Elastic Threshold-based Admission Control for QoS Satisfaction in Wireless Networks

with Reward Optimization for Multiple Priority Classes

April 6, 2010

M. ConlanA. Moini

Content

BackgroundKey QoS metrics for wireless cellular networksCall Admission Control (CAC) algorithmsElastic threshold-based CAC algorithmSystem modelPerformance modelAnalysis resultsComparison with other CAC algorithmsConclusionsReferences

Background

Mobile wireless networks must increasingly carry multiple classes of services with distinct Quality of Service (QoS) requirements• real-time multimedia services

Standard voice calls Streaming video/audio

• non-real-time services SMS text messages Picture mail Email

Network providers need a method for optimizing the cumulative value of services they provided

This presentation focuses on a threshold-based CAC algorithm which determines optimal threshold levels maximizing system “reward” while satisfying QoS constraints for multiple priority service classes

Key QoS Performance Metrics Cellular Wireless Network

Blocking probability of new callsDropping probability of handoff calls

• a handoff occurs when a mobile user with an ongoing connection leaves current cell and enters another cell

• an ongoing connection may be dropped during a handoff due to unavailability of wireless channels (insufficient bandwidth in new cell)

QoS Constraints • observed blocking probability should be less than the blocking

probability threshold for each service class i

You can reduce handoff-call-drop probability by rejecting new connection requests, thus increasing in new-call blocking probability.

Bih ≤ Bti

h Bi

n ≤ Bti

n

Call Admission Control (CAC) Mechanism to regulate traffic volume in (wireless or

wired) networks• intended to ensure, or maintain, a certain level of quality of

service• work by regulating total utilized bandwidth, total number of

calls, packets or data bits passing a specific point per unit time• Extensively studied for single-class network traffic, such as

voice (real-time) Threshold-based

• when a defined limit is reached or exceeded, new calls may be prohibited from entering the network until at least one current call terminates or prevent new calls from entering the network only if the resources of a particular type would be overburdened

• example: keep the dropping probability of handoff calls and/or the blocking probability of new calls lower than pre-specified thresholds

Partition-based algorithms• partition system resources and allocate distinct partitioned resources

to serve distinct service classes Priority-based

• regulation of calls according to priority descriptors Graceful degradation

• service quality of individual calls can deteriorate to a certain extent before new calls are denied entry

Call Admission Control (CAC) Algorithms

Threshold-based Algorithm• Ogbonmwan, Li and Kazakos (2005)• 3 threshold levels for a system with two service classes• used to reserve channels for voice handoff calls, new voice

calls, and data handoff calls• threshold values are periodically reevaluated based on

workload conditions

Distributed CAC algorithm• Haung and Ho (2002)• partitions channel resources in each cell into three partitions:

real-time calls partition non-real-time calls partition, and shared partition used by both classes calls to share

• applies a threshold value to new calls to satisfy more stringent QoS requirements for handoff calls

• uses an iterative algorithm to estimate call arrival rates to each cell in the heterogeneous networks

CAC Algorithms (cont.)

Bandwidth Reservation and Reconfiguration • Ye, Hou and Papavassilliou (2002)• mechanism to facilitate handoff processes for multiple

services

Common Characteristics of CAC Algorithms Call admission decisions based on meeting or not

exceeding a certain threshold levels

• Example: keep dropping probability of handoff calls and/or blocking probability of new calls lower than pre-specified thresholds

Handle QoS requirements without considering “value” issues associated with service classes, i.e., what value priority service classes will bring to a system

Threshold-based CAC Algorithm

Chen and Chen (2006)Assigns distinct, discrete thresholds to each

service typeShares all available channels among all

service classes to achieve higher utilizationLeverages thresholds to limit traffic from low-

priority calls, hence reserving more bandwidth for high-priority calls

Limitations:

• suffers from use of discrete thresholds which cuts traffic from service classes abruptly and reject any further traffic

• How to select “appropriate” threshold level

Elastic Threshold-based CAC Algorithm for Multiple Service Classes with Priorities

Extends earlier work by Chen et al. (2006)Utilizes two thresholds for each service class i:

low thresholdhigh threshold

Rejects a fraction of class i new service calls when low threshold is reached

Rejects all class i new service calls once high threshold is reached

CLAIM: Elastic Threshold-based CAC Algorithm produces optimal results!By allowing multiple service call types to

share all channels and by limiting call arrivals of low-priority service classes, elastic threshold-based CAC algorithm produces optimal results:maximizes systems reward while meeting QoS

requirements “reward” refers to any kind of “value” brought to the

system due to services example: “revenue”

generates higher rewards compared to existing CACs

Network Reward Function (assuming 2-priority service classes*)

reward earned from servicing class i new calls per unit time

reward earned fromservicing class i handoff callsper unit time

*: extensible to multiple service classes without loss of generality

Service QoS Requirements(assuming 2-priority service classes)

QoS constraints are expressed in terms of blocking probability thresholds:

Observed handoff dropping probability and new call blocking probability of class i generated by a CAC algorithm must not exceed the corresponding threshold probabilities.

Blocking probability threshold for new calls

Blocking probability threshold for handoff calls

System Model (Wireless Cellular Network)

Each cell has C channels where C can vary depending on the available bandwidth in that cell

When a call of service class i enters a handoff area from a neighboring cell, a handoff call request is generated

Threshold is reached if accepting an incoming call will cause the number of channels used to exceed the threshold value.

