The considered interaction, that can regard e.g. a two-host web communication, involves the following subset of elements of the network: (1) Host A (HA), playing ...
Model-driven Maintenance of QoS Characteristics in Heterogeneous Networks1 A. D’Ambrogio, V. de Nitto Personé and G. Iazeolla Dept. of Computer Science University of Roma TorVergata 1 Via del Politecnico, I-00133 Roma (Italy) {dambro,denitto,iazeolla}@info.uniroma2.it
Abstract. System QoS maintenance is the activity intended to maintain the system QoS at acceptable levels. The activity consists of continuously measuring the system QoS characteristics and adjusting the system parameters that affect them. This paper introduces a model-driven approach to the maintenance of QoS characteristics in heterogeneous networks that support geographically distributed processing services. The model is used as an on-line tool that enables network managers to rapidly adjust the network parameters to maintain the network QoS characteristics at required levels. The on-line use of the model requires short model evaluation times, which are obtained by use of a multi-level hierarchical hybrid technique. The application to the maintenance of timerelated and capacity-related network QoS characteristics is illustrated.
1. Introduction In recent times a structured collection of concepts, called QoS framework, has been developed to describe the Quality of Service (QoS) of IT systems and its use in providing open distributed processing services [5]. The framework is intended to assist those that produce specification and design of IT systems and those that define the communication services. In this paper we deal with the framework concepts applied to a heterogeneous network, simply called network, that supports geographically distributed processing services. In particular the paper deals with concepts related to network QoS characteristics and QoS requirements, network QoS management, operational phase of the network QoS management, network QoS maintenance and tuning activity of the QoS maintenance [5]. A QoS characteristic of the network is a quantified aspect of QoS, for example time delay, capacity, accuracy etc. A QoS requirement is the user expectation of the QoS, for example expectation that the time for a specific service (e.g. downloading a stream of data) must not exceed a specified value. The network QoS management refers to all the activities designed to assist in satisfying one or more QoS requirements. 1
Work partially supported by funds from the FIRB project on “Performance Evaluation of Complex Systems” and from the University of Roma TorVergata CERTIA Research Center.
The operational phase of the network QoS management denotes management activities intended to honor the agreements on the QoS to be delivered. The network QoS maintenance is the activity intended to maintain QoS to acceptable levels. This is performed by the tuning activity of the network consisting of a compensatory adjustment of the network operation. In this paper we assume that the network QoS tuning activity is directed by an efficient performance model to support the management decision process. Performance modeling has been in many cases used as an off-line approach to system evaluation [1,7]. This paper, on the contrary, deals with the on-line use of the model. In other words, the performance model is part of the compensatory adjustment loop of the network operation. The time-efficiency of the model is in this case essential to conduct the decision process in due time. Indeed, the model-based tuning mechanism requires the network manager to identify in a short time the network parameters to reset. The integration of similar performance models into commercially available network management tools is part of the advancement in the maintenance of the network QoS characteristics to effectively support geographically distributed services.
