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Wireless Link: Adaptation

Wireless Link: Adaptation. EE206A (Spring 2003): Lecture #7. Reading. Mandatory Rex Min and Anantha Chandrakasan. A Framework for Energy-scalable Communication in High-density Wireless Networks . ACM ISLPED, 2002. http://www.mit.edu/~rmin/research/min-islped02.pdf

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Wireless Link: Adaptation

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  1. Wireless Link: Adaptation EE206A (Spring 2003): Lecture #7

  2. Reading • Mandatory • Rex Min and Anantha Chandrakasan. A Framework for Energy-scalable Communication in High-density Wireless Networks. ACM ISLPED, 2002.http://www.mit.edu/~rmin/research/min-islped02.pdf • Curt Schurgers, Vijay Raghunathan, and Mani Srivastava. Power Management for Energy-Aware Communication Systems. ACM Transactions on Embedded Computing Systems, 2003. (to appear) http://nesl.ee.ucla.edu/pw/NESL/papers/Accepted/J22_200X_tecs.pdf • Vijay Raghunathan, Saurabh Ganeriwal, Curt Schurgers, and Mani Srivastava. Energy Efficient Wireless Packet Scheduling and Fair Queuing. ACM Transactions on Embedded Computing Systems, 2003. (to appear) http://nesl.ee.ucla.edu/pw/NESL/papers/Accepted/J24_200X_tecs.pdf • Recommended • Modiano, E. An adaptive algorithm for optimizing the packet size used in wireless ARQ protocols. Wireless Networks, vol.5, (no.4), Baltzer, 1999. p.279-86. http://lids.mit.edu/~modiano/papers/J8.pdf • P. Lettieri, C. Schurgers, and M. B. Srivastava. Adaptive Link Layer Strategies for Energy Efficient Wireless Networking. ACM/Baltzer Wireless Networks, vol.5, (no.5), Baltzer, October 1999. p.339-55. http://nesl.ee.ucla.edu/pw/NESL/papers/1999/J15_1999_winet.pdf

  3. Adaptivity is Key to Wireless Systems • Time-varying environment • Fading, noise level, distance etc. • Time varying application requirements • rate, delay, error rate, # of users, energy etc. • Adapting system parameters can be used for • Wider operation range • Optimization of some cost function of the unspecified application-level variables • e.g. energy, # of users, total “utility” etc.

  4. Some Possibilities Parameters System Functions Application Compression rate Presentation Data resolution Transport FEC type, amount Network Frame length Data Link MAC Spreading gain Physical Transmit power

  5. Example #1: Spreading Gain Adaptation 100 PG=12 dB 80 60 DSSS Modem Chip with Adaptable Spreading Code PG=15 dB throughput x x x x x x x 40 20 PG=15 dB 0 x -15 -10 -5 0 5 Signal-to-interference ratio • Bit rate & robustness trade-off Any communication is better than none!

  6. 14 12 10-4 Increasing BER 10 10-3 8 Tx Energy per useful bit (J/bit) Goodput (normalized) 6 4 10-8 2 0 200 400 600 800 1000 1200 1400 Optimum for 1E-4 Packet Length (bytes) Packet Length (bytes) Example #2: Frame Length Adaptation • Adapting frame length saves link from dying in harsh channel conditions

  7. 10 9 Optimal Code Rate & Packet Length Selecting Optimal Code Rate 10-2 8 8x10-3 7 6 4x10-3 5 4 10-3 Energy per useful bit (J/bit) 3 10-8 2 0 200 400 600 800 1000 1200 1400 Packet Length (bytes) Example #3: Joint Code Rate & Frame Length Adaptation • Adapting code rate first, and then frame length, provides robust link & reduced battery consumption.

  8. QoSmanager QoSmeasurement compression &playout control ReactiveApplication admissioncontrol response multilevelQoS compressionratio changes inQoS level Operating System Routingtable RoutingDaemon Network change in QoS level & mobility events admissioncontrolresponse multilevelQoS compressionratio error control,frame length, & packet scheduling AdaptiveLink channelmodel estimator Link controller mode X spread code SIR Scalable CODEC Adaptable Radio Example #4: Joint Adaptation of Radio and CODEC

  9. Lower Layer Knobs • RF • Transmit power • Carrier frequency • Antenna direction • Baseband • Coding (symbol-level) • Modulation • Spreading gain • Equalization • Link/MAC • Frame length • Coding (packet-level)

  10. Conceptual Framework G : I V E N - Radio characteristics (e.g. power) and capabilities (e.g. range of symbol rates) - Time-varying Channel - Quality of Service O : P T I M I Z E - Minimize energy subject to rate, delay, and BER requirement - Maximize rate subject to energy, delay, and BER requirements - etc. S L & P L P E L E C T I N K H Y S I C A L A Y E R A R A M E T E R S Adapt - Frame Length - Spreading Gain - Hybrid FEC/ARQ Error Control Scheme Link layer retransmission (ARQ) e.g. SACK Channel coder (forward error correction - FEC) block codes e.g. Reed-Solomon convolutional codes - Symbol Rate, Carrier Frequency, Power etc.

