is a ?ow diagram illustrating several exemplary
steps that may be performed by an Execution Manager or
other resource allocator, to drop task execution using time
value curve information of the present invention. At steps 132
and 134 a time value curve is generated for each new task as
described with regards to FIGS. 2-4. At step 136 it is deter
mined whether the added task is going to result in the stalling
of an existing task. If so, at step 138 the stalled task is removed
from the task queue.
FIG. 13 is a graph that is provided to illustrate the perfor
mance of a TV QoS system versus that of a ?xed QoS as the
offered load increases. As shown in FIG. 13, the number of
missed targets increases linearly with the increase in load. In
contrast, the TV QoS does not begin to drop targets until the
average tra?ic approaches the offered load (as indicated by
the dashed line X in FIG. 13).
FIG. 14 is a graph that is provided to illustrate the graceful
performance degradation that can be realiZed in a network
which implements time-value curves of the present invention
to abort stalled queues. As the siZe of the packet payload is
reduced the frequency of resource accesses is increased. Once
the offered load is less than the average tra?ic, resource
congestion quickly ensues, and data stream are dropped. The
present invention uses time-value curves to identify the most
temporally relevant tasks even during congestion, thereby
resulting in graceful network performance degradation in the
face of congestion as shown in FIG. 13.
Accordingly a method and apparatus has been shown and
described which allows Quality of Service to be controlled at
a temporal granularity. Time-value curves, generated for each
task, ensure that mission resources are utiliZed in a manner
which optimiZes mission performance. It should be noted,
however, that although the present invention has shown and
described the use of time-value curves as applied to mission
work?ow tasks, the present invention is not limited to this
application; rather, it can be readily appreciated by one of
skill in the art that time-value curves may be used to optimiZe
the delivery of any resource to any consumer by taking into
account the dynamic environment of the consumer and
resource. The allocation of any resource, including network
bandwidth, storage, processing capability, etc., may bene?t
from the teachings of the present invention.
Having described various embodiments of the invention, it
will be appreciated that many of the above ?gures are ?ow
chart illustrations of methods, apparatus (systems) and com
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25
30
35
40
45
50
55
60
65
8
puter program products according to an embodiment of the
invention. It will be understood that each block of the ?ow
chart illustrations, and combinations of blocks in the ?ow
chart illustrations, can be implemented by computer program
instructions. These computer program instructions may be
loaded onto a computer or other programmable data process
ing apparatus to produce a machine, such that the instructions
which execute on the computer or other programmable data
processing apparatus create means for implementing the
functions speci?ed in the ?owchart block or blocks. These
computer program instructions may also be stored in a com
puter-readable memory that can direct a computer or other
programmable data processing apparatus to function in a
particular manner, such that the instructions stored in the
computer-readable memory produce an article of manufac
ture including instruction means which implement the func
tion speci?ed in the ?owchart block or blocks. The computer
program instructions may also be loaded onto a computer or
other programmable data processing apparatus to cause a
series of operational steps to be performed on the computer or
other programmable apparatus to produce a computer imple
mented process such that the instructions which execute on
the computer or other programmable apparatus provide steps
for implementing the functions speci?ed in the ?owchart
block or blocks.
Those skilled in the art should readily appreciate that pro
grams de?ning the functions of the present invention can be
delivered to a computer in many forms; including, but not
limited to: (a) information permanently stored on non-writ
able storage media (eg read only memory devices within a
computer such as ROM or CD-ROM disks readable by a
computer I/O attachment); (b) information alterably stored on
writable storage media (e. g. ?oppy disks and hard drives); or
(c) information conveyed to a computer through communi
cation media for example using baseband signaling or broad
band signaling techniques, including carrier wave signaling
techniques, such as over computer or telephone networks via
a modem
The above description and ?gures have included various
process steps and components that are illustrative of opera
tions that are performed by the present invention. These com
ponents may be implemented in hardware, software or a
combination thereof. However, although certain components
and steps have been described, it is understood that the
descriptions are representative only, other functional delinea
tions or additional steps and components can be added by one
of skill in the art, and thus the present invention should not be
limited to the speci?c embodiments disclosed. In addition it is
understood that the various representational elements may be
implemented in hardware, software running on a computer, or
a combination thereof.
While the invention is described through the above exem
plary embodiments, it will be understoodby those of ordinary
skill in the art that modi?cation to and variation of the illus
trated embodiments may be made without departing from the
inventive concepts herein disclosed. Accordingly, the inven
tion should not be viewed as limited except by the scope and
spirit of the appended claims.
The invention claimed is:
1. A method of allocating a resource to a plurality of
resource consumers includes the steps of:
generating, with a network device, a time-value curve
de?ning a temporally dynamic priority of a resource
consumer over a time period, wherein the priority is
calculated as a function of time; and
using the time-value curve to allocate access to the
resource to the consumer over the time period.

Time-value curves to provide dynamic QoS for time sensitive file transfer

Time-value curves to provide dynamic QoS for time sensitive file transfer

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