Presentation on theme: "Predictable Performance Optimization for Wireless Networks Lili Qiu University of Texas at Austin Joint work with Yi Li, Yin Zhang,"— Presentation transcript:
Predictable Performance Optimization for Wireless Networks Lili Qiu University of Texas at Austin Joint work with Yi Li, Yin Zhang, Ratul Mahajan, and Eric Rozner ACM SIGCOMM 2008 August 21, 2008
2 Motivation Wireless networks are becoming ubiquitous Managing wireless networks is hard Our goal: develop systematic techniques to optimize wireless performance Predict if given sending rates are achievable Perform what-if analysis Optimize sending rates for different objectives WirelineWireless
3 Unpredictability of wireless networks Need predictable wireless performance optimization. S S R R D D SS 50% 100% 50% bad-good good-bad
5 Existing models are insufficient Asymptotic performance bounds [GP00,LB+01,GT01,GV02] –Cannot model any specific networks Conflict graph based model [JPPQ03] –Assumes perfect scheduling and overestimates performance –Requires an exponential number of constraints DCF models [Bianchi00,KA+05,GLC06,GSK05 QZWH+07,KDG07] –Not general: restricted topologies or traffic demands –Cannot be easily incorporated into optimization procedure Need a better network model for optimization.
6 Our network model Provide a compact characterization of feasible solution space to facilitate optimization Simple: O(N) constraints for N links Flexible and accurate –Handle asymmetric link loss rate –Handle asymmetric interference –Handle hidden terminals –Handle heterogeneous, multihop traffic demands Network measurement Network model Throughput constraints Loss rate constraints Sending rate constraints
7 Throughput constraints Divide time into variable-length slot (VLS) –3 types of slots: idle slot, transmission slot, deferral slot Expected payload transmission time Probability of starting tx in a slot Success probability Expected duration of a variable-length slot
8 Loss rate constraints Inherent and collision loss are independent Inherent loss –Based on one-sender broadcast measurement Collision loss –Synchronous loss Two senders can carrier sense each other Occur when two transmissions start at the same time –Asynchronous loss At least one sender cannot carrier sense the other Occur when two transmissions overlap
9 Sending rate feasibility constraints unicast –Random backoff interval uniformly chosen [0,CW] –CW doubles after a failed transmission until CW max, and restores to CW min after a successful transmission or when max retry count is reached –CW(p i ): the expected contention window size under packet loss rate p i [Bianchi00] Sending rate feasibility constraints DIFS Data Transmission Random Backoff ACK Transmission SIFS
10 Extensions to the basic model RTS/CTS –Add RTS and CTS delay to VLS duration –Add RTS and CTS related loss to loss rate constraints Multihop traffic demands –Link load routing matrix e2e demand –Routing matrix gives the fraction of each e2e demand that traverses each link TCP traffic –Update the routing matrix: where reflects the size & frequency of TCP ACKs
12 Flow throughput feasibility testing Test if given flow throughput are achievable Challenge: strong interdependency Our approach: iterative procedure Initializeτ= 0 and p = p inherent Check feasibility constraints Converged? no yes Estimate τ from throughput and p Estimate p from throughput andτ Output: feasible/ infeasible Input: throughput
13 Fair rate allocation Initialization: add all demands to unsatSet Scale up all demands in unsatSet until some demand is saturated or scale 1 Output X Move saturated demands from unsatSet to X if (unsatSet≠ ) if (scale 1) yes no yes no
14 Total throughput maximization Formulate a non-linear optimization problem (NLP) Solve NLP using iterative linear programming Sending rate is feasible E2e throughput is bounded by demand Link load is bounded by throughput constraints Maximize total throughput
15 Evaluation methodology Model validation –How to quantify over-prediction error? Verify if prescribed rates are achievable –How to quantify under-prediction error? Scale up all prescribed rates by a common factor Performance optimization –Fairness maximization: Jain’s fairness index –Total throughput maximization This talk: testbed results only –19 mesh nodes at UTCS building; up to 7 hops –Extensive simulation results are in the paper
16 Optimization schemes Our rate optimization No rate optimization (current practice) Conflict graph based optimization –Plug conflict graph model to our framework –Conflict graph assumes perfect scheduling [JPPQ03] Represent each wireless link with a vertex Draw an edge between the vertices if the corresponding links interfere Derive clique constraints – all links in a clique in the CG cannot be active together
17 Baseline: conflict graph model CG model significantly over-estimates sending rates. UDP TCP y=0.8x y=x y=0.8x
18 Model validation: UDP traffic 1) Most estimated rates are achievable within 20%. 2) Rates scaled up by just 10% become unachievable. y=x y=0.8x
19 Model validation: TCP traffic Our model is accurate for TCP traffic. y=x y=0.8x
20 Maximizing fairness UDPTCP Fairness index is close to 1 under our scheme, while it degrades quickly in other schemes.
21 Maximizing total throughput UDP Our scheme significantly increases total throughput. TCP
22 Conclusions Main contributions –Predictable wireless performance optimization A simple yet accurate wireless network model Effective model-driven optimization algorithms –Demonstrate their effectiveness through testbed experiments and simulation Future work –Handle dynamic traffic and topologies –Use passive measurement to seed our model
24 Impact on different routing schemes Our scheme helps all routing schemes considered. TCP UDP
25 TCP Pathologies under no rate control S1 S2 R D1 D2 No Rate Limit (Mbps)Rate Limit 0.805, , TCP cannot set the rate that maximizes throughput.