  11. Issues • How do these knobs affect the cost function? • How to coordinate the adaptation of the various knobs? • What is the overall adaptation framework? • What is the appropriate place (or places) in the protocol stack to do the adaptation? • Some knobs are end-to-end, other are link-level • Traditionally, protocol stacks are modular, with little information exchange across layers • In wireless, performance of layers are closely coupled • How to do the needed information exchange across the layers? • e.g. how to give channel state information to, say, an application? • Interplay of RF and electronics/computing • Complex relationship between application requirements and radio parameters • e.g. application seen data rate != radio bit rate • due to bit error rate, protocol overhead, retransmissions etc. • Estimates available at receiver while the knobs are often at the transmitter • delay in the control loop • Multiple conflicting objectives (cost functions) Rather complex problem!

  12. BER 256-QAM 16-QAM 64-QAM 4-QAM SNR (dB) 256-QAM SNR 64-QAM 16-QAM 4-QAM No transmission time Case Study #1: Exploiting the Modulation Knob • Fixed transmit power • Target performance (BER) • Adapt modulation • Varying channel SNR • Maximize throughput Throughput  TraditionalAdaptiveModulation

  13. 64-QAM Required Rb 16-QAM 4-QAM time No transmission Alternative: Exploiting the Modulation Knob for Energy Ebit(J) • Slow varying SNR • Target performance (BER) • Adapt modulation • Varying load Rb • Minimize energy Ebit 4-QAM 16-QAM 64-QAM SNR = 10 dB SNR = 16 dB SNR = 22 dB SNR = 28 dB Rb(Mbit/s) Energy  ModulationScaling

  14. Ebit b = 6 b b = 4 b = 4 b = 2 b = 2 Tbit Modulation Scaling Shutdown b = 0 • The energy - delay curve is convex  Slowing down is more energy efficient than shutting down • For energy efficiency, operate as slow as possible Energy  Slowdown L·Ebit L·Tbit

  15. Energy Consumption of Transmitting a Packet Ptransmit Power consumed by the power amplifier, depends on the required performance and the wireless channel (distance, fading, etc.) Pelectronics Power consumed by the electronic circuitry for filtering, upconverting, modulation, frequency synthesis, etc. Eoverhead Energy consumption that is independent of the packet size and modulation scheme (startup cost, fixed encoded header, etc.) Tbit Time to transmit one bit (depends on modulation and symbol rate Rs) L Size of packet payload H Size of packet header

  16. Energy Consumption of Transmitting a Bit : Optimize modulation Minimize overhead Minimize header size Independent of modulation Modulation Scaling Function of the target performance, only very weakly dependent on b =1 when no variable symbol rate provision

  17. Operate at Max Symbol Rate It is preferable to operate at the maximum symbol rate that can be implemented efficiently (i.e. without severe penalty) The energy is a function of the modulation level: there is an optimum value of b, which depends on the parameters of the system

  18. Energy per Bit Region of modulation scaling Rs (Mbaud) b (bits/symbol)

  19. b = 6 Ptransmit = 36 mW Ptransmit = 9 mW b = 4 Ptransmit = 2.25 mW b = 2 Energy-Delay Trade-off

  20. Controlling the Modulation Scaling Knob • Who controls the modulation scaling knob? • One possibility: packet scheduler • Normally wireless packet schedulers decide • Which node transmits • What packet • At what time • With modulation scaling, the scheduler decides • Which node transmits • What packet • At what time • What modulation setting

  21. packet stream 1 packet stream 2 … packet stream N Example 1: Deadline-driven Packet Scheduling with DMS • A set of real-time packet streams need to be transmitted over a common communication channel • The packets in each stream have a deadline • The streams have different periods and variable packet sizes • Goal: schedule the transmission of the individual packets such that their deadlines are not violated • Non-preemptive real-time scheduling

  22. The DMS Knob for QAM

  23. Non-preemptive EDF Scheduling TA TA A A A packet stream 1 TB TB B B B packet stream 2 TC C C packet stream 3 Non-preemptive EDF (earliest deadline first) scheduling TA TB TC TA TB C A A B A B C B

