Tugas Jartel

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which covers SNA control mechanisms. Other types of congestion control have also been

suggested or used, and some of these will be described later in this chapter. Control mechanisms

are always needed to guard against stochastic fluctuations in traffic that may arise, which may

temporarily deplete network resources (nodal buffers and transmission links) and causecongestion to develop.

When it occurs, congestion manifests itself in two ways: Time delays will increase markedly inthe network, and the throughput, measured in packets per unit time delivered to their

destinations, may begin to decrease as offered load increases. Both effects are diagrammed in

Fig. 5-8. As shown in the figure, the decrease in throughput with an increase in offered load beyond a specified operating point is due to the blocking of finite resources in the network

(buffers and finite line capacity). If an offered load is high enough, a deadlock situation may

even prevail: All buffers are filled, traffic ceases to flow, and the throughput drops to zero.

Strategies have been proposed to prevent the occurrence of deadlock [CIES], [KLEI 1976].Deadlock is not considered explicitly in the viruial circuit window flow-control analysis

described in this section. Rather, it is assumed that the control mechanism eliminates the

congestion region of the throughput curve of Fig. 58; infinite buffer models used for the analysisdo not exhibit the deadlock behavior of that curve. In Section 5 — 5, which cover congestion

control by input-buffer limiting, the phenomenon of deadlock does appear in the model used,

since it incorporates a finite buffer. The use of (more realistic) finite buffers in the VC model of

this section would complicate the analysis immeasurably with little gain in insight.

The purpose here is to compare window control mechanisms on the basis of their time delay-

throughput characteristics. We assume chat these mechanisms, if properly designed, do not letdeadlock develop. To carry out the analysis we first develop a model for the virtual circuit, then

show how the window control mechanism may be easily modeled on top of this.

[GIES] A. Giessler et al., “Flow Control Based on Buffer Classes,” IEEE Trans. on Comm., vol.

COM-29, no. 4, April 1981, 436-443.

[KLEI 1976] L. Kleinrock, Queuing Systems. Volume 2, Computer Applications, John Wiley &Sons, New York, 1976.

5 — 2 — 1 Virtual Grcuit Model

Consider a virtual circuit covering M store-and-forward nodes from source to

destination in a packet-switched network. A typical example appears in Fig.

5 — 9(a). Three virtual circuits are shown in this small network example. All

emanating from the same source node I. VC1 traverses M 3 store-and-for ward

nodes (including the source node), while VC2 and VC3 each cover two store-and-forward nodes. The virtual circuits overlap in three of the network

links. 1f we focus on only one VC, a little thought will indicate that it may

be modeled by the M queues in series of Fig. 5 — 9(b). This is an extension of

the single-queue model for the store-and-forward process at a node in a

network

that was treated in Chapter 2. The parameter). represents. as usual, the

average



packet-arrival rate (load) to the VC, and Poisson arrival statistics are

assumed.

As was the case previously, this is the simplest model possible: A single

server

(single transmission link) servicês each queue.

Propagation delay has been neglected The two sources of delay in each

queue are thus queueing deLay (wait time) and transmission time. The latter

depends. of course, on packet length and transmission-link capacity. For the

i th

queue in the VC (I I M), the transmission rate or capacity is denoted by p

packets/sec. (The daca link control on each link along a VC is ignored her.

Recall that by virtue of the network layering concept used, the daca link

layer

provides the VC with an error-free, transparent transmission medium on each

link along the path. Occasional bit errors Will cause link frames, and hence

packets within them, to be retransmitted. This phenomenon occurs relatively

infrequently and is assumed to have negligible effect on the VC time delay;

thus

it is ignored. The transparency of che data link layer enables us co decouple

the

characteristics of the two layers.)

A more general (and more valid) model would show the complete network

represented by interconnected queues with virtual circuits superimposed arid

interacting. The analysis of such a queueing network is much mòre complex and

is deferred until Section 5 — 4. It is apparent from Fig. 5 — 9 that even if one

focuses on a single VC. as is the case here, two or more VCs may share nodal

queues arid transmission links, giving rise to queues with multiple

customers.



180 Network Layec Flow Control and Congestion Control

5 — 2-2 Sliding-window Model

The discussion up to this point has focused on validating the queueing

network model of Fig. 5 — 9(b) for a single virtual circuit. How does one now

superimpose the flow-control mechanism on this model? We describe the

sliding-window control first. Recall from Section 5 — 1 that in this control

mechanism each packet is individually acknowledged as it arrives at its

destination. The acknowledgement, on arriving back at the source, shifts the

window forward by one, allowing another packet to enter the network if all

packets in a window have been previously transmitted. An example appears in

Fig. 5 — 10. We now use the letter N to represent the window size; the letter w

will be used to describe the next window control mechanism that we model and

analyze.

By virtue of the sliding-window control, packetscan be transmitted onto the

VCso long as there are fewer than N along the VC. IfN have already entered

the

VG no more are allowed in until an acknowledgement packet arrives, sliding

the window forward by one. How arriving packets are handled when the window

is depleted (N unacknowledged packets are in transit along the VC) gives rise

to

a variety of control models. We choose the simplest case: Packets are assumed

blocked and lost to the system if, on arrival, N packets are outstanding

along the

VG. This is often a realistic case. Consider two examples. In the first

example,

that of interactive terminals accessing a network, terminal keyboards might

be

locked under conditions of congestion. The user must wait until a later time

to

transmit his or her data. The packets he or she might have transmitted are

thus

• Figure.5— 9 Single virtual circuit model

a. Virtual circuits in a network

b. Virtual circuit queueing model



Figure 5 — 9(b) thus should really show multiple customer streams arriving at

various queues along the VC and exiting as they move along their own VC. The

simplest model that takes these added customer types into account assumes

that

they use up a portion of the transmission capacity on a given link, reducing

the capacity available to the VC user. This concept is made more rigorous and

is

justified mathematically in [PENN] and (SCHW 19771. The s shown in Fig.

5 — 9(b) are assumed to have been reduced accordingly.

(PENN] M. C. Pennotti and M. Schwartz. “Congestion Control in Store and

Forward

Tandem Links,” IEEE Tran.m. on Cornu., vol. COM-23, no. 12, Dec. 1975, 1434 —

1443.

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