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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

On the Flow Classification Thresholds of FD-MAC Protocol Hai Wang* , Chao Dong*† , Lianjing Cui‡ , Weibo Yu* , and Renhui Xu* *

Nanjing Institute of Communications Engineering, China of Computer Science and Technology,Nanjing University, China ‡ Troop 65426, China Email: [email protected], [email protected]

† Department

Abstract—Flow Driven MAC Protocol (FD-MAC) is a slot based MAC protocol which is designed for long distance wireless multi hop transmission. With the flow-driven resource reservation mechanism, FD-MAC protocol is especially suitable to be used for wireless ad hoc network with dynamic traffic pattern. We suggest that the Flow Classification Thresholds (FCTs) of the protocol are the key parameters to the performance of the protocol, that are not yet been fully investigated. Then the values of the parameters are analyzed theoretically, and the optimal values are given in turn. Simulation result validates our analysis; best performance of the protocol would be achieved when optimal FCT values are chosen.

I. I NTRODUCTION With the wide proliferation of wireless transmission, long distance wireless networking will be a key challenge for wireless mesh network to win the battle with traditional 3G communications. Long distance MAC protocol, which is an important issue in long distance networking, has attracted a lot of attention in recent years. Various solutions such as Wireless Long Distance transmission (WILD) [1] have been proposed. However, most of the protocols are derived from random access protocols such as IEEE 802.11 DCF, etc. These protocols could not meet the requirement of multimedia services in nature, especially for delay bound, minimum throughput requirement under moderate and high traffic load. FD-MAC(Flow Driven MAC protocol [2]) is a flow-driven, slot-based MAC protocol with long distance supportability. It is a hybrid of contention-free and contention-based MAC protocol. In this scenario, the traffic is classified into 2 categories: Elephant Flow (EF) traffic which has large amount of data to transmit and sustains a long time, and Mice Flow (MF) flow traffic which sustains only a short period of time. This protocol allocates resources (time-slot in this article)onthe-fly to EFs, while MFs are assigned to contend for the remaining slots. It is suitable for networks with asymmetrical traffic pattern, such as emergency and battlefield networks. With pure slot-based structure (no mini-slot is introduced), it is also suitable for long distance data transmission, where mini-slot may not be applicable. The rest of this paper is organized as follows. Some related work is reviewed in section II. Details of FD-MAC protocol are described in section III. The theoretical analysis on the Flow Classification Threshold (FCT) is presented in section IV,

and the upper and lower bound of the thresholds are given. The simulation results are shown in section V. Section VI concludes this paper. II. R ELATED W ORK Reservation-based medium access control has been widely studied for MANETs and a variety of protocols have been proposed in the literature [3] [4] [5]. In all these solutions, slot-based MAC protocols have been drawn a great attention for its capability to offer delay bound and minimum bandwidth service to the application. With the widely deployment of GPS equipment, the problem of network synchronization of slot-based protocol is greatly relieved, making this type of protocol become more practical than ever. However, most slot-based reservation MAC protocols are using mini-slot structure in their implementations. For example, in [6] [7], Li proposed two evolutionary-dynamic MAC protocols based on TDMA and TSMA, respectively. In both protocols, a node with packets to transmit is allowed to contend for idle slots with mini-slot structure. Tang [8] proposed a channel access protocol, named collision-avoidance time allocation (CATA), which allows nodes to contend for and reserve the time slots by means of distributed reservation and handshake mechanism. In CATA, four mini-slots are used before data transmission. In [9], Coupechoux proposed a slotted CROMA protocol, which uses REQ/RTR packets sent in a mini-slot to reserve the channel before the data transmission. Mini-slot structure works fine in short range wireless transmission environment, however, it cannot work in a WILD, dynamic environment. For a radio with a transmission range of several kilometers, the propagation delay of the signal could not be ignored; the tail of the time slot should be left blank to keep the data from running into another time slot at the receiver side. The length of the blank tail is decided by the maximum possible transmission range among the nodes. In this environment, the more mini-slots are used, the less efficiency we will get, for a blank tail should be set for each mini-slot. FD-MAC, which is designed for WILD environment, does not introduce the mini-slot structure in its implementation. For a reservation based protocol, the trigger of the resource reservation process is an important issue. In literature, various

