Energy-Efficient Congestion Control

1. Improve network efficiency by 1000 times! Energy-Efficient Congestion Control Opportunistically reduce link capacity to save energy Lingwen Gan1, Anwar Walid2, Steven Low1 1Caltech, 2Bell Labs

2. Network links consumea lot of electricity Electricity consumption of network links Electricity consumption of the United Kingdom > Fiber optics, copper cable Reduce electricity consumption of network links.

3. Exploit low link utilization What we do: dynamically manage link capacity.

4. router router . . . Technologies to change link capacity Sleep mode [Gupta’03] Voltage and frequency speed scaling [Pillai’01] Link bundle Link bundle 2~20 component link to sleep

5. Linear power consumption Power consumption (units) # active component links energy saving reduced capacity

6. Outline • Challenge • Goals • Algorithm • Simulations

7. Challenge: interaction with TCP capacity throughput congestion TCP reacts Reduce traffic throughput

8. Two approaches Adjust capacity slowly. Routing time scale. [He’06] [Fisher’10] Adjust capacity fast, but TCP friendly. Packet time scale. [Francini’10]… Flow time scale. This work • Fast response • Small overhead

9. Goals Dynamic Bandwidth Adjustment (DBA) Algorithm, such that 1) Operate at flow time scale. 2) Do not reduce throughput. 3) Save as much energy as possible. 4) Throughput does not oscillate---stability.

10. Recall TCP transmission rate TCP packet loss probability at steady state transmission rate packet loss probability

11. Recall Random Early Discard (RED) link link capacity incoming traffic buffer size packet drop probability buffer size

12. Recall network solves NUM Thm [Kelly’98, Low’99]: The network model solves the Network Utility Maximization problem: Ideal throughput Transmission rates Ideal capacity Throughput on the links

13. Bottleneck & non-bottleneck links Bottleneck link: packet drop probability • Do not reduce capacity buffer size Non-bottleneck link: packet drop probability • Reduce capacity • Keep 0 packet drop buffer size

14. Keep the buffer at the “right” place packet drop probability target buffer buffer size

15. DBA Algorithm (for each link) 1. Pick a target delay satisfying 2. At any time, set target buffer size and update capacity as capacity current buffer size

16. zero throughput reduction &maximum energy saving Thm: Network under DBA algorithm, modeled by Current network architecture converges to (original) target throughput (zero throughput reduction) with minimum energy consumption (maximum energy saving)

17. Model network delay transmission rate incoming traffic TCP sources Links packet loss packet drop No network delay With network delay ? Global stability delay

18. Local stability under network delay Thm: Network (with DBA) is locally asymptotically stable, in the presence of network delay modeled as provided some mild conditions hold.

19. Goals Dynamic Bandwidth Adjustment (DBA) Algorithm, such that ✔ 1) Operate at flow time scale. ✔ 2) Do not reduce throughput. Standard simplifying assumptions ✔ 3) Save as much energy as possible. ✔ 4) Throughput does not oscillate---stability. ns2 simulation to verify. ns2 is a standard and accurate simulation software.

20. Simulation setup TCP Source 1 TCP sink 1 1Mb/s 1Mb/s Node 1 Node 2 1Mb/s 1Mb/s • Compare two configurations • static: 50Mb/s • DBA: 5~50Mb/s TCP Source 20 TCP sink 20 20 additional TCP flows come and go abruptly.

21. Zero throughput reduction Fast recovery static DBA Throughput does not oscillate. Throughput (Mb/s) Initial dip throughput preservation throughput preservation throughput preservation instant increase TCP flows come TCP flows go time (s)

22. Maximum energy saving short transient same as throughput static DBA capacity ramps down slowly capacity (Mb/s) capacity ramps up fast same as throughput same as throughput TCP flows come TCP flows go time (s)

23. Concluding remarks Network link is lightly utilized, can reduce capacity to save energy. Propose DBA to adjust link capacity in TCP flow time scale. Optimality: zero throughput reduction, maximum energy saving. Stability:locally asymptotically stable. Verified by ns2 simulations. Thank you!