Patent Application
20050027503
The present invention is related to the invention described in U.S. Patent Application Attorney Docket D 1-4-2-1-3, entitled “Extensible Traffic Generator for Synthesis of Network Data Traffic,” which is filed concurrently herewith and incorporated by reference herein.
The present invention relates generally to communication systems, and more particularly to techniques for generating data traffic for use in testing or other processing applications in such systems.
Traffic generators are commonly utilized in generating data traffic having characteristics suitable for testing a given communication system design. Such traffic generators may be implemented in hardware or software. Data traffic characteristics such as the time distribution of packet arrival are critical for testing communication system performance parameters such as buffering and scheduling capacity. It is generally desirable for the traffic generator to provide data traffic output which closely models the “real-life” behavior of packet arrival timing in the system. For example, such behavior often involves so-called burst arrival, when a certain number of packets arrive substantially back-to-back, that is, one after another without any significant intervening time between arriving packets.
In order to provide proper stress testing of the components of a communication system, in a system design phase or otherwise, a traffic generator should incorporate an efficient and accurate burst model. Unfortunately, conventional traffic generators typically utilize burst techniques, such as constant burst or probabilistic burst, that fail to provide adequate levels of efficiency and accuracy. As a result, such traffic generators do not provide sufficiently close modeling of “real-life” packet arrival behavior in a communication system.
Although other burst modeling techniques are known in the context of queuing theory, such techniques are often not readily applicable for use in practical hardware or software traffic generators. One such technique is the Hurst parameter, which has been used to describe burst behavior in theoretical network traffic description as well as in predicting natural burst events such as floods. Additional details can be found in, for example, W. Stallings, “High Speed Networks and Internets: Performance and Quality of Service,” Chapter 9, and W. E. Leland, “On the Self-Similar Nature of Ethernet Traffic,” IEEE/ACM Transactions on Networking, February 1994. However, the Hurst parameter is mathematically very complex, and therefore difficult to understand and formulate. In addition, it exhibits a computational complexity which makes it highly impractical to implement in a hardware or software traffic generator.
Accordingly, a need exists in the art for a traffic generator which provides improved modeling of burst arrival, in a manner that overcomes the disadvantages of the conventional techniques noted above.
The present invention provides a traffic generator having an enhanced burst modeling feature based on what is referred to herein as a compensatory burst model.
In accordance with one aspect of the invention, a traffic generator generates a first type of traffic in accordance with a given distribution, and generates a second type of traffic that includes at least one traffic burst. The traffic burst is generated based at least in part on an amount of the first type of traffic generated over one or more time intervals.
By way of example, in an illustrative embodiment, generation of the second type of traffic involves accumulating traffic over one or more of the time intervals for which the first type of traffic is generated, and generating the traffic burst based at least in part on the accumulated traffic. More specifically, the second type of traffic may comprise a plurality of traffic bursts, with a given one of the traffic bursts being generated by determining a current burst size and a current compensatory-accumulation size, creating an initially-empty burst container having a capacity that is equal to the burst size, adding compensatory traffic to the burst container whenever the total traffic of the first type generated within a given sample slot time is less than a comparison level, such that for each such addition of compensatory traffic, a level of traffic in the burst container increases by the compensatory-accumulation size, and generating the given traffic burst when the burst container level is greater than or equal to the burst size.
Advantageously, the compensatory burst model generates traffic bursts in a manner which tends to compensate for temporary reductions in the amount of traffic of the first type, so as to substantially maintain a desired level of traffic flow.
The present invention will be illustrated herein using an exemplary traffic generator providing an enhanced burst modeling feature. It should be understood, however, that the techniques of the invention can be more generally applied to any type of traffic generation application. The invention does not require the particular elements of the illustrative embodiment, and other elements can be used in addition to or in place of the particular elements shown.
The traffic manager 102 manages the traffic generation functions of the traffic generator 100, and includes in this embodiment a timestamp generator 110, a timestamp manager 112, a protocol data unit (PDU) generator 114, a traffic supplier 116, and an event subsystem and/or forwarder 118. Generated traffic patterns, parameters and other traffic-related information are stored in the traffic file memory 104. Traffic generated by the traffic generator 100 under the control of the traffic manager 102 may be delivered via output interface bus 106 to one or more devices.
In generating traffic using the techniques described herein, the traffic manager 102 may also utilize distribution models 120, traffic models 122, protocol models 124, and configurable elements 126. Examples of configurable elements include a pattern generator 130 and a sequencer 132.
Additional details regarding the operation of one or more of the modules or other elements of the traffic generator 100 may be found in the above-cited U.S. Patent Application Attorney Docket Attorney Docket D 1-4-2-1-3.
The traffic generator 100 or other traffic generator configured in accordance with the invention can be implemented in hardware, software, firmware or any combination of these. For example, the traffic generator 100 may be viewed as a hardware traffic generator or a software traffic generator, or a combination of both. Generally, the traffic generator 100, whether implemented as a hardware traffic generator, a software traffic generator or a combination of both, may be implemented as or within an information processing device having at least one processor and at least one associated memory.