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

mechanisms have been proposed to trigger resource reservation process. In [10], Ragnin proposed a Queue-driven Cutthrough Media Access Control (QCMA) protocol, in which resource reservation is driven by the queue length in a node that has exceeded a given threshold. In [1], Nedevschi proposed a JAZZYMAC for a WILD mesh network. In JAZZYMAC, dynamic length of the timeslot is used to adapt to the link with different queue length. However, all the above protocols do not introduce any ‘flow’ concept in resource reservation in MANETs. In [11], Acharya suggested that in a multi-hop environment data transmission is performed hop-by-hop so that the ACK message of the previous hop can be combined with the RTS message of the next hop. This can significantly reduce the control message overhead. The idea of ‘data flow’ has been introduced in DCMA. However, it was used to simplify the handshaking process and to relieve the burden of the upper layer. It didn’t utilize flow to trigger the channel resource reservation process. In [12], Papagiannaki introduced the concept of ‘flow’ to improve the performance of the Internet. In [13], Mori proposed an approach to identify EFs with periodically sampled packets and indicated that traffic is better to be regarded as composition of different flows instead of composition of different packets. However, the benefit of ‘flow’, especially its capability in channel resource reservation in MANET, has not yet been investigated. In this paper, we introduce FD-MAC protocol, which is a distributed flow-driven reservation based MAC protocol for WILD MANETs. Then we discuss the key parameters of the protocol-Flow Classification Threshold(FCT) in detail. Simulation result validates our analysis. III. D ESCRIPTION OF FD-MAC A. Overview FD-MAC is a slotted MAC protocol, which combines random access and reservation-based access, and introduces a flow-driven mechanism for resource reservation. In slotted MAC, channel bandwidth is divided into a number of timeslots that can be used by the network nodes through contention. In FD-MAC, packets of MFs are forwarded using a random access protocol (i.e., slotted ALOHA), all unreserved slots are available for contention. For packets of EFs, a flow-driven reservation based access scheme is used to reserve a slot or more to be used exclusively for the transmission of EF packets. By introducing the flow-driven mechanism, the FD-MAC is expected to simplify network control as well as reduce data collision, which in turn will improve the channel utilization rate. B. Flow-Driven Reservation Process In FD-MAC, each node maintains two tables and updates them dynamically. One of them is called General Flow (GF) table, and is used to record the flows whose packets are being processed. The other is called the Long Flow (LF) table, and is used to record the flows for which the node has reserved or is reserving a slot. When a packet is received, the FD-MAC protocol will call an OnPacketReceive procedure (see Fig.1)

Procedure:OnPacketReceive(packet) 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20:

index ←ExtractIndex(packet); entry ←Search(GFTable,index); if entry = NULL then entry.count++; Forward(packet); else entry ←Search(LFTable,index); if entry = NULL then entry.count++; if entry.slotReserved = NULL then ForwardOnSlot(entry.slotReserved, packet); else Forward(packet); end if else entry ← CreateEntry(GFTable,index); entry.count ←1; entry.slotReserved ←NULL; end if end if Fig. 1.

OnPacketReceive Procedure

to process the packet. The flow identity is extracted from the packet (line 1), and in most cases, source address, destination address, source port, destination port and protocol fields are sufficient to identify a data flow. Other flow identification mechanisms are also applicable. If an arriving packet does not belong to any flow recorded in the GF and LF tables, it is considered coming from a new flow and a new entry is created in the GF table (see lines 16-18). Otherwise it will increase the variable count of the entry (see line 4 and line 9), which records the number of arriving packets belonging to a given flow in a counting period T . At each node, a timer with value of T is introduced to periodically call an OnCheckTimer procedure (Fig. 2) to discriminate MFs from EFs based on a couple of given upper and lower FCT thresholds. Assuming that Kh is a given upper threshold for FCT, and that Kl is a given lower threshold, when the OnCheckTimer procedure is called, the mean packet arrival rate λ of all flows in the GF table are calculated every count seconds. If λ exceeds Kh , then a new EF will be identified and a slot(s) reservation procedure will be called to reserve a slot(s) for this EF (refer to lines 4-6). For an existing EF, if the λ of an entry in the LF table is less than Kl , then the corresponding entry will be moved from the LF table to the GF table (see lines 15-16). Consider a couple of downstream and upstream nodes located in the direction of data transmission. Assume that the slot state information of all nodes in the network is well advertised. FD-MAC chooses the downstream node to initiate the reservation process, which is described as follows. Once a downstream node identifies a new EF, it will send