  24. T1 T2 T3 fl(l) T4   L L l T5 Scheduling Set-up • Schedule a set of streams with • different periods and random offsets • uniformly random packet size

  25. Digression: Schedulability of Non-Preemptive EDF Packet Scheduler • Task T = (c, p) • c = communication cost of a packet • p = interval between successive packets • Periodic flows: p is the constant interval • Sporadic flows: p is the minimum interval • Behavior of flows • Let tk is the time of the kth packet of T • (k+1)th packet of F will occur at tk+1 = tk + p • Periodic flows: kth packet of T must begin no earlier than tk and be completed no later than the deadline of tk+p • Sporadic flows: (k+1)th packet of T will occur at tk+1 ≥ tk + p • Worst case for a sporadic task T = (c, p) is when a packet is sent every p time steps.

  26. Concrete Tasks • Difficulty of scheduling affected by the time that tasks are started (“phase”) • After the first packet, subsequent packets arrive according to p • If the phase R ≥ 0 of a task T is also specified, then T is called “concrete” • Represented as ((c,p),R) • A task set may not be schedulable, but some of the concrete task sets generated from it may be • E.g. consider task set [(3,5), (4,10)] • [((3,5),0), (4,10),0)] is schedulable • [((3,5),1), (4,10),0)] is not schedulable

  27. Scheduling and Concrete Flows • Scheduling algorithm is “universal” for concrete periodic (sporadic) tasks if it can schedule every schedulable set of concrete periodic (sporadic) tasks • Scheduling algorithm is “universal” for periodic (sporadic) flows if it can schedule any concrete periodic (sporadic) tasks generated from a set of schedulable tasks • E.g. Preemptive EDF is universal for all sets of concrete periodic tasks for which the phases are all 0. The result generalizes to all periodic tasks .

  28. Non-preemptive Scheduling: Necessary Condition • Intuitively, • Condition 1: reflects the requirement that link not be overloaded • Condition 2: reflects the restriction to non-preemptive work-conserving scheduling • RHS is the least upper bound on the the link demand than can be realized in an interval L starting from the time a packet of task Ti is scheduled, and ending sometime before the deadline of that invocation. Demand in L must be less than or equal to the length of the interval • Corollary: If a set if sporadic tasks s = {T1, T2, … Tn}, sorted in non-decreasing order by period, is schedulable then s satisfies conditions (1) and (2) above

  29. Sufficiency: Universality of Non-preemptive EDF Scheduling • Theorem: Let s be a set of sporadic tasks {(c1,p1), (c2,p2), …, (cn,pn)} sorted in non-decreasing order by period. If s satisfies conditions from the previous theorem, then the non-preemptive EDF scheduling algorithm will schedule any concrete set of sporadic tasks generated from s. • Corollary: Let p be a set of periodic tasks {(c1,p1), (c2,p2), …, (cn,pn)} sorted in non-decreasing order by period. If p satisfies conditions from the previous theorem, then the non-preemptive EDF scheduling algorithm will schedule any concrete set of sporadic tasks generated from p. • In short, non-preemptive EDF is universal for both periodic and sporadic tasks … so one only needs to checks for conditions of the first theorem

  30. Non-preemptive Scheduling of Concrete Sporadic Tasks • Theorem: Let s = {(T1,R1), (T2,R2), ..., (Tn,Rn)} be a concrete set of sporadic tasks generated from the set of sporadic tasks s = {T1, T2, ..., Tn}. Then s is schedulable if and only if s is schedulable. The problem of scheduling sporadic tasks is equivalent to the problem of scheduling concrete sporadic tasks… i.e. the conditions of the first theorem are also necessary and sufficient for the schedulability of concrete sporadic task sets

  31. Non-preemptive Scheduling of Concrete Periodic Tasks • Unlike concrete sporadic tasks, schedulability of concrete periodic tasks is a function of the assignment of release times. A periodic task set that is not schedulable may generate sets of concrete tasks that are schedulable as well as sets which are not. • NON-PREEMPTIVE SCHEDULING OF CONCRETE PERIODIC TASKS (SCPT): Let p = {(c1, p1), (c2, p2), ..., (cn, pn)} be a set of periodic tasks and let p = (p, ) be a set of concrete periodic tasks generated from p. Is it possible to schedule wp non-preemptively? • Theorem: NON-PREEMPTIVE SCHEDULING OF CONCRETE PERIODIC TASKS is NP-hard in the strong sense. • So unless P=NP, there does not exist a universal non-preemptive scheduling algorithm for concrete periodic tasks.

