Elastic Threshold-based Admission Control for QoS Satisfaction in Wireless Networks with Reward Optimization for Multiple Priority Classes April 6, 2010 M. Conlan A. Moini
Jan 01, 2016
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 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.
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.