A more particular example of a software traffic generator is a software traffic generator which comprises an element of a software-based development tool for simulating the operation of interconnected integrated circuits or other types of electronic systems. Such a software-based development tool is typically configured to run at least in part on a general-purpose computer, workstation or other information processing device comprising at least one processor and an associated memory.
The traffic generator 100 is configured to execute one or more traffic generation processes, as will be described in greater detail below in conjunction with
It is also to be appreciated that the traffic generator 100 in the illustrative embodiment may further include, in addition to or in place of the particular modules or other elements shown in
The present invention in accordance with one aspect thereof provides improved techniques for burst modeling in a traffic generator. The techniques can be utilized in generating packet bursts or other bursts of data traffic. For example, the techniques can be used to model the behavior of file transfer protocol (FTP) traffic experienced by a network server in a network-based communication system, and in numerous other traffic modeling applications.
The particular burst model utilized in the illustrative embodiment may be stored in the set of traffic models 122, and utilized by the traffic manager 102 to generate the corresponding traffic in the manner described herein.
It is to be appreciated that, although described in the context of network traffic arrival, the burst modeling techniques of the invention can be applied to a wide variety of other traffic generation applications. Also, the invention does not require the use of packet-based traffic or any other particular data traffic format.
In a generalized queuing system, inter-arrival time between two consecutive queue elements follows a certain probabilistic distribution, which may be used to model the operation of the system. Similarly, the data traffic that arrives at a given node in a network-based communication system may also be observed to follow a probabilistic distribution taken from queuing theory. An example of one such probabilistic distribution is the well-known Poisson distribution. Assuming that the sequence of inter-arrival times between consecutive queue elements is represented by {A1, A2, A3. . . Ak}, then the arrival process in the corresponding queuing system is said to follow a Poisson distribution if the following holds:
Burst arrival occurs in the generalized queuing system when there is no time difference between arrivals of elements of the queue. As indicated previously, in the data traffic context, burst arrivals generally correspond to back-to-back packet arrivals. Conventional techniques are unable to provide efficient and accurate modeling of such a condition, in a manner suitable for use in a hardware or software traffic generator.
The illustrative embodiment of the present invention provides an improved burst model referred to herein as the compensatory burst model. It should be noted that the compensatory burst model may be combined with other models that generate non-burst arrivals in order to achieve a desired traffic arrival pattern.
Utilizing the compensatory burst model of the illustrative embodiment, burst traffic may be viewed as being dependent at least in part on the normal course of packet arrivals. For example, if a certain time interval witnesses a shortage of packet arrivals, packet bursts may be viewed as eventually compensating the shortage, such that a particular level of packet flow is maintained over a longer period of time.
The compensatory burst model will now be described in greater detail with reference to the diagrams of
The term “burst container” as used herein is intended to include, by way of example and without limitation, a counter or other set of information stored in a specified location in a memory of an information processing device. The process of accumulating burst traffic by adding traffic to a burst container may be implemented by incrementing a counter or other set of information which characterizes the contents of a burst container. It should therefore be understood that references in the description of the illustrative embodiment to the accumulation of burst traffic or the addition of traffic to a container do not require the use of actual traffic per se, but can instead be implemented in a straightforward manner using conventional counters, data structures and/or associated processing logic.
In the illustrative embodiment, the comparative traffic may be generated so as to follow the above-described Poisson distribution. Other types of distributions may be used for generating the comparative traffic, including by way of example a Gaussian distribution or any other desired distribution, as will be readily appreciated by those skilled in the art. The comparative traffic may thus be generated using a given distribution which is itself a combination of multiple distributions.
It will be assumed for purposes of illustration that the compensatory burst model operates using a number of user-defined parameters. These parameters may include the following:
With reference to
Similarly,
This compensatory-accumulation size is also referred to herein as a “mug” size, in that the compensatory burst model may be viewed as filling a given burst container 300 with a mug 302, as indicated in the diagram of
With continued reference to
The sample slot times denoted generally by the solid circles 320, 322, 324, 326 and 328 denote sample slot times for which the amount of generated traffic is less than the comparison level, with each such sample slot time resulting in an increase in the contents of the burst container by an amount corresponding to the compensatory-accumulation size. The sample slot time denoted generally by the dashed circle 330 represents the sample time slot for which the burst container level becomes greater than or equal to the current burst size. The burst therefore occurs substantially at this point in time. The process of accumulating burst traffic in the burst container and subsequently generating a burst when the burst container level equals or exceeds the burst size then repeats itself indefinitely, for as long as this particular burst modeling is desired.
A given interval between two consecutive bursts in the illustrative embodiment is always a multiple of the sample slot time. A longer sample slot time will therefore result in longer intervals between consecutive bursts.
Other parameters, such as the comparison level, compensatory-accumulation size and burst size, also influence the interval between consecutive bursts. For example, the higher the comparison level, the longer the interval between consecutive bursts. Similarly, the larger the burst size, the longer the interval between consecutive bursts, and the larger the generated bursts. With regard to the compensatory-accumulation size, an increase in this size will shorten the interval between consecutive bursts, and vice-versa.