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

Procedure:OnCheckTimer(timer) 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20:

for ∀entry ∈ GFTable do λ ← CALCULATE(entry.count); if λ ≥ Kh then Extract(entry,GFTable); Insert(entry,LFTable); SndReserveSlotReq(entry); end if if λ ≈ 0 then Extract(entry,GFTable); Release(entry); end if end for for ∀entry ∈ LFTable do if λ < Kl then Extract(entry,LFTable); Insert(entry,GFTable); end if end for entry.count ←0; RechargetheTimer(timer,T ); Fig. 2.

OnCheckTimer Procedure

a resource reservation message to the upstream node and start a timer. This message contains the information of the flow for which the reservation is made and the slot(s) to be reserved according to its own slot state and that of its upstream partner’s. When the upstream node receives this message, it will mark the flow as an EF if it has not already done so, and will reserve the intended slot(s). Then it will mark all the packets belonging to this flow with an ‘EF’ stamp and sends them out in the reserved slot(s). When the downstream node receives the packets of this flow with the ‘EF’ stamp, it knows that the reservation process is successful. If the downstream node does not receive such packets before the timer expires, it will resend the reservation message and repeat the above process for several times (in our implementation, retry time is designated as 3) before it move this flow from the LF table back to the GF table. The simulation results in [2] shows that FD-MAC outperforms IEEE 802.11 DCF, MACA, and S-ALOHA significantly in terms of throughput(nearly 17% more than that of IEEE 802.11) and latency under medium and high traffic load in MANET. IV. P ROTOCOL PARAMETERS A NALYSIS From section III we may notice that several parameters (Kh ,Kl and T ) need to be specified before the operation of the protocol. In this section, we will discuss how to choose reasonable value for these parameters. Parameter T refers to flow check period. The protocol will periodically calculate packet arrival rate of each data flow on the frequency of T1 . The check period T should be long enough to eliminate the effect of short-term flow fluctuation. In our

scenario, short term traffic such as web surfing and email is not expected to be regarded as EF, the traffic that introduced by them may end before resource reservation process has been accomplished. On the contrary, streaming multimedia traffic such as live video is a reasonable candidate for EF. Web surfing, email may last only for seconds, while streaming traffic may last for hours. Therefore, setting the value T in minutes (such as one minute) may be a good choice to eliminate short term traffic. The value of Kh and Kl , however, unlike the value of T , can not be chosen intuitively. Kh defines the upper bound for a MF, and Kl defines its lower bound. The values of these two parameters decisively affects protocol performance. For example, if the value of Kh is small enough, most data flow will run over the threshold, and numerous dedicated time-slots will be reserved for these flows. The slots of the channel will soon be exhausted, and a new EF with a large amount of data traffic will not find a slot available to serve it, while flows with less traffic will occupy the resource. On the other hand, if the value of Kh is considerably large, few data flows will reach the threshold and few slots will be dedicated to the data flow. As a result, the performance of the protocol would be nearly the same as that of the slotted-ALOHA protocol. Kl yields similar a effect, but in a contrasting way. Therefore, the optimum values of Kh and Kl are crucial to the performance of the protocol. In fact, we have the following results: Lemma 1:If a frame has N slots, it is supposed that each slot lasts for 1 time unit (one data frame lasts for N time unit). Data slots are allocated on the frame basis (slots allocation are repeated frame by frame), thus, the lower bound Kl = N1 . Proof: With the purpose of the protocol in mind, we propose to allocate dedicated time slots for flows with large traffic, while other flows with smaller amounts of traffic will contend for the remaining time slots. We anticipate that throughput gain is introduced by the elimination of EF from contending sets of flows. The collision among MFs will decrease even with fewer time slots. If such conditions are not met, then the throughput gain of the protocol will not be guaranteed. Suppose that the packet arrival rate of EF is λ1 , and there are a EFs that are already identified in the network. The packet arrival rate of MF is λ2 , and there are b MFs in the network. P is defined as the probability for one data packet to send out successively without collision in a pure contending slotted protocol, and P  is defined as the probability for one data packet to send out successively without collision in FD-MAC. Thus, we have 1 Cb1 (1 − e−λ2 )e−λ2 (b−1) e−λ1 a , P = CN

