Calitatea Serviciilor Influenta Politicii de Prior It Ate

8/3/2019 Calitatea Serviciilor Influenta Politicii de Prior It Ate

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Precup Madalin

131 IA

Calitatea serviciilor: influenta politicii de prioritate

Priority Queuing



In aplicatia cu prioritate mai mica nu se pierd foarte multe pachete(maxim

7pachete/sec) deoarece pachetele sunt puse in asteptare “la coada” si in functie de

prioritate router-ul le trrimite in zona de asteptate.Aplicatia nu este cu prioritatemare,iar atunci nu se formeaza “cozi”foarte mari iar numarul pachetelor este cat mai

mic.



In aplicatia cu prioritate mai marese pird mai multe pachete(in primele2min nu se pierde nici un pachet,iar dupa aceea pachetele pierdute incep sa creasca-

pana la 10 pachete/sec deoarece pachetele prioritare au intodeauna intaietate,iar

celelalte cu prioritate mai scazuta “sunt aruncate”,iar daca mai apar pachete cu

prioritate mai mare acestea or sa fie trimise de router mai departe ,iar celelalte pachete

raman “la coada ” si cele cu prioritate mai scazuta pot fi trimise mai departe dupa foarte mult timp sau chiar niciodata si in acest fel se pierde informatia.Numarul

pachetelor este mai mare si din cauza ca legatura dintre aplicatie si Router_1 este

PPP_DS1 care nu este foarte scump si se pot lega maxim 8 conexiuni.



In cazul aplicatiei cu prioritate mai mica delay-ul este cu 1 milisecunda mai mare

deoarece legatura dintre apicatii este PPP_DS1 ,iar Router_1 in functie de prioritate

trimite informatia “mai departe”.



Queue_Delay_Variation



Buffer-ul si delay-ul sunt legate una de alta,iar delay-ul este legat de cantitatea de timpcare ii trebuie sa faca un switch cu buffer-ul full.Politica de prioritate determina ordinea

in care pachetele sunt servite iar politica de alocare a buffer-ului determina spatiul

buffer-ului care este alocat intre sesiuni.In aplicatia de prioritate mare si in cea de

prioritate mai scazuta invarcarea buffer-ului router-ului nu este mare(aceasta variind

intre 0.02 si 0.25pachete)



Politici de prioritate



Weighted Fair Queuing (WFQ)

Weighted Round Robin is a practical approximation of GPS which isparticularly

relevant in the case of ATM where “customers” are cells having an identicalservice requirement.A more generally applicable realization, known as Weighted Fair Queueing(WFQ) [13], is currently being implemented to schedule variable lengthpackets in theInternet. It is shown in [13] that this policy enjoys the property that, for anytrafficarrival pattern, provided the packet sizes are bounded, the differencebetween the individual queue backlogs in the GPS and WFQ systems remainsbounded. This property entails that performance results derived for the moretractable GPS system can also be applied (with necessary adjustments) toWFQ. In this paper we demonstrate that

queue lengths in WRR are also within bounded distance of correspondingqueue lengthsin GPS and that analagous reasoning therefore allows us to deduceperformance properties of WRR. The particular performance results of interest in this paper are LargeDeviations estimations of the individual queue length distributions, which dueto thebounded distance between their trajectories, are common to the threesystems GPS,WFQ and WRR.

The generalized processor sharing (GPS) discipline is proven to have two desirable

properties: (a) it can provide an end-to-end bounded-delay service to a session whosetraffic is constrained by a leaky bucket; (b) it can ensure fair allocation of bandwidth

among all back logged sessions regardless of whether or not their traffic is constrained.

The former property is the basis for supporting guaranteed service traffic while the later property is important for supporting best-effort service traffic. Since GPS uses an

idealized fluid model which cannot be realized in the real world, various packet

approximation algorithms of the GPS have been proposed. Among these, weighted fair

queueing (WFQ) also known as packet generalized processor sharing (PGPS) has beenconsidered to be the best one in terms of accuracy. In particular, it has been proven that

the delay bound provided by WFQ is within one packet transmission time of that

provided by GPS. We show that, contrary to popular belief there could be largediscrepancies between the services provided by the packet WFQ system and the fluid

GPS system. We argue that such a discrepancy will adversely effect many congestion

control algorithms that rely on services similar to those provided by GPS. A new packetapproximation algorithm of GPS called worst-case fair weighted fair queueing (WF2Q) is

proposed. The service provided by WF2Q is almost identical to that of GPS, differing no

more than one maximum size packet.



Store-and–Forward

Two problems relevant to the design of a store-and-forward communication

network (the message routing problem and the channel capacity assignment problem) are

formulated and are recognized to be essentially non-linear, unconstrainedmulticommodity (m.c.) flow problems. A Flow Deviation (FD) method for the solution

of these non-linear, unconstrained m.c. flow problems is described which is quite similar

to the gradient method for functions of continuous variables; here the concept of gradientis replaced by the concept of shortest route flow. As in the gradient method, the

application of successive flow deviations leads to local minima. Finally, two interesting

applications of the FD method to the design of the ARPA Computer Network are

discussed.

