Dia 1

Dia 1

Department of Mathematical Information Technology TIES431 Tietokoneverkkojen jatkokurssi (3 op, 2 ov) http://www.cc.jyu.fi/~timoh/kurssit/verkot/verkot.html Content Functional aspect to the QoS in networks; components, protocols and management. The main focus will be Quality of Service in Internet. Requirements: seminar presentation+generate two questions related to it (2-3 students per group). Return your presentation slides to [email protected] at least

one hour before your presentation starts. attend at least to 6 seminar sessions complete 6 home exercises (from the others presentations) and the laboratory exercise OR attend at least to 6 seminar sessions Complete 6 home exercise Pass the exam and the laboratory lexercise Content Course book : Zheng Wang: "Internet Quality of Service: Architectures and Mechanisms ", ISBN: 1-55860-608-4 PhD Thesis by Alexander Sayenko: Adaptive scheduling for the QoS supported networks

Other useful books: Routing in the Internet (2nd Edition) by Christian Huitema W. Stallings: Data and Computer Communications, sixth edition, Prentice Hall. Chapters 12, 15, 16, 17. W. Stallings: High-speed networks, TCP/IP and design principles, Prentice Hall, 1998. Chapters 11-15. Detailed Content 1. Introduction, What and why QoS ? 2. Lectures and Seminar presentations:

QoS mechanisms: Packet classification and marking (TOS, DSCP) RFC2859, Classification overview QoS mechanisms: Traffic regulation Policing and Shaping QoS mechanisms: Resource sharing, scheduling (WRR, WFQ, DRR) Schedulers QoS mechanisms: Congestion management (RED, WRED) RED QoS mechanisms: Signalling NSIS QoS architectures - Integrated Services Integrated Services in the Internet Architecture: an Overview RFC1633, RFC2990 - Next Steps for the IP QoS Architecture QoS architectures - Differentiated Services: An Architecture for Differentiated Services, RFC 2475, RFC 3260 - New Terminology and Clarifications for Diffserv QoS provisioning Providing QoS, Inter-Domain QoS Provisioning and Accounting QoS management and monitoring (token bucket, EWMA, TSW) Monitoring, Integrated QoS monit.

Different applications (multicast, RT vs- NRT) MCAST CAC, Mcast Adaptive models (A. Sayenko's PhD thesis pp. 35-56) QoS Frameworks (A. Sayenko's PhD thesis pp. 62-78) Propose own topic Laboratory exercise WiMAX QoS Home work NS2 simulator model Test and monitor Questions and analysis

Exam Mit tarkoitetaan palvelun laadulla IP-verkoissa? Mit erilaisia mekanismeja sen toteuttamiseen on? Montako palvelunlaatuluokkaa on mahdollista toteuttaa DiffServarkkitehtuurilla? Montako nist todennkisesti toteutetaan tavallisessa operaattori/yritysverkossa? Avaa ja selit seuraavat termit. Kerro mys, mihin tarkoitukseen kutakin kytetn.

WFQ: RED: CBWFQ: LLQ/PQ: MQC: shaping: policing: Exam Oletetaan, ett yritys A haluaa omassa verkossaa kytt palvelunlaatuominaisuuksia. He ovat pohtineet liikenteens jakamista kolmeen eri kokonaisuuteen; VoIP, liiketoimintakriittiset sovellukset ja muu. Kuvaa lyhyesti, miten toteuttaisit QoSominaisuudet heidn verkossaan, kun yrityksell on kuusi toimipistett, jotka on yhdistetty operaattorin MPLS VPN palvelun kautta. Mit on multicast? Miten se eroaa unicastist ja broadcastist? Mist lydt listan kytss olevista multicast-osoitteista?

Mik on IGMP? Mik versio siit on tll hetkell kytss. Kuinka se toimii? Kerro mitk ovat PIM-SM ja MSDP jotka tll hetkell muodostavat internetin laajuisen multicast-verkon pohjan. Answers QoS:n avulla pyritn takaamaan erilaisille sovelluksille (VoIP, video, datan siirto), niiden vaatimat siirtoedellytykset.