2. The considered network Fig. 1 gives a description of the network we consider, with a view of two communicating hosts, Host A and Host B, each one residing on an independent local area network (LAN), separated by a wide area network (WAN). By looking at the communication between two hosts residing on two separated LANs, we do not neglect the fact that there may exist hundreds of hosts, on thousands of LANs that compete for the use of the common WAN. The considered interaction, that can regard e.g. a two-host web communication, involves the following subset of elements of the network: (1) Host A (HA), playing the role of client host with an operating user; (2) Host B (HB), playing the role of server host; (3) the heterogeneous network N, composed of: (a) two separate LANs, the first (LAN1) a Token Ring for HA, and the second (LAN2) an Ethernet for HB (b) two gateways GW1 and GW2, that connect LAN1 and LAN2 to the WAN, respectively (c) the WAN, X.25 packet switched network, with its PS1, ..., PSn packet switching nodes. It is assumed the user residing in HA runs its client/server web application WP, consisting of two parts, WPA and WPB, running on HA and HB, respectively [3]. The WPA part is accessed by use of a web browser running on HA, and communicates with its peer part WPB, managed by the web server running on HB. It is assumed the interaction between WPA and WPB is based on the use of the application layer protocol HTTP [4]. This interaction takes place over the various components of the heterogeneous network N involving several technologies (Token Ring, X.25, Ethernet) and thus several mechanisms to deal with heterogeneity, in particular: (m1) protocol conversion, from the application layer protocol HTTP to the transport layer protocol TCP, to the network layer protocol IP, to the data-link layer and physical layer protocols (and viceversa), in either direction from
HA to HB, with the IP to X.25 (and viceversa) protocol conversion at the gateway level; (m2) packet fragmentation and re-assembly at many protocol conversion interfaces; (m3) window-type flow control procedure operated at transport layer by protocol TCP for a fixed window size of value C (for the sake of simplicity neither variable window sizes, nor the use of congestion-avoidance algorithms are considered). A2
... ...
LAN1
.. Token Ring A1
An
Host A
GW1 B2
PS1 PS2
WAN
Bm
...... GW2
Ethernet
LAN2
PSn
B1
Host B
Fig. 1. General view of the considered network
The more detailed view of the packet flow in the system platform is given in the network model illustrated later on in Fig.3. The model puts into evidence the work performed by hosts HA and HB and by the remaining components of the network in order to deal with mechanisms m1, m2 and m3, when transferring data from HA to HB (a symmetrical flow holds when transferring data from HB to HA). Packets in the IP form enter LAN1, from where they exit in LLC/MAC802.5 form to enter GW1, that first converts them into IP form and then fragments the IP packets into X.25 form to be accepted by the WAN. Vice versa for the GW2, where X.25 packets are first re-assembled into IP packets and then converted into LLC/MAC802.3 form for the LAN2, which in turn converts LLC/MAC802.3 packets into IP form.
3. The network performance model The considered network consists of various subsystems each of different complexity, and in turn consisting of various subsystems of various complexities. Each one of the
LANs, for example, is in itself a complex network (see Fig. 2), and similarly the WAN, and each of its PS1, ..., PSn packed switching nodes, and so on. msg
jobs flow tokens flow
sendmsg1 source
setmsg
sink
incrbits A createmsg
allocmsg
destrmsg
Submodel A
all frames delivered
tok sendfr1
welcome
bye
B
I alloctok
O reltok
frames still to be delivered
decrnfrm
Submodel B setmitt
transfrm
I
O getToken
putToken
Fig. 2. Part of Level-1 model (Token Ring part)
Fig. 2 gives a hierarchical illustration of the LAN model further detailed into submodels A and B, with additional symbols (createmsg, allocmsg, destrmsg, etc.) which are standard symbols of the extended queueing network notation [8]. Producing a model of the network with a unique abstraction-level could yield so many components and details to make the model very difficult to handle, and its evaluation so time-consuming (only simulation modeling would be possible) to make the model useless for its on-line use in the tuning activity of QoS network maintenance. In order to obtain model efficiency this paper thus introduces a hierarchical hybrid approach [6] basing on decomposition and aggregation [2]. To this scope, three abstraction levels are introduced: Level-1 abstraction: At this level the separable subsystems are first identified according to decomposability theory [2], and then studied in isolation. Assume the LAN1 is a separable subsystem. In this case it can be preliminarily studied separately from the rest of the network, then evaluated to obtain its end-to-end delay, and finally substituted in the network N by an equivalent service center whose service time is the endto-end delay obtained above. The decomposition and aggregation theory [2] tells us
under which circumstances such a substitution can be done without a large error. If this is possible, the LAN1 model, that normally consists of a very large number of service centers (see Fig. 2, where the extended queueing network notation [8] is used), is collapsed into a single equivalent center. The decomposability conditions for the LAN1, which can be formally verified, are respected in the considered model. The same reasoning applies to the LAN2, and to the WAN. In conclusion, at this level subsystems LAN1, LAN2 and WAN can be separately evaluated off-line to obtain their equivalent end-to-end delay and can then be replaced each by a single equivalent center. The off-line evaluation is made either numerically (Markov chain modeling) or by simulation since each independent model of such subsystems is too complex to be evaluated in closed form. Level-2 abstraction: At this level the entire network N is modeled as in Fig.3, in a simplified way, since its LAN1, LAN2, and WAN subsystems have been each aggregated into a single equivalent center, with service time (mean, variance, distribution) being the end-to-end delay obtained at Level-1. The aggregated service time of each LAN takes into consideration that fact that there may exist hundreds of hosts on the same LAN. In a similar way, the aggregated service time of the WAN takes into consideration the fact that there may be thousands of LANs that compete for the use of the WAN. The host HA is modeled as being divided into two sections to indicate a division of work between the application WPA & HTTP work (performed by the first section) and the TCP-IP conversion work performed by the second section. For similar reasons the host HB is divided into two sections.