The combination of the buffer size of routers deployed in the Internet and theInternet traffic itself leads routinely to routers dropping packets. Motivated by this, we

initiate the rigorous study of dynamic store-and-forward routing on arbitrary networks ina model in which dropped packets must explicitly be taken into account. To avoid the

uncertainties of traffic modeling, we consider arbitrary traffic on the network. We

analyze and compare the effectiveness of several greedy, on-line, local-control protocolsusing a competitive analysis of the throughput . One goal of our approach is for the

competitive results to continue to hold as a network grows without requiring the memory

in the nodes to increase with the size of the network. Thus, in our model, we have link

buffers of fixed size, B, which is independent of the size of the network, and B becomes a parameter of the model.Our results are in contrast to another adversarial traffic model

known as Adversarial Queuing Theory (AQT), which studies the stability and growth rateof queues as a function of the network and traffic parameters. For example, in AQT theFurthest-To-Go (FTG) protocol is stable for all networks whereas Nearest-To-Go (NTG)

can be unstable for some networks. Unlike AQT, in our setting NTG is preferable to

FTG: we show that the NTG protocol is throughput-competitive on all networks whereasthe FTG protocol has unbounded competitiveness whenever a network contains even

small cycles

Ptiority Queuing

We consider the setting of a network providing differentiated services. As is often the

case in differentiated services, we assume that the packets are tagged as either being a

high priority packet or a low priority packet. Outgoing links in the network are serviced by a single FIFO queue.



Our model gives a benefit of α 1 to each high priority packet and a benefit of 1 to each

low priority packet. A queue policy controls which of the arriving packets are dropped

and which enter the queue. Once a packet enters the queue it is eventually sent. The aimof a queue policy is to maximize the sum of the benefits of all the packets it sends.

We analyze and compare different queue policies for this problem using the competitiveanalysis approach, where the benefit of the online policy is compared to the benefit of an

optimal offline policy. We derive both upper and lower bounds for the policies weconsider. We believe that competitive analysis gives important insight to the performance

of these queuing policies

First-In-First-Out (FIFO)

We consider exible manufacturing systems using the `First Come, First

Served' (FCFS or FIFO) scheduling policy at each machine. We describe and discuss

in some detail simple deterministic examples which have adequate capacity but

which,under FCFS, can exhibit instability: unboundedly growing WIP taking the form of a

repeated pattern of behavior with the repetitions on an increasing scale.Key Words:

scheduling policy, exible manufacturing system, queueing network,

stability, FIFO, `First Come First Served'.

We consider network models involving multiple ows with bu ering/ queuing at each

node (processor). Specifying a queue discipline (i.e., a scheduling policy for the pro-

cessing at nodes) then de nes the dynamics for the system. A queue discipline is

called stable if the queue lengths (WIP) remain uniformly bounded in time for any

realization | con guration, initial state | with input rates subject to the obvious capacitylimitations. We quote from 3] the observation that, \We have been unable to resolve

whether FCFS is stable | a signi cant open question." It is the point of this note

to resolve that question1 | to show by examples that the popular `First Come, First

Served' policy (FCFS | also known as FIFO = `First In, First Out') is not a stable

queue discipline.

We will use the terminology of manufacturing systems: we refer to the nodes as

machines and to the ows as product streams, although it is clear that models of this

sort arise also in other contexts. Thus, for each stream (product type) Pj one has a

sequence of tasks fig to be done at machines Mk(i). Associated with each task is a

processing time i, time units taken to process a unit of product; we do not imposeany time penalty for switching between tasks at a machine. The `obvious capacity

limitations' mentioned above now take the form:

whereCj is the (constant) input rate for eachPj, since this sum just gives theutilization

factor for Mk , i.e., the proportional time required for the processing to handle its share

of the load.

This is a deterministic continuum model, but we observe that for an analysis of (po-



tential) instability one is necessarily interested in long time scales and expects to treat

amounts of product large compared to a discrete unit. Thus, even if the underlying

model were discrete and stochastic, any uctuations can be expected to be negligible in

comparison with the quantities involved so a deterministic treatment, using the mean

values, gives a reliable description of the behavior. We do note that this negligibility

of small' uctuations only applies away from the `trivial' ground state | all bu ers re-maining empty with processing exactly matching input | so our deterministic analysis

is necessarily inadequate to consider any transitions from that state to the larger scale

scenarios we describe which might be induced precisely by those uctuations.

It is easily veri ed | much as for the earlier analysis of clearing policies 5], 4] that

| FCFS is stable within the restricted class of acyclic con gurations, in which one can

argue inductively, so our examples are necessarily nonacyclic. There is also good

reason to feel that instability cannot occur without substantial discrepancies in the

processing.

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Calitatea Serviciilor Influenta Politicii de Prior It Ate