(1)

1 1 −λ2 −λ2 (b−1) )e , P  = CN −a Cb (1 − e

(2)

while

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

If this data flow is regarded as MF, then the throughput Sm

Therefore, we have is:



P −P 1 = CN Cb1 (1 − e−λ2 )e−λ2 (b−1) e−λ1 a − 1 1 −λ2 −λ2 (b−1) )e CN −a Cb (1 − e

= b(1 − e−λ2 )e−λ2 (b−1) [N e−aλ1 − (N − a)] = b(1 − e−λ2 )e−λ2 (b−1) [a + N (e−aλ1 − 1)].

We define

f (a) = a + N (e−aλ1 − 1).

Sm = (a − 1)(1 − e−λ1 ) + (N − a + 1)(b + 1)(1 − e−λ2 )e−λ2 b . (3)

So the throughput gain G is: G = Se − Sm = (1 − e−λ2 ) + (1 − e−λ2 )[ (N − a)b(1 − e−λ2 )e−λ2 (b−1) − (N − a + 1)(b + 1)(1 − e−λ2 )e−λ2 b ] = (1 − e−λ2 )[1 − (b + 1)e−λ2 b +

(4)

Then we have f (0) = 0; f (N ) > 0 f  (a) = 1 − N λ1 e−aλ1 , lnN λ1 f  (a) = 0 ⇒ a0 = . λ1 From (5) we have

(11)

We let F = 1 − (b + 1)e−λ2 b + (N − a)e−λ2 b (beλ2 − b − 1) = 1 − [(b + 1) + (N − a)(bλ2 − b − 1)]e−λ2 b . (12)

f  (a) > 0; a > a0 , f  (a) < 0; 0 < a < a0 . So a0 is the only minimum value of function f (a). From (3) we know that when a = a0 , FD-MAC will have maximum throughput gain than that of slotted ALOHA. From the practical meaning of parameter a0 , which represent the actual number of EF flows, we know that a0 > 0, which means (from (6)) that 1 (7) N λ1 > 1 ⇒ λ1 > . N If (7) is not met, it means that there will have no maximum throughput gain for any practical value of a, which is not anticipated for the performance of FD-MAC. Because Kl is the lower bound threshold for EF, all packet arrival rate λ1 of EF should be larger than Kl , so if we let Kl = N1 , then we can rewrite (7) as 1 (8) λ1 > Kl ⇒ λ1 > . N The proof of Lemma 1 has been accomplished. For Kh , with the conditions listed above, we have such lemma: N −a+b Lemma 2:The upper bound threshold Kh = b(N −a+1) . Proof: Given a data flow with a mean packet arrival rate of λ2 . If it is served in dedicated slots as an EF, then it would have a larger throughput than if it had been served as an MF; thus, we should regard this data flow as an EF. We analyze the throughput in both conditions (as an MF and an EF) separately and then compare the results. If this data flow is regarded as an EF, then all the packets belonging to this flow should be forwarded in dedicated time slots, and no contention would be anticipated. The throughput of this flow is 1 − eλ2 (assuming that there is no traffic overflow). Supposing there are already a − 1 EFs and b MFs in the network, the total throughput Se would be Se = (a − 1)(1 − e−λ1 ) + (1 − e−λ2 ) + (N − a)b(1 − e−λ2 )e−λ2 (b−1) .

(N − a)e−λ2 b (beλ2 − b − 1)].