WAN
Network N acknowledgement
n
second HB sect
LAN 2
WAN
GW 2
token GW 1
GET
LAN 1
λ interactive or bulk packets
second HA sect
WPA
γ(C-n)
token pool
Arrival Source
first HA section
λ
Sink
WPB
RELEASE
first HB section
Fig. 3. Level-2 model
The token pool with the GET and RELEASE nodes is used to model the so-called passive queue [8] to represent the window-type flow control procedure implemented by TCP between the exit of the first HA section and the entrance of the first HB section. For a window size C, up to C consecutive TCP packets can GET a token from the pool and enter the second HA section. Non-admitted packets are enqueued in front of this section. On the other hand, admitted packets exit the second HB section and RELEASE their token before entering the first HB section, thus allowing another packet to enter. When data transfer takes place in the opposite direction (from HB to
HA), the GET node with its queue takes the place of the RELEASE node, and vice versa. The Level-2 model is however still too complex to be evaluated in closed form, and thus we assume its evaluation is also made off-line, numerically or by simulation. The evaluation will yield the acknowledgement throughput (or number of returned acks per time unit, in the figure), denoted as γ (C-n), where C is the chosen window size, n the number of acknowledged packets and C-n the number of still unacknowledged ones in the network. The evaluation is very time-intensive, being done for each C and n, and makes it imperative to execute it off-line. Level-3 abstraction: At this level the entire network N is replaced by a single equivalent center (see Fig.4) whose service rate is the ack throughput γ (C-n) calculated at abstraction level 2. In other words, the entire network N is now seen as a single server λ
i Network E(ts) = function of ack throughput ( γ(C-n) )
Fig. 4. Level-3 model
system with arrival rate λ (the packets arrival rate from the user application in HA) and mean service time of value E(ts) depending on the γ (C-n) throughput, namely:
E(ts)
= 1 / γ(i) = 1 / γ(C)
for 0 ≤ i ≤ C for i > C
with i the number of packets in the queue, including server. Such a model is a model of the M/M/1 type [7] with state-dependent service time (i.e. dependent on the number n of packets in the center). Its response time gives the network end-to-end time delay, and its throughput the network capacity we are looking for. The evaluation can be done by a markovian approach [6] in a sufficiently short time. The poisson assumption (M) for the arrival process of mean λ is a reasonable assumption for the packets flow from the client user application. The exponential assumption (M) for the network service time is a pessimistic assumption that introduces a security factor on the QoS maintenance, which can however be replaced by a general service time assumption (G) by introducing the coxian approximation without a large impact on the model processing time. This final model is thus the one to be used on-line in the tuning activity of the network QoS maintenance. 3.1 Model effectiveness By use of the proposed multi-level approach that combines, in a hybrid way, simulation evaluations (Level-1 and Level-2) and analytical evaluation (Level-3), the model
evaluation time is drastically reduced with respect to the brute-force single-level approach in which the entire network is simulated with all the details of the LANs, the WAN, etc. Indeed, the evaluation time in the multi-level approach is of just a few seconds of Pentium4, against the about 30 hours of the brute force single-level approach. In more detail, the 30 hours reduce themselves to just 40 minutes if the Level-2 model is simulated, and to just a few seconds if the Level-3 model is analytically evaluated instead.