Trkeimmt QoS parametrit: tarvittava kaista, viive ja sen vaihtelu sek hvikki). Luokitellaan liikenne eri luokkiin ja kohdellaan niit erilailla verkossa. Diffserv ja Inserv -arkkitehtuurit. Diffserv perustuu ToS (DSCP)kentn kyttn IP- paketissa ja Intserv RSVP:n kyttn resurssien varauksessa. ToS kentn kuusi bitti on mritelty uudelleen DSCP kentksi, joka mr miten pakettia pit kohdella per hyppy (PHB). Lisksi kytetn traffic policing ja traffic shaping menetelmi liikenneprofiilien sovittamisessa verkkoon esim. bandwidthbroker ja COPS- protokolla konfigurointitietojen siirtmiseksi aktiivilaitteille. Answers

TOS- kentss 8 bitti, joista 6 on mritelty Diffserv kyttn eli teoriassa 2^6 eri luokkaa. IETF on mritellyt kaksi PHB:t: Expedited Forwarding (EF) ja Assured Forwarding (AF). EF: paketit viipyvt mahdollisimman vhn aikaa reitittimen jonossa ja liikenne muokataan maksimikaistan mukaisesti. AF: Nelj rinnakkaista palveluluokkaa ja jokaisella luokalla on kolme pakettien tiputusluokitusta. Operaattorit kyttvt 3-4 luokkaa (VoIP, RT, NRT, BE) konfiguroinnin ja yllpidon helppous.. Introduction, Motivation, What and Why ??

What is the BIG picture in IP QoS What are the small pieces that for the big picture Traffic differentiation and Quality of Service What is the difference between these two What have been standardized on these areas Why to choose this or that method/architecture for particular application Are there any sense to make these things Introduction, Motivation, What and Why ?? Keep in mind:

ISPs are there for the money They dont care about you They dont care about your applications They dont care what you are doing They care about your money Therefore, They care your opinions They care that you are satisfied Internet QoS Common nominator Separate control path Router is divided into layers Data path (Forwarding) Control path (Path & connection control)

Management path (Device management) More/less processing More than BE Less than per packet per device processing Adaptive router Meter Classifier Shaper/Dropper DSWcalculator Scheduler

IIS IntServ Connection oriented nature on top of connectionless IP Control path build as separate messaging sequence with the help of reservation protocol and agents RSVP protocol is responsible to do actual messaging and book keeping CAC agent checks to see if there is free capacity to accommodate new realtime connections IIS IntServ Connection oriented nature of IntServ requires that there is book keeping between Connection identifier (FilterSpec) Resources (FlowSpec) Path (Route)

IIS - IntServ IIS - IntServ DS - DiffServ Connectionless class based differentiation policy build on top of IPv4 There is no connection control as the operation is based on the aggregates Control can be build as a outside functionality with brokering functionality RSVP signaling between end user and network broker to produce provisioning that resembles IntServ DS - DiffServ

Connectionless nature does not require per flow book keeping Aggregates must be kept but they are rather static Per user information is stored on the edge of the network DS - DiffServ Scheduler example: WFQ- based Load Balancing Algorithm WFQ scheduling policy is used (end-to-end delay bounds as well as guaranteed output rates for different traffic classes). Guaranteed rate for each flow in class i can be denoted as follows: Ri ,l

wi Bl Ni (1) where wiBl is the portion of the total bandwidth which service class i receives in path l. Ni denotes the number of ith class packets. The worst-case delay bound experienced by a packet belonging to a flow H i 2( K 1) Li L Di ,l max i h 1 Bh (2)

where Li denotes the max. packet size for a flow, Lmax is the max. size of a packet permitted in the network and Bh is the overall bandwidth on link h. Each flow is assumed to be regulated by the Leaky Token Bucket scheme with bucket depth and the token rate . The and can be viewed as the maximum burst size and the long term bounding rate. Load Balancing Algorithm

