Opportunistic Routing with Congestion Diversity

Multi-hop Routing in Wireless Mesh NetworksAbhijeet BhorkarMarch 19, 2012

Adviser: Prof. T. JavidiCommittee Members: Prof. B. Rao Prof. A. Snoeren Prof. M. Franceschetti Prof. B. Lin In this presentation, I am going to talk about routing in wireless mesh networks. The talk is divided into two aspects of routing first considers improving throughput when we do not access to the network topology. Second considers controlling congestion (35)1Motivation2

Wireless Mesh Networks Characteristics: Fixed nodes Dynamism : Link Quality, traffic Optimize :High throughput, low latency We envisage wireless mesh networks to develop soon also an a compliment for Cellular entworks. Rcently there has been some interest to deploy a mesh of pico cells in 5G wireless systems. These are typicallly fixed, unlimited enery. The dynamsn for ilnk quality. We need to optimize over high throughput, delay and balancing load. (35)

(40)2Routing in Wireless Mesh Networks3 Theory work: Learning optimal routes in absence of initial topology knowledge (III) Congestion awareness in opportunistic MAC (IV) Systems work: Reducing end-end delay in 802.11(conventional) MAC (II)(I)Shortest-path

(e.g. SRCR 2005)(III)Shortest-path

Opportunistic MAC(e.g. EXOR 2006)(II)Congestion aware(backpressure-based)

(e.g. BCP 2010)(IV)Congestion aware(backpressure-based)Opportunistic MAC(e.g. DIVBAR 2006)Conventional unicast MACConventional unicast MACIn liturature, many routing algorithms have been proposed to solve this problem. In lit, there were some paradigms Like shortest path routing and high traffic, backpressure routing is used to avoid congestion. Recently there had been some interest in utilizing the congestion diversity wherein the decisions are based on actual outcomes and select one relay out of them uses receiver diversity These routing algorithms typically require knowledge of link statitics to be known apriori. My initial work tackled I found difficulties in enabling the opportunistic mac. Talking in the reverse order(210)34Outline4Design a routing policy that exhibits good delay performance on convectional MAC1Congestion Diversity Protocol (CDP)Propose a metric that reacts to congestion for 802.11 MACExperimental test-bed at UCSDEvaluate CDP

Design an opportunistic routing policy that enhances throughput by minimizing the expected number oftransmissions when topology is unknown 32Review the design of congestion aware routing inopportunistic settingI would like to design a routing policy that exhibits good delay performance. Here, I will describe the CDP protocol, Where I will propose the metric which reacts to the congestion. Then, I will decrscibe the experimental testbed in Calit2. Finally, I will evaluate the CDP by comparing the performance Compatitive protocols. The MAC in this part is 802.11 without opportunism. 90 sec4

SRCR : Selects forwarder with smallest tx time (ETX)Time invariantPros: Good performance in low trafficCons:

Conventional ApproachPoor performance in high trafficDoes not account for queue congestion

sec551 111Forwarder at 101 10(s)141617(d)63412511157. Consider a small fixed network of 5 nodes, where we would like to route from source node 10 to destination node 17. As it you can see, it needs a musltihop either through node 14 or node 16. (setup from real test-bed)This I will use for explanation at various places of the talk. example Routing algorithms of wireless mesh networks can be classified time invariant and time varient. Traditional approach for has been to use shortest path routing. These strategies are time invarient and do not change. In time invariant policies, Aodv, SRCR are time-invariant policies Out of these strategies the best known strategy is SRCR. SRCR uses ETX as a metric which takes into account link transmission times rather than distanceinto account. The links are then marked with the link transmission times. The path 10-14-17 with minimum ETX is chosen since it minimizes the expected number of transmissions. Now consider a UDP traffic with low input rate Of 1Mbps at node 10. SRCR is successful in delivering almost all packets to the destination with low delayHowever, these routing policies are susceptible to congestion. So if node 14 is congested with high arrival rate of 8 mbps, the queue-length at node 14 is high as shown in figure, the SRCR still routes the packets through node 14. There is high packet loss for flow 10-17 at node 14. Furthermore, it incurs high delay. . We will use the representation of the routing path chosen as depicted in this figure, where y axis denotes the node chosen for node 10. 1805