4. The model-driven QoS maintenance process The QoS characteristics we address are time-related and capacity-related characteristics. The time-related one is the network end-to-end delay, or the time for a packet (generated by the user application) to get across network N. The capacity related one is instead the network throughput or number of packets per time unit that can be delivered from source (HA) to sink (HB) through the network. Such two characteristics are of interest to the QoS maintenance tuning activity since they directly affect the ability of network N to meet the QoS requirements, as seen below. The window size C is an important factor for both such QoS characteristics, together with the arrival rate λ of packets generated by the user application and characterized by a given proportion α of so-called bulk packets with respect to interactive packets. Bulk packets are packets whose average length is much larger than that of interactive ones (e.g. 8000 bytes against 100) and the proportion α depends on the type of user application. Interactive packets are the dominant part in applications where command sending and short reply dominate, while bulk packets prevail in applications with many upload and download operations. As stated above, our model can address time-related and capacity-related characteristics. An example of QoS requirement with capacity-related characteristic is: E1: the time to download a stream of data of x KByte of length from sending host A to receiving host B should not exceed y sec. An example of QoS requirement with time-related characteristic is: E2: the time to receive at host B a command of one packet length sent from host A should not exceed z sec. In order to maintain QoS to the levels specified by QoS requirements like E1 or E2, in this Section we focus on the QoS tuning activity (see Sect.1) of the network throughput or else end-to-end delay. To this purpose a control system is foreseen which takes account of the difference between the required and the measured QoS and yields a tuning feedback to the system. Fig.5 illustrates the tuning mechanism. The mechanism includes the following operations [5]: a) measurement of the system performance (SP) characteristics, e.g. mean network throughput or mean end-to-end delay; b) translation to QoS values; c) calculation of QoS difference; d) identification of QoS related system components; e) modification of parameters of identified components and system reset.
The system performance characteristics to be measured are related to each considered QoS requirement. In case E1 the system performance characteristic to monitor is the mean network throughput, measured in packets per second. In case E2 the system performance characteristic is the mean end-to-end delay, or the time to send a packet from host A to host B, measured in seconds. Assume the agreed QoS requirement is e.g. E1. Being E1 a capacity-related QoS requirement the network throughput should be continuously monitored. This can be done by use of a measurement probe of the SP (see Fig. 5) and then translating it into QoS values, i.e. in terms of the time to download the stream of data. In such a way it is possible to verify if the negotiated QoS requirement E1 is satisfied. To this purpose the difference (∆QoS) between the measured QoS and the negotiated QoS is calculated and in case an established threshold is exceeded the necessary adjustment of system parameters is performed and the system is reset to obtain the required QoS. User
Measured QoS
Negotiated QoS
Achieved QoS
∆ QoS
from SP to QoS
from QoS to SP
Measured SP
System reset Service Access Point
System Performance (SP)
Fig. 5. Network tuning mechanism [5]
By incorporating the performance model into the decision process of the tuning operations d) and e), the QoS related system components can be identified and directions can be obtained on the reset of system parameters to obtain performance improvements. The efficiency of the proposed model (see Sect.4) is an essential means to conduct the decision process in due time. Indeed the tuning mechanism requires that by use of such model the network manager make model evaluation in a way to identify in a short time the system parameters to reset to new values. According to the illustrated model, main parameters to reset are: • arrival rate λ of user application packets • network window size C and decisions are to be taken on the effects on end-to-end delay and network throughput of such parameters reset.