A more detailed example of a traffic generation process utilizing the compensatory burst model of the invention will now be described. It will be assumed that the comparative-traffic distribution is selected as a Poisson distribution, and that a fixed sample slot time and a fixed comparison level are used. The time instance at which a given traffic burst is generated is denoted in this example as tb. The traffic generation process with compensatory burst modeling proceeds as follows.
At Time Instance (0):
The generated comparative traffic is compared with the comparison level. If the generated comparative traffic is less than the comparison level, an amount of compensatory traffic equal to the compensatory-accumulation size is added to the empty burst container, so that the current burst container level increases by the compensatory-accumulation size. No compensatory traffic is added to this burst container if the generated comparative traffic is greater than or equal to the comparison level. After addition of compensatory traffic to the burst container, the accumulation of compensatory traffic in the burst container is compared with the burst size. If the accumulated compensatory traffic is greater than or equal to the burst size, the burst is generated at a burst time tb which corresponds substantially to time instance (0+sample slot time). It will be assumed for this example that the result of this comparison of accumulated compensatory traffic with burst size indicates that the accumulated compensatory traffic is less than the burst size.
At Each of One or More Subsequent Time Instances (0+2*(Sample Slot Time)). (0+(n−1)* (Sample Slot Time)):
The total comparative traffic generated between the current time instance and the immediately preceding time instance is compared with the comparison level. If the generated comparative traffic is less than the comparison level, an amount of compensatory traffic equal to the compensatory-accumulation size is added to the burst container, so that the current burst container level increases by the compensatory-accumulation size. No additional compensatory traffic is added to the burst container if the generated comparative traffic is greater than or equal to the comparison level. After addition of compensatory traffic to the burst container, the accumulation of compensatory traffic in the burst container is compared with the burst size. If the accumulated compensatory traffic is greater than or equal to the burst size, the burst is generated at a burst time tb which corresponds substantially to the current time instance. It will be assumed for this example that the result of this comparison of accumulated compensatory traffic with burst size indicates that the accumulated compensatory traffic is less than the burst size for each of the time instances (0+2*(sample slot time)), . . . (0+(n−1)*(sample slot time)).
At Time Instance (0+n*(Sample Slot Time)):
The total comparative traffic generated between the time instance (0+(n−1)*(sample slot time)) and the time instant (0+n*(sample slot time)) is compared with the comparison level. If the generated comparative traffic is less than the comparison level, an amount of compensatory traffic equal to the compensatory-accumulation size is added to the burst container, so that the current burst container level increases by the compensatory-accumulation size. No additional compensatory traffic is added to the burst container if the generated comparative traffic is greater than or equal to the comparison level. After addition of compensatory traffic to the burst container, the accumulation of compensatory traffic in the burst container is compared with the burst size. If the accumulated compensatory traffic is greater than or equal to the burst size, the burst is generated at a burst time tb which corresponds substantially to the time instance (0+n*(sample slot time)). It will be assumed for this example that the result of this comparison of accumulated compensatory traffic with burst size indicates that the accumulated compensatory traffic is greater than or equal to the burst size for time instance (0+n*(sample slot time)), such that the burst is generated substantially at this point in time.
At Time Instance (0+(n+1)*(Sample Slot Time)):
The foregoing traffic generation example can be generally viewed as comprising the following steps for each burst to be generated:
A given burst in this example is thus accumulated over a comparative traffic process. During the burst, the comparative traffic process is preferably halted, but such halting is not a requirement of the invention.
An example set of parameters for use in the illustrative embodiment of the invention is as follows.
It should be understood that these particular parameter values are provided solely by way of example. The invention does not require the use of these values or any other particular parameter values.
The combination of the comparative traffic and the traffic bursts advantageously provides improved modeling of “real-life” traffic behavior. More specifically, with regard to the present example, the traffic arrival primarily follows a Poisson distribution, with the periodic bursts tending to compensate the temporary loss in normal traffic arrival, such that a particular level of traffic flow is maintained over a longer period of time.
The present invention in the illustrative embodiment described above overcomes one or more of the drawbacks of the conventional techniques. For example, a traffic generator with an enhanced burst modeling feature in accordance with the invention provides improved efficiency and accuracy in modeling of “real-life” traffic behavior in a network-based communication system. A high degree of flexibility and user control in the burst generation process is provided. Also, the invention can be readily implemented in a practical hardware or software traffic generator. The invention allows a wide variety of burst-related processing applications, such as the benchmarking of communication systems against burst behavior, to be implemented in an efficient manner.
As mentioned previously, one or more software programs for implementing the traffic generation functionality described herein may be stored in a memory of an information processing device and executed by a processor of that device.
It should again be emphasized that the above-described embodiment is intended to be illustrative only. For example, alternative embodiments may be configured which utilize different traffic generator configurations, modeling parameters, parameter values, or processing steps than those specifically described herein.
These and numerous other alternative embodiments within the scope of the following claims will be apparent to those skilled in the art.