State of Art Approach: Backpressure BP : Selects forwarder with the smallest queue backlogTime variantPros:Potential for good delay Throughput optimalCons:Poor delaySpreads the trafficLacks knowledge of distance

101417sec1 1 1

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661661 Forwarder at 10On the other hand, there is a state of art approach to tackle the congestion, which is backpressure routing. This policy is time variant and selected the forwarder depending on the current network congestion. Backpressure routing selects with smallest virtual queue In this small network backpressure is successful in routing through a least congested node. As we can see here, that node 10 selects node 16 seeing the congestion at node 14. Thus, BP has potential to reduce congestion and reduce the delay. In addition to that, BP holds a nice property that under some assumption assumptions of max-weight scheduling And network model, it is throughput optimal. By this, we mean that the routing strategy is stable for all arrival rates which could have been made stable under any other policy. However, there are many disadvantages of this policy, the most improtant one is that under bigger network even under low traffic it can spread the traffic in trying to avoid the congestion. This figure shows the forwarders chosen by Node 10 and we can see that it oscillates and speads traffic. It spreads the traffic, ther-by it increases the contention and delay.. Further, changes of loops in the routes are high. This is shown in the delay and loss plot that even under low traffic we observe high delay and loss. An important cause of this increase delay is that it does not take distance into account (180) 67Our Approach7Propose a metric that indicates the overall congestion on the shortest path to the destinationImplement various congestion-aware routing algorithmsEvaluate on the test-bed

Shortest path BackpressureDesign Congestion Diversity Protocol (CDP) that Combines properties of shortest path and backpressureIn this work, we combine the good properties of backpressure routing and shortest path. The research objective of my work is to would like to design a routing policy that exhibits good delay performance and reacts to congestion. So that, at low traffic it performsAs good as SRCR and high traffic similar to back-pressure without spreading. CDP protocol is based on the idea of congestion aware routing. Routing decisions are based on congestion in the networkIt exploits path diversity when available. Our contribution of this work are that we propose a metric that reacts to congestion and identifies a congested path. The implementation goal is the decentralized computation of the proposed measureand route the packets efficiently to avoid congestion. Finally we evaluate the performance of CDP in test-bed. (1min)There has been some effort in theoretical literature to combine the two. However, as we will see later, the approach called Enhanced backpressure still does performs poorly. (1 min)

7Nodes are associated with overall congestion measure Vt (n) is the expected delay to reach the destination

The congestion measure enables correct selection of next hop16 is selected as (congestion aware) , thus 5 never selected (avoids spreading)

Congestion Diversity Protocol : Birds eye view81 11 261

V=2V=6V=2+210141617

56 1 11

(60)Let us now consider our canonical network. Here the nodes are now associated with link qualities and queue lengths. To avoid congestion the nodes should be associated with the congestion measure. This congestion measure has three properties. It is time varient, f congestion(16) < congestion(14), node 10 selects 16 as forwarderNever picks backword node 5. In particular, the node n is labeled with congestion measure Vi(t). Our thesis is that Vi(t) should approximate the expected delay to reach the destination. , The the nodes are ordered according to a congestion measure Vi(t) and the node with lowest rank is then selected as forwarder.

Effectively, V(n) can be decomposed into two parts. First one is the local congestion and the other is expected delay down the stream. There can be many approaches to approximate local congestion. One of them if two devide the Queue length by link probability, which is effectively, the number of transmissions to empty the queue. Now for each node, we have the local congestion in the figure. For 14, its 8. For 16 its 2. For 10 the next hop chsen will be node 16, and the effective congestion is 2. while the expected delay down the stream will be 2 making V(10) to be 4(120 min)8Compute V and broadcast as control packetPassive probing: monitor data packetsActive probing: use probe packetsCDP Design9Mac LayerHeuristicsAvoid loops (split-horizon)Eliminate poor quality links