Fig.6 gives the mean end-to-end delay as a function of the packets arrival rate λ, for various values of the window size C and for a fixed percentage of interactive/bulk packets α = 0.9. 25
end-to-end delay (sec)
20 15
C=4
10
C=7 C = 12
5
λ
0 0,5 1,0
1,5
2,0
2,5 3,0
3,5
4
4,5
5
λ (packets/sec) Fig. 6. Mean end-to-end delay versus λ, for α=0.9
Fig.7 gives the network throughput as a function of the packets arrival rate λ, for various values of the window size C and for a fixed percentage of interactive/bulk packets α = 0.4. 2,5
network throughput (packets/sec)
2 1,5
C=4 C=7 C = 12
1 0,5
λ
0 0,5
1,0
1,5
2,0
2,5
3,0
3,5
λ (packets/sec) Fig. 7. Network throughput versus λ, for α=0.4
As an example application of the network tuning decision process, assume to be in the case of the QoS requirement E1, and that the negotiated QoS is a time of no more than 10 seconds to download a stream of data of 140 Kbytes. In other words, a throughput of no less than 14 Kbytes/sec, or 1,75 packets/sec for packets of 8 Kbytes length, is required. By use of the model it is possible to find the values of the parameters λ or C that guarantee a mean network throughput greater than or equal to 1,75 packets per sec-
ond. By looking at Fig. 7 it is easily seen that for 1,75 ≤ λ ≤ 2 only a window size C=12 can be chosen, while for λ > 2 C=7 can be chosen as well. In a similar way, assume to be in the case of the QoS requirement E2, and that the negotiated QoS is a time of no more than 15 seconds to deliver a command of one packet length to B. By use of the model it is possible to find the values of the parameters λ or C that guarantee a mean end-to-end delay lower than or equal to 15 seconds. By looking at Fig. 6 it is easily seen that: - for λ ≤ 2,8 all values of the window size C (4, 7 or 12) can be chosen, - for 2,8 < λ ≤ 3,8 C=7 or C=12 can be chosen, - for 3,8 < λ ≤ 4,3 only C=12 can be chosen, while no values of C can guarantee the considered requirement if λ > 4,3.
5. Conclusions A performance model-driven approach to network QoS maintenance has been introduced. The considered network is a heterogeneous network that supports geographically distributed processing services. The performance model allows to effectively implement tuning activities by enabling network managers to identify in a short time how to adjust the network parameters in order to continuously maintain at the required levels the network QoS characteristics. The necessary model efficiency has been obtained by use of a multi-level hierarchical hybrid technique. The model-driven approach has been applied to the maintenance of time-related and capacity-related network QoS characteristics.
References 1. 2. 3. 4. 5. 6. 7. 8.
Bolch G., Greiner S., de Meer H., Trivedi K.S., Queueing Networks and Markov Chains, Wiley, New York, 1998. Courtois P.J., Decomposability: Queueing and Computer System Applications, Academic Press, 1997. D'Ambrogio A., Iazeolla G., Steps towards the Automatic Production of Performance Models of Web Applications, Computer Networks, 41 (2003) 29-39. Fielding R., Irvine U.C., Gettys J., Mogul J.C., Frystyk H., Masinter L., Leach P., BernersLee T., Hypertext Transfer Protocol- HTTP/1.1, Internet Request for Comments RFC 2616, June 1999. ISO/IEC 13236-1998 International Standard. Information Technology-Quality of Service: Framework. Korner U., Fdida S., Perros H., Shapiro G., End-to-End Delays in a Catenet Environment, Performance Evaluation Review, vol. 15, February 1988. Lavenberg S.S., Computer Performance Modeling Handbook, Academic Press, New York, 1983. Sauer C., Mac Nair G.A., Simulation of Computer Communication Systems, Prentice Hall, 1983.