It is assumed that is equal to guaranteed rate Ri for the service class i (Eq. 1). If there are Ni active flows then the max. burst size is assumed to be equal to NiLmax. Hence, worst-case delay can be presented as: N i ,l Lmax 2( K 1) Li H Lmax Di ,l Ri h 1 Bh (3) When new ith service class connection request appear, the guaranteed rate (Eq. 1) and worst-case delay bound (Eq. 3) are recalculated and obtained values are used for determining the price for each path. The price of the path is dependent on the resource consumption as well as the congestion level of the path and it is defined as follows:

where coefficient denotes the priority of the path. i ,l Ri ,of l busy connections on LSP l in class i is Ni,l and the price charged per unit time for Because the D number ri ,l connection a single is ri,l,, the revenue paid by the ith class customer on LSP l is the product of Ni,lri,l. H Let xi,l be a binaryl variable such that xi,l=1, when connection in class i is transferred using LSP l, otherwise xi,l=0, i=1,...,m, l=1,...,L. (4) Load Balancing Algorithm The main goal of the proposed model is to maximize the total revenue R L

m maximize R N i ,l ri ,l xi ,l (5) l 1 i 1 subject to m x 1, l 1,..., L xi ,l 0,1 ,

i 1,..., m i ,l i 1 l 1,..., L Bi Ri ,l , Ri ,l 0 Di ,l Di ,max , Di ,l 0, Ri,l = guaranteed rate Bi = req. bandwidth Di,max = max. allowable delay

Di,l = delay on path l Ni = number of packets ri,l = price of the path Simulations In the simulations, the arrival rates of connection requests and the mean holding time of a connection are exponentially and uniformly distributed random variables, respectively. The traffic sources are divided between the three traffic classes (gold, silver and bronze) with different set of QoS parameters.

All traffic from SRC to DST is carried over MPLS network by using one of the parallel LSPs. All MPLS nodes use WFQ. Simulations Parameters of the service classes Class Type Max flows weight Buffer length (pkts) Bandwidth (kbit/

sec) Delay (msec) Gold Video (H.263) 10 0.6 50 280 80

Silver Video conf. (H.263) 18 0.25 100 67 150 Bronze Exponential (UDP) -

0.15 170 - - The performance of the proposed model is compared with three dynamic load balancing approaches: o o o Round Robin (RR) Random routing (RAN) Lightest Loading routing scheme (LL)

Scenario 1: Low Utilization Figures 2(a)-2(d) depict the utilization of each LSP during the simulation with all the load balancing approaches. o All the other models distribute traffic load more evenly between candidate paths and consume therefore relatively larger amount of network resources Mean end-to-end delays remain low with all the approaches due to small utilization of the paths, as can be seen in Fig. 3. Scenario 1: Low Utilization (a) Proposed model

(c) RAN approach (b) RR approach (d) LL approach Figure 2. Paths utilizations as a function of simulation time in scenario 1 Scenario 1: Low Utilization (a) Gold class delays (b) Silver class delays Figure 3. Mean delays as a function of the simulation time in scenario 1 Scenario 1: Low Utilization

There is no great difference between approaches in terms of network revenue, as can be seen in Fig. 4. o However, the proposed model produces the largest revenue because it distributes more flows to the shortest and therefore the most expensive path (see Eq. 4). Figure 4. Evolution of revenue in scenario 1 Scenario 2: High Utilization

The number of connection requests in each traffic class is higher than scenario 1 -> networks utilization increases (Fig. 5). The number of active traffic flows is restricted due to bandwidth constraint in Eq. 5 and therefore rate Ri (Eq. 1) can be guarantee to each traffic flows belonging to service class i. Figure 6 depicts gold and silver service classes mean end-to-end delays during the simulation. The proposed model can fulfill delay requirements while other approaches are not capable of providing delay gurarantees. Scenario 2: High Utilization (a) Proposed models (b) RR approach (c) RAN approach

(d) LL approach Figure 5. Paths utilizations as a function of simulation time in scenario 2 Scenario 2: High Utilization (a) Gold class delays (b) Silver class delays Figure 6. Mean delays as a function of the simulation time in scenario 2 Scenario 2: High Utilization In terms of revenue, the proposed model performs much better than other approaches in higly loaded network (Fig 7.). o o

Since the proposed model consider not only utilization but also the price of the path, it is capable of selecting the path producing the highest revenue. In this scenario, the revenue is improved more than 20% compared to RR and RAN approaches and about 50% compared to LL approach. Figure 7. Evolution of revenue in scenario 2

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