Control packets from neighborsHigher priorityqueue Local delayUser spaceKernel spaceControl queueData queueQueue LengthTx timeLet us now see the design of CDP in the implementation. The goal is to compute V(n) for each node n.These are computed using control packets. Its behavior is dictated by control packetsEach node broadcasts a packet with the information for the effective delay in the congestion. Generated in user space. control packets are given higher priority, since they determine the behavior of the CDP. As well, they are transmitted through a heigher priority queue compared to data packets to avoid the loss of control packets. . They are transmitted at the lower phy rate to be reliable. The control packets from other neighbors are then used to compute the congestion measure. In order to compute the local delay we need an estimation of link qualities. . (2 min) We used active and passive, talk about them. Next, I will determine the issues in the implementation of cdp for which we have modified CDP . The first is formation of loops for which we have used split horizon rule. Furthermore, we neglect link with low link quality. (3)

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Experimental Setup 10~100 mt.Indoor Network of 12 Alix nodes in Atkinson Hall (Calit2)MAC: 802.11g using the MadWifi-NG driverMultiple hops (5 hops maximum)4 neighbors averageFixed rate 48 Mbps13 dBm transmit power

Performance MeasuresMean end to end delayMean throughput ratio

Comparative Protocols: SRCR Back Pressure (BP) Enhanced Back Pressure (E-BP)ETX + Queue differential

In order to test our protocol and how it performs in practice, we have deployed a testbed. Indoor Network of 12 Alix nodes in Atkinson Hall (Calit2) 500Mhz CPU and 1GB flash.MAC: Atheros IEEE 802.11 chipset (5212) using the MadWifi-NG driver.Multiple hops (5 hops maximum)13 dBm transmit power4 neighbors averageThe comparitive protocols used are Comparative protocols:SRCRBack Pressure (BP)Enhanced Back Pressure (E-BP). Enhanced backpressure even though tries to combineETX and backpressure, which makes simple additive combination of ETX with queue differential andfails to provide good delay. The performance metrics used are Mean end to end delayMean throughput ratio. (1.3 min)

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Results Setup11Choose random configurations Two flows with random source, destination and traffic loadExclude the configurations with high loss and only single hopsPlot differential delay and normalized throughput w.r.t CDPUDP (low load, high load)TCPTo analyse the performance in general setting, we have chosen to work with 2 random flows. Where we choose Random source destination pairs for each flow We plot CDF of mean delay of CDP - mean delay of candidate protocol Ratio of delivery ratiosIt shows that about

(3.5 minutes)

11

Results (Low Load UDP)12

Fraction of configurationssecFraction of configurationsTo analyse the performance in general setting, we have chosen to work with 2 random flows. Where we choose Random source destination pairs for each flow We plot CDF of mean delay of CDP - mean delay of candidate protocol Ratio of delivery ratiosIt shows that about

(3.5 minutes)

12Results (TCP) 13No performance gainTCP is congestion aware (reduces to low load)Reordering of packets reduces congestion window

Fraction of configurationssecFraction of configurationsTo analyse the performance in general setting, we have chosen to work with 2 random flows. Where we choose Random source destination pairs for each flow We plot CDF of mean delay of CDP - mean delay of candidate protocol Ratio of delivery ratiosIt shows that about

(3.5 minutes)

13

Results (High Load UDP)14Throughput ratio

Fraction of configurationssecFraction of configurations70%

To analyse the performance in general setting, we have chosen to work with 2 random flows. Where we choose Random source destination pairs for each flow We plot CDF of mean delay of CDP - mean delay of candidate protocol Ratio of delivery ratiosIt shows that about

(3.5 minutes)

14

Point (A)1510141617

E-BPBPCDPSRCR

Next hop for node 10

(Low load)(High load)We now delve into this results by considering a canonical example. We can observe that this canonical example is embedded in our topology.. In this example theFlow from node 10-17 is congested from the flow 14-16. This plot plots the next hop for nodes 10. Recall that SRCR and BP show poor performanceSRCR being unaware of the congestion at node 10 routes the packets through node 16.BP, spreads the packets and result in high delay. E-BP tries to improve the delay but fails to provide good performance. While CDP detects the congestion at node 16 and routes the packets through node 16. CDP shows about 50x improvement w.r.t. delay and 100x w.r.t. BP. Delivary ratio follows similar trend. Here backpressure suffers due to high packet loss due to increased Interference and packets drops. (90 min)15

Point (B)16 10141617

E-BPBPCDPSRCRNext hop for node 10

(High load)(Low load)

However, we if increase the load on node 10 while reducing the load at 14 we observe thatThe performance of CDP becomes pooper. Next, we will investigate this result in more detail

