Elastic ThresholdElastic Threshold--based Admission based Admission Control for QoS SatisfactionControl for QoS Satisfactionin Wireless Networks in Wireless Networks

with Reward Optimization with Reward Optimization for Multiple Priority Classesfor Multiple Priority Classesfor Multiple Priority Classesfor Multiple Priority Classes

April 6, 2010April 6, 2010

M. M. ConlanConlanA. A. MoiniMoini

ContentContent

� Background

� Key QoS metrics for wireless cellular networks

� Call Admission Control (CAC) algorithms

� Elastic threshold-based CAC algorithm

� System model

� Performance model� Performance model

� Analysis results

� Comparison with other CAC algorithms

� Conclusions

� References

BackgroundBackground

� 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� 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 Key QoS Performance Metrics Cellular Wireless NetworkCellular Wireless Network

� Blocking probability of new calls

� Dropping 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 � 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.

Bi

h≤ Bti

hBi

n≤ Bti

n

Call Admission Control (CAC)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 overburdenedentering 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) AlgorithmsCall 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� 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 (CAC Algorithms (cont.)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 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

ThresholdThreshold--basedbased CAC AlgorithmCAC Algorithm

� Chen and Chen (2006)

� Assigns distinct, discrete thresholds to each service type

� Shares all available channels among all service classes to

achieve higher utilization

� Leverages thresholds to limit traffic from low-priority

calls, hence reserving more bandwidth for high-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

ElasticElastic ThresholdThreshold--basedbased CAC Algorithm CAC Algorithm for Multiple Service Classes with Prioritiesfor Multiple Service Classes with Priorities

� Extends earlier work by Chen et al. (2006)

� Utilizes two thresholds for each service class i:

� low threshold

� high threshold

� Rejects a fraction of class i new service calls when low � 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: CLAIM: Elastic ThresholdElastic Threshold--based CAC based CAC Algorithm produces optimal results!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 services

� example: “revenue”

� generates higher rewards compared to existing CACs

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

reward earned from servicing reward earned from 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 RequirementsService QoS Requirements(assuming 2(assuming 2--priority service classes)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)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. 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

ElasticElasticThresholdThreshold--based CAC Algorithm for based CAC Algorithm for Multiple Service Classes with PrioritiesMultiple Service Classes with Priorities

new callclass i

low

threshold

high

threshold

handoff call class i

high

threshold

low

threshold

� 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.

ElasticElasticThresholdThreshold--based CAC Algorithmbased CAC Algorithmfor 2for 2--priority Service Classes*priority Service Classes*

*: extensible to multiple service classes without loss of generality

Elastic ThresholdElastic Threshold--based CAC Algorithmbased CAC Algorithmfor 2for 2--priority Service Classespriority 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 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 ThresholdElastic Threshold--based CACbased CACCall Admission ProbabilitiesCall Admission Probabilities

Prob. of Prob. of accepting a accepting a new call of new call of

kkii : number of channels : number of channels required by a service callrequired by a service call

n : n : totaltotal number of channels number of channels allocated in the systemallocated in the system

Prob. of Prob. of accepting a accepting a

handhand--off call of off call of service class service class ii

new call of new call of service class service class ii

SPN Model for Elastic ThresholdSPN Model for Elastic Threshold--based CACbased CAC

SPN Model for Elastic ThresholdSPN Model for Elastic Threshold--based CACbased CAC

Places

Transitions

*

:

*

:

SPN Model for Elastic Threshold CACSPN Model for Elastic Threshold CAC

Transitions Enabling Predicates

:

:

SPN Model for Elastic Threshold CACSPN Model for Elastic Threshold CAC

Arrival Rates

=

if

if

0 if is disabled.

=

if

if

0 if is disabled.

SPN Model ParametersSPN 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 Finding OptimalOptimalThreshold CombinationThreshold Combination

� Challenge: find a set of threshold levels that provide “legitimate” solution

� Two-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” 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 ThresholdComparison of Elastic Threshold--based with based with other CAC Algorithmsother CAC AlgorithmsModel and analyze wireless cellular network performance using simulation

Apply 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 Simulated Wireless Cellular Network with WrapWrap--around Structurearound Structure

• 6 adjacent cells

• 1024 users

• Random destination

• Random speed

• Random pause time

Simulation ParametersSimulation Parameters

handoff call

blocking

probability

new call

blocking

probability

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

Reward Rate vs. Number of Mobile UnitsReward Rate vs. Number of Mobile Units

Elastic threshold-based CAC algorithm produced highest reward.

QoS of Call Admission AlgorithmsQoS of Call Admission Algorithms

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

ConclusionsConclusions

� 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 callsgenerated by service calls

• outperforms existing CAC algorithms for QoS satisfaction and reward optimization

• is extensible to multiple priority service classes

� Threshold-based and spillover CAC algorithms perform reasonably well under moderate load

� Partitioning CAC algorithms perform poorly among all

ReferencesReferences

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 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.