  32. Summary of Non-Preemptive Scheduling

  33. TA TB TC TA Admission step (off-line) C A A B A B C static C A B A B C C A B A C B dynamic C C Adjustment step (on-line) A A B B stretch C C A A B B Real-time Packet Scheduling with DMS

  34. Simulation Scenario #1 E/E0 static dynamic static· dynamic static· dynamic· stretch Utilization (=1): 84% 

  35. Simulation Scenario #2 E/E0 dynamic static· dynamic static Utilization (=1): 64% static· dynamic· stretch 

  36. Ebit Tbit Summary of Deadline-driven Packet Scheduling with DMS • Modulation level provides a control-knob to introduce energy-awareness in communication systems • Dynamic Modulation Scaling (DMS) is part of the power management subsystem. Real-time energy aware packet scheduling results in energy savings by leveraging: • Variations in worst-case traffic load: static • Variations in packet size: dynamic· stretch

  37. Example 2: Packet Fair Queuing with DMS • Recap GPS & WFQ • In GPS each infinitely divisible backlogged stream is guaranteed a rate • WFQ is a packet approximation of GPS • Compute finish times of packets under GPS • Earliest Finish Time First (EFTF) scheduling • Diff. in pkt. delays between GPS and WFQ is  (Lmax / C)

  38. Tokens i i Recap Continued • Leaky bucket mechanism • Regulate and police traffic in a network • Amount of traffic that enters the networkin (,t) is: • i average rate, i burstiness • GPS (WFQ) + Leaky-bucket • i  gi (guaranteed rate) for session to be stable • Worst case delay guarantee using GPS is given by:

  39. E2WFQ : Combining DMS & WFQ[Raghunathan @ ISLPED-2002] • Intuitively, what does fairness mean? • Each connection gets no more than what it wants (subject to a max.) • The excess, if any, is equally shared • In E2WFQ, excess is not distributed  Used to save energy instead • Energy saving opportunities • Average input rate of a stream is lower then guaranteed rate • Packet lengths may be variable (often shorter than worst case) • Low, time varying link utilization

  40. E2WFQ Algorithm • Goal of algorithm • Vary the output rate to match the current workload (demand) • Bound the performance impact due to output rate variation • Basic steps of the algorithm • Monitor the instantaneous workload • Calculate the output rate to match the instantaneous workload • Set the output link speed

  41. E2WFQ Algorithm (contd.) • Monitoring the input rate • Queue size is a good indicator of instantaneous arrival rate • Input rate suddenly increases ==> queue size increases • Parameter  is defined to be the desired time from a packet’s arrival at the end of queue to its departure from head of queue. • Compute the required output rate • For flow i, required rate for k’th pkt. to have a total delay of  is: • Required rate for a flow is simply the maximum over all flows • Output link rate can be calculated as

  42. E2WFQ Algorithm (contd.) • Change the output link speed • The new modulation level can be calculated as: • If R < C, transmissions can be slowed, resulting in energy savings • Accounting for variable packet sizes • Variation in packet sizes can be exploited to further scale down the modulation level • The new required rate for a packet is given by:

  43. Rnew Low  High  Rold What is  ? •  determines the system’s response to workload variations •  is the time constant of the first order input response to the system •  is related to the bandwidth of the low pass filter through which the input rate is filtered •  determines the maximum impact on packet delays • The maximum delay of a packet of stream i, under E2WFQ is:

  44. Choice of  • Best choice of  depends on: • Allowable latency hit • Hnput rate variability • High value of   system responds sluggishly to input variations, filtering out steep workload transients • Because of Jensen’s inequality and the convexity of energy-speed curve, this yields higher energy savings. • However, high value of  increases the packet delays

  45. Simulation Results • Significant energy savings • Energy savings increase as link utilization decreases • For a given utilization energy savings increase burstiness decreases

  46. Simulation Results (contd.) • Output rate follows input rate • Increases in steps of (Li / )

  47. Simulation Results (contd.) • Distribution of packet delays • Most packets have a delay of  • No delay bound violations (D=200 ms, =50 ms, (Lmax/Cmin)=2 ms)

  48. Summary: Exploiting the Modulation Knob • Modulation scaling: select appropriate modulation level to minimize the energy consumption • Optimum setting depends on: • Channel variations • Traffic load and deadlines • In general: wireless packet scheduling problem • Real-time scheduling with deadlines • Packet fair queuing • Soft deadlines in time varying channel: loading in time

  49. Case Study #2: Power-aware Adaptation in MIT’s mAMPS Project

  50. Energy Consumption of mAMPS-1

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