(30 sec)16Conjectures17

Test MethodologyIntra-flow and inter-path interference can negate gain of multi-path (congestion) diversityModular approach is not sufficient in exploiting congestion diversityIn real networks with high existing interference, modular approach is sufficientAnalyze performance of multi-path in two toy topologiesRe-conduct the experiments in presence of interference Here, we will propose some conjectures. 17Interference and Congestion Diversity(1)18101661714Path-2Path-1Intra-flow and inter-path interference can negate gain of multi-path (congestion) diversityModular approach is not sufficient in exploiting congestion diversity

Consider routing where Path-1 chosen with probability

Interference =1 performs best

Let us consider a same set of nodes. We can route the packets using two routing paths-path1 and path2.We consider a set of routing protocols which choose path 1 with probability alpha. This figure shows the performance of the set of routing protocols as alpha is varied. It shows that routing along path 1 is the best. This is because load balancing when paths are interferneceing can lead to poorer performance. Since, CDP cannot track the interference precisely, CDP has performance degradation. This example shows that when the interference dominates the congestion exploting congestion diversity is not beneficial.Furthermore, we have a modular approach, were we did not touch the MAC later. This modular approach has been harmful in tackling the self-interfering effectively. (2 min)18Interference and Congestion Diversity(2)19

Consider routing where Path-1 chosen with probability

1311516631417Path-2Path-1 =0.5 performs best

However, there are some cases where load balancing can help. Let us consider another example in our network which sufficiently longer paths. In this case congestion diversity gains has dominated interference. These examples also suggest that It is difficult to capture the interaction between MAC and routing.These examples also suggest if the interference affect is minimized then we can observe the congestion diversity gain.The idea we have is to create a uniform interfernece in the network. So that whenever, we switch paths between routes the effective interference change is not significantly changed. In the next slide, we will try to separate the effect of self-interference and congestion diversity. (1.3 mins)19

Performance in High Interference20

In real networks with high existing interference, modular approach is sufficientFraction of configurationssecFraction of configurationsIn real networks typically, each node engages in transmission. Consider a setup where is node is transmitting at a fixed UDP rate to one of its neighbors. This setup tries to maintain the network on a uniform interference floor. In presence of such interference, we we try to perform load balancing the changes of self-interfenrece are minimal And the gains of congestion diversity are observed. With this experience, we next repeat the experments in presence of external interfnerece. We observe that CDP gain is significant over other (1 min)2021Outline Design an opportunistic routing policy that enhances throughput by minimizing the expected number oftransmissions when topology is unknown 21Design routing policy that exhibits good delay performance and study in a test-bed.12Review the design of congestion aware routing in opportunistic settingDistributed Opportunistic Routing with Congestion Diversity (D-ORCD)OptimalityOpportunistic Routing321Routing decisions are made based on actual transmission outcomes via a three-way handshake.Decisions based on rank ordering of nodesS12D30.80.80.80.80.2Routing Cycle1Transmission (T)2Acknowledgment (A)3Relaying (R)Slot n(T)(R)(A)22Opportunistic RoutingIn opportunistic routing, the routing decisions are made based on actual transmissions outcomes via a three-wayHandshake. First, in transmission stage the packet is transmitted. In acknowledgement stage, the packet is acknowledged. Finally in the relay stage the transmitter decides on the relay node. In order to determine the relay or forwarder the nodes rank order the relay neighorsUsing some metric. This rank ordering we call as policy . 1.2 minutes22Rank Ordering23Potential relays are rank ordered according function considered cost to destination Transmitter n chooses relay k S among the recipient nodes S iff

Different notions for calculation of ExOR : Expected number of tx along shortest path (Bi2005)SR : Expected net cost (LT2006)

ORCD : Expected draining time (MZJ2010)

ExOR, SR have time-invariant rank orderingORCD has time-variant rank orderingORCD needs centralized computationsLet us discuss this rank ordering. Optimal policy rank orders according to a index function V(i). Lamda(i) denotes the expected reward from node i.

Different notions of Vi(t). EXOR and LOTT are congestion-unaware. EXOR uses extected number of tx to compute. Lott uses generic cost and reward to determine Vi and effectively captures the opportunistic gain.ORCD extended lottss setup in a centrazlied setup to congestion aware routing.

