A Theory of QoS for Wireless I-Hong Hou Vivek Borkar P.R. Kumar University of Illinois, Urbana-Champaign

Background: Wireless Networks There will be increasing use of wireless networks for serving traffic with QoS constraints: – VoIP – Video Streaming – Real-time Monitoring – Networked Control 1/19

Challenges How to formulate a relevant and tractable framework for QoS? Relevant – Jointly deal with three criteria: – Deadlines – Delivery ratios – Channel unreliabilities Tractable – Provide solutions for QoS support: – Admission control policies for flows – Scheduling policies for packets 2/19

Client-Server Model A system with N wireless clients and one AP Time is slotted One packet transmission in each slot – successful with probability p n – failed with probability 1-p n Non-homogeneous link qualities – p 1,p 2,…,p N may be different AP N slot length = transmission duration p1p1 p2p2 p3p3 pNpN 3/19

Protocol for Operation AP indicates which client should transmit in each time slot Uplink – Poll (ex. CF-POLL in PCF) Data No need for ACK p n = Prob( both Poll/Data are delivered) Downlink Data ACK p n = Prob( both Data/ACK are delivered) AP n CF-POLL 4/19

Protocol for Operation AP indicates which client should transmit in each time slot Uplink – Poll (ex. CF-POLL in PCF) – Data No need for ACK p n = Prob( both Poll/Data are delivered) Downlink Data ACK p n = Prob( both Data/ACK are delivered) AP n Data 4/19

Protocol for Operation AP indicates which client should transmit in each time slot Uplink – Poll (ex. CF-POLL in PCF) – Data – No need for ACK – p n = Prob( both Poll/Data are delivered) Downlink Data ACK p n = Prob( both Data/ACK are delivered) AP n 4/19

Protocol for Operation AP indicates which client should transmit in each time slot Uplink – Poll (ex. CF-POLL in PCF) – Data – No need for ACK – p n = Prob( both Poll/Data are delivered) Downlink – Data ACK p n = Prob( both Data/ACK are delivered) AP n Data 4/19

Protocol for Operation AP indicates which client should transmit in each time slot Uplink – Poll (ex. CF-POLL in PCF) – Data – No need for ACK – p n = Prob( both Poll/Data are delivered) Downlink – Data – ACK – p n = Prob( both Data/ACK are delivered) AP n ACK 4/19

QoS Model time slot Timeline of the system 5/19

QoS Model Clients generate packets with fixed period  Deadline = Period Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least q n arrival τ 5/19

QoS Model Clients generate packets with fixed period  Deadline = Period Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least q n arrival deadline 5/19

QoS Model Clients generate packets with fixed period  Deadline = Period Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least q n arrival deadline τ 5/19

QoS Model Clients generate packets with fixed period  Deadline = Period Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least q n delivered dropped 5/19

Problem Formulation Admission control – Decide whether a set of clients is feasible Scheduling policy – Design a policy that fulfills every feasible set of clients 6/19

The proportion of time slots needed for client n is Work Load 7/19

The proportion of time slots needed for client n is Work Load expected number of time slots needed for a successful transmission 7/19

The proportion of time slots needed for client n is Work Load number of required successful transmissions in a period 7/19

The proportion of time slots needed for client n is Work Load normalize by period length 7/19

The proportion of time slots needed for client n is We call w n the “work load” Work Load 7/19

Necessary Condition for Feasibility of QoS Requirements Necessary condition from classical queuing theory: But the condition is not sufficient Packet drops by deadline misses cause more idleness than in queuing theory AP 1 2 SX SX forced idleness 8/19

Let I be the expected proportion of idle time Stronger necessary condition Sufficient? Stronger Necessary Condition Still NO! 9/19

Even Stronger Necessary Condition Let I S = Expected proportion of the idle time when the set of clients is S – I S decreases as S increases Theorem: the condition is both necessary and sufficient 10/19

Largest Debt First Scheduling Policies Give higher priority to client with higher “debt” AP SFFF SF 11/19

Two Definitions of Debt The time debt of client n – time debt = w n – actual proportion of transmission time given to client n The weighted delivery debt of client n – weighted delivery debt = (q n – actual delivery ratio)/p n Theorem: Both largest debt first policies fulfill every feasible set of clients – Feasibility Optimal Policies 12/19

Tractable Policy for Admission Control Admission control consists of determining feasibility Apparently, we need to check: 2 N tests, so computationally complex Theorem: Can be made into N tests – Polynomial time algorithm for admission control – Order clients by q n 13/19

Simulation Setup Implement on IEEE Point Coordination Function (PCF) Application: VoIP standard Deadline miss ratio (DMR) = shortfall in delivery ratio 64 kbp data rate20 ms period 160 Byte packet11 Mb/s transmission rate 610 μs time slot32 time slots in a period 14/19

Evaluated Four Policies DCF PCF with randomly assigned priorities Time debt first policy Weighted-delivery debt first policy 15/19

Test at Edge of Feasibility Two groups of clients: – Group A requires 99% delivery ratio – Group B requires 80% delivery ratio – The n th client in each group has (60+n)% channel reliability Feasible set: 6 group A clients and 5 group B clients Infeasible set: 6 group A clients and 6 group B clients 16/19

Results for a Feasible Set 17/19

Results for a Feasible Set fulfilled 17/19

Results for a Feasible Set 17/19

Results for a Feasible Set 17/19

Results for an Infeasible Set 18/19

Results for an Infeasible Set small DMR 18/19

Results for an Infeasible Set 18/19

Results for an Infeasible Set 18/19

Conclusion Formulate a framework for QoS that deal with deadlines, delivery ratios, and channel unreliabilities Characterize when QoS is feasible Provide efficient scheduling policy Provide efficient admission control policy Address implementation issues 19/19

Backup Slides An example: – Two clients, τ = 3 – p 1 =p 2 =0.5 – q 1 =0.876, q 2 =0.45 – w 1 =1.76/3, w 2 =0.3 – I {1} =I {2} =1.25/3, I {1,2} =0.25/3 w 1 +I {1} =3.01/3 > 1 However, w 1 +w 2 +I {1,2} =2.91/3 < 1