(5) (6)

(10)

(9)

It is clear that in order to get Se − Sm > 0, F should not be smaller than zero. Suppose [(b + 1) + (N − a)(bλ2 − b − 1)]e−λ2 b > 1 ⇒ (b + 1)(N − a + 1) > b(N − a)eλ2 + eλ2 b .

(13)

From Taylor’s expansion form ex > 1 + x, (13) can be rewrite as: (b + 1)(N − a + 1) > b(N − a)eλ2 + eλ2 b ⇒ (b + 1)(N − a + 1) > b(N − a)(1 + λ2 ) + (1 + λ2 b) ⇒ N − a + b > λ2 (b(N − a + 1)) N −a+b . (14) ⇒ λ2 < b(N − a + 1) N −a+b Therefore, F < 0 ⇒ λ2 < b(N −a+1) . Then we have the N −a+b converse-negative proposition, λ2 ≥ b(N −a+1) ⇒ F ≥ 0.

N −a+b we can see from (11) that, if λ2 > b(N −a+1) ,the throughput gain G could be positive, then this flow is better to be classified as EF. Because Kh is the upper bound for a MF, so we may define N −a+b . (15) Kh = b(N − a + 1)

Equation (15) means that the mean packet arrival rate of a MF should be less than Kh , or this flow should be classified as EF, which will introduce positive throughput gain (G > 0) to the system. So the Lemma 2 is proven. In implementations, the flow number a, b could be chosen by the network administrator, based on the channel resource and statistical information of the historical data traffic. The value of a could be chosen as 1 ≤ a ≤ N . The value of b will be decided by the offered load.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

TABLE I E NVIRONMENT PARAMETERS parameters N a b λ1 λ2

VI. C ONCLUSION FD-MAC is a light-weighted, flow-driven MAC protocol which is suitable to be used in long distance wireless networking environment. Through theoretical analysis, the optimum key parameters of the protocol were given. Results show that the upper threshold Kh is related to the number of EF and MF traffic, as well as the number of time slots in one frame. The lower threshold Kl is only related to the number of time slots per frame. Based on statistical analysis of the past traffic, we can set optimum value for both parameters. In our paper, reservation was based on time slot, nevertheless, other resource such as frequency, codewords can also be used for reservation process with minor adjustment of the protocol. We would study them in our future work.

value 32 6 64 18.2 7.2

1

Normalized network throughput

0.9

0.8

0.7

ACKNOWLEDGMENT

0.6

This research work was supported by NSFC under Grant No. 60670280, National 863 Project of China under Grant No. 2008AA01Z216, and National 973 project of China under Grant No.2009CB3020402.

0.5

0.4

0.3 11

Fig. 3.

12

13

14

15 16 17 The upper bound threshold

18

19

20

21

Normalized throughput for various upper bound thresholds

V. E VALUATION In this section, we evaluate the relation between the value of FCT and the performance of FD-MAC in terms of network throughput. The simulation is based on QUALNET platform [14]. Simulation parameters are shown in Tab.I. The check period T is the length of 10 frames. So there are totally 320 slots in one check period. From equation (8) and (15) we can derive that Kh is 16.7 and Kl is 10. In our simulation, we fix Kl to the theoretical value, then we verify the optimum value of Kh through simulation. In Fig.3, the horizontal axis indicates the value of Kh and the vertical axis indicates the normalized network throughput. In this scenario, network throughput is defined as the number of packets that are successfully transmitted. From Fig.3, we can see that while the value of Kh is lower than 16, some mice flows are classified as EFs. Nodes forward the packets of these flows on the reserved slots. Therefore, many packets of EFs contend the slots without available reservation resource. The data collision probability is high in the contention area and the network throughput is low. As the upper bound threshold increases, there are more slots available for EFs. Many packets are transmitted successfully and the network throughput is soaring up. However, when the upper bound threshold is beyond the average EF rate(18.4), some EFs that the data arrival rate are not greater than the threshold can only contend for the slots. This induces the declination of successful transmission probability and network throughput in turn. We may find that when Kh is 17, which is close to theoretical value (16.7), the maximum network throughput would be achieved, the result justifies our analysis.

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