(1.2 min)2324CDP in Opportunistic SettingDeveloped D-ORCD (distributed opportunistic routing with congestion diversity)Nodes are ordered according to a congestion measure Vt (n).Vt (n) is the expected draining time

Routing policy: select the forwarder with the lowest congestion measure based on actual outcomes.D-ORCD is throughput optimal*Simulations show D-ORCDs superior performance for UDP traffic*Proof based on M. Naghshwar, H. Zhuang, T. Javidi, A General Class of Throughput Optimal Routing Policies in Multi-hop Wireless Network, Transactions on Info. Theory, 2012We extended the design of ORCD to a distributed setting. We designed an algorithm D-ORCD which computes the congestion measures in a distributed manner taking the system Implications into account.

2425Outline Design an opportunistic routing policy that enhances throughput by minimizing the expected number oftransmissions when topology is unknown 253Distributed Adaptive Opportunistic Routing (AdaptOR)No Regret Routing (NRR)DesignOptimalityDesign routing policy that exhibits good delay performance and study in a test-bed.12Review the design of congestion aware routing in opportunistic settingAs seen in the previous design the algorithms require knowledge of the link qualities.Typically these quantities are unknown. We have used probe packets to determine link quantities. In the next work, we ask some questions. Can we only use data packets to come up with a routing strategy without probing overhead. We also ask the question if probing is used, what is the best strategy to use probe packets. This leads us to design of two algorithm AdaptOR and NRR. This part will help us to introduce the notion of opportunisitc routing.

(40) 25Problem Setup26Multi-hop ad-hoc network of nodesTransmission model:Node i broadcasts a packetci: fixed cost of transmissionSet of nodes S successfully receive with probability P(S|i) Routing model:Only one node k S is selected as forwarder (others drop the packet)Reward r obtained when packet reaches destinationNet cost= total transmission cost - r 1(packet delivered)

1D2i2SsWe now describe the network model. We consider a ad-hoc network of nodes connected by unreliable links. The transmission model for the network is as follows. When node I transmits a packets we assume that cost ci is incurred. Let S denote the set of nodes which successfully receive the packets. These recepints then let the transmitter know using some ack mechanism. We assume error free acks. Furthermore, we assume slotted time, i.e. transmission and relay selection occur in one time slot. We assume only one forwarder. The problem is how to route the packets optimally based on this modelHere, we will be setting up the problem with respect to net reward which is negative of the net cost26Given network and transmission model provides a rank ordering

Any given packet minimizes expected cost (LT2006)Our workAssumes no initial knowledge of transmission modelKnown Results27Genie algorithm(*)

Optimal rank orderingI will now describe known facts about routing. We assume no initial knowledge about the topology. We use machine learning methods.

(1.5 minutes)27 AdaptORMinimizes expected per packet costSacrifices data packets for exploration Estimates expected cost without computing P(S|i)Randomizes over routing paths for exploringNRRMinimizes regret (total cost optimal total cost) over all packets Avoids loss of data packets using probe packets Uses infrequent probe packets to estimate P(S|i)Greedy w.r.t. perturbed estimate of

Contributions28

AdaptOR.. NRR estimates V() using its current information And it uses a perturbed value of estimate V() in a greedy manner. NRR is mainly different in two aspects.

Both these algorithms uses ideas from machine learning.One uses Q-learning other uses ideas from active learning for bandit problems.

I will give an overview of the algorithms for adatOR and NRR in the next slides. (1.5 minutes)28AdaptOR Operation29Estimate expected costRelay Feedback estimated costRank order nodesTransmissionRelay SelectionWith high prob : Select relay with best estimateWith diminishing prob : Select relay randomly or drop Parts of network remain unexplored due to incorrect estimates Consider the working of a distributed bell-ford like algorithm 29NRR Operation30Genie Algorithm

EstimateLink quality ProbingPerturb V Rank order nodesTransmissionRelay SelectionNRR works similar to a centralized distras algorithm. 30Main ResultsWhen,Channel statistics are ergodicReception outcome perfectly known

AdaptOR minimizes average cost Theorem: 31

Expected total cost till N following for algorithm cost for packet m NRR minimizes regret Theorem:

(40,1050)Let us now describe the theoretical guarantees for these algorithm .We need some notations. AdaptOR optimizes the average reward

(13 mins)

3132Summary32We designed CDP that exhibits good delay performance on convectional MAC1We designed AdaptOR and NRR that determines optimal routing when topology is unknown 32We designed D-ORCD to avoid congestion in opportunistic settingI would like to design a routing policy that exhibits good delay performance. Here, I will describe the CDP protocol, Where I will propose the metric which reacts to the congestion. Then, I will decrscibe the experimental testbed in Calit2. Finally, I will evaluate the CDP by comparing the performance Compatitive protocols. The MAC in this part is 802.11 without opportunism. 90 sec32List of publications during PhD. A. Bhorkar, T.Javidi, A. Snoeren, Achieving congestion diversity in wireless Ad-hoc networks, to be submitted to Transactions on Networking.A. Bhorkar, T. Javidi, A. Snoeren, Achieving congestion diversity in wireless Ad-hoc networks, Infocom 11A. Bhorkar, T. Javidi, A. Snoeren, On practical implementation for congestion diversity in wireless Ad-hoc networks, Allerton 10A. Bhorkar, T. Javidi, A. Snoeren, Empirical Measurement of Draining Time, Expected Transmission (ETX), and Packet Erasure Probability in WiFi-based Mesh Networks, WinMEE 11A. Bhorkar, M. Naghshvar, T.Javidi, Opportunistic routing for congestion diversity in wireless Ad-hoc networks, submitted to Transactions on Networking.A. Bhorkar, A. Nilson, P. Johansoon, Common Opportunistic Routing and Forwarding, VTC10A. Bhorkar, T. Javidi, No regret routing in wireless Ad-hoc networks, Asilomar 10A. Bhorkar, M. Naghshvar, T. Javidi and B. Rao, An Adaptive Opportunistic Routing Scheme for Wireless Ad-hoc Networks,, IEEE Transaction on Networking, 2012.A. Bhorkar, M. Naghshvar, T. Javidi and B. Rao, AdaptOR An Adaptive Opportunistic Routing Scheme for Wireless Ad-hoc Networks, ISIT 09A. Bhorkar, M. Naghshvar, T. Javidi and B. Rao Exploration vs Exploitation in wireless Ad-hoc networks, CDC 09E. Coviello, A. Bhorkar, F. Rossetto, B. D. Rao, M. Zorzi, A Robust Approach to Carrier Sense for MIMO Ad Hoc Networks, ICC 2009A. Bhorkar, B. S. Manoj, B. D. Rao, R. R. Rao, Antenna Selection Diversity Based MAC Protocol for MIMO Ad Hoc Wireless Networks, GLOBECOM 200833AdaptORNRRCDPD-ORCD33AcknowledgementsPhD. advisor: Prof. Tara Javidi

Doctoral Committee:Prof. Bhaskar RaoProf. Alex SnoerenProf. Massimo FanceschettiProf. Bill Lin

Colleagues and Collaborators: Mohammad Naghshvar, Anders Plymoth, Per JohansonB.S. Manoj, Emanuele Coviello,

Friends from San Diego, most of whom are no longer in San Diego.

My parents and my wife34Questions?35

Bill Watterson48 minutes35Develop multi-rate CDP utilizing multiple PHY ratesDefine congestion measure for multi-rate CDP

Implement opportunistic versions on test-bed Modify MadWifi drivers 802.11 Mac layer

Future Work36DSS12D31650We are currently investing the impact of TCP and mixed TCP/UDP traffic on CDP. We have also started looking at multi-rate version, wherein, we utilize the multiple rates avaivailable at the MACLayer. We have some initial implementations for prio36NRR Details 37 is the estimate of reward at time n number of times node a transmitted a packet. number of times set S received transmission from node aAt time n=2j ,j 0,Transmit probe packetsEstimate link probability using Estimate assuming is true modelAt each time n

Rank order potential relays according function Transmitter i chooses relay a S among the recipient nodes S

Estimate assuming model is trueVariance in the estimate

In summary, we describe the algorithm. Later we describe the intuition behind it 2 min37Chart10.010.80.10.81

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SRCR(L)SRCR(H)BP(H)BP

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SRCR(L)SRCR(H)BP(H)BP

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