Each service call has its specific QoS requirement • dictated by its service type attribute (e.g.,

real-time, non real-time)• requires certain number of bandwidth

channels• imposes system-wide QoS requirements

Elastic Threshold-based CAC Algorithm for Multiple Service Classes with Priorities

System rejects a fraction of class i new calls when is reached and rejects all class i new calls when is reached

starts blocking a fraction of class i handoff calls when is reached and blocks all class i handoff calls when is passed.

new call

class i

low threshold

high threshold

handoff call class

i

high threshold

low threshold

Elastic Threshold-based CAC Algorithm for 2-priority Service Classes*

*: extensible to multiple service classes without loss of generality

Elastic Threshold-based CAC Algorithm for 2-priority Service Classes

Low threshold is triggered if a new low-priority class 2 call arrives when the number of channels used by the system is greater than by .

CAC then starts rejecting a fraction of (class 2) call arrivals until a class 2 a new call arrival causes the number of channels being used exceed the high threshold

Once the high threshold of new calls is reached, the system rejects all class 2 new calls.

Similar behavior for class 2 handoff calls

Elastic Threshold-based CACCall Admission Probabilities

Prob. of accepting a

hand-off call of

service class i

Prob. of accepting a new call of service

class i

ki : number of channels required by a service call

n : total number of channels allocated in the system

SPN Model for Elastic Threshold-based CAC

SPN Model for Elastic Threshold-based CACPlaces

Transitions

*

:

*

:

SPN Model for Elastic Threshold CAC

Transitions Enabling Predicates

:

:

SPN Model for Elastic Threshold CAC

Arrival Rates

=

if

if

=if

if

0 if is disabled.

0 if is disabled.

SPN Model Parameters

Blocking/dropping probabilities as a function of arrival rate:

Reward earned per unit time, per cell

reward earned from servicing class i handoff calls per unit time

reward earned from servicing class i new calls per unit time

V i : assigned reward per call for service class i (no distinction between new and handoff calls)

Finding Optimal Threshold Combination Challenge: find a set of threshold levels that provide “legitimate”

solutionTwo-step process

• Step I : finding a “legitimate” solution• Step II: determining a locally optimal solution by applying a

greedy search starting from the legitimate solution found in Step I

Finding a “legitimate” solution• Method I : set all thresholds at max capacity (C) and

incrementally reduce low threshold, in reverse priority order, until “legitimate” solution is found

• Method II: start with all thresholds set to minimum channel size required to support the QoS constraints and incrementally increase until “legitimate” solution is found (invoked only if 1st method fails). Next perturb threshold levels using a greedy search algorithm to optimize reward while satisfying QoS requirements

Check adjacent threshold levels (current threshold ) for values with higher reward, if any.

legitimate solution : maximizes reward per unit time while satisfying QoS constraints

Comparison of Elastic Threshold-based with other CAC Algorithms

Model and analyze wireless cellular network performance using simulationApply competing CAC algorithms to measure system QoS and reward rate performance• threshold-based• partition• spillover• elastic threshold-based

Consider two distinct priority service classes • real-time (e.g. video) and non real-time (e.g. voice)• each service type requires a number of bandwidth channels to satisfy its bandwidth QoS requirement• handoff calls have a higher priority than new calls since disconnection of an ongoing call is considered very undesirable

Simulated Wireless Cellular Network with Wrap-around Structure

• 6 adjacent cells• 1024 users• Random destination• Random speed• Random pause time

Simulation Parameters

handoff callblocking probability

new callblocking probability

for each service class i (i =1,2)

Reward Rate vs. Number of Mobile Units

Elastic threshold-based CAC algorithm produced highest reward.

QoS of Call Admission Algorithms

Elastic threshold-based CAC algorithm ensures QoS for more users.

Conclusions

Elastic threshold-based CAC algorithm is superior• satisfies QoS requirements even in heavy load

conditions• generates high rewards despite increased traffic

generated by high population

• leverages low threshold to regulate traffic (rejecting just a fraction of traffic) and the high threshold to reject traffic generated by service calls

• outperforms existing CAC algorithms for QoS satisfaction and reward optimization

• is extensible to multiple priority service classesThreshold-based and spillover CAC algorithms

perform reasonably well under moderate loadPartitioning CAC algorithms perform poorly

among all

References

1. S.E. Ogbonmwan, W. Li, D. Kazakos, Multi-threshold bandwidth reservation scheme of an integrated voice/data wireless network, in: 2005 International Conference on Wireless Networks, Communications and Mobile Computing, Maui, Hawaii, June 2005, pp. 226–231.

2. Y.-R. Haung, J.-M. Ho, Distributed call admission control for a heterogeneous PCS network, IEEE Transactions on Computers 51 (2002), 1400–1409.

3. J. Ye, J. Hou, S. Papavassilliou, A comprehensive resource management for next generation wireless networks, IEEE Transactions on Mobile Computing 1 (4) (2002) 249–263.

4. I.R. Chen, C.M. Chen, Threshold-based admission control policies for multimedia servers, The Computer Journal 39 (9) (1996) 757–766.

5. O. Yilmaz and I.R. Chen, "Elastic threshold-based admission control for QoS satisfaction with reward optimization for servicing multiple priority classes in wireless networks,“ Information Processing Letters, Vol. 109, No. 15, July 2009, pp. 868-875.