초록 ▼

Self-similar data communication in network traffic is modeled real time and is analyzed using a Markov modified Poissen process (MMPP) to characterize the traffic flow and to accommodate high variability in traffic flow from one time period to the other. The analysis is performed at multiple time le

Self-similar data communication in network traffic is modeled real time and is analyzed using a Markov modified Poissen process (MMPP) to characterize the traffic flow and to accommodate high variability in traffic flow from one time period to the other. The analysis is performed at multiple time levels using a bottom-up approach. The parameters of the model are adjustable at each level according to the traffic parameters at that level. Each model consists of 2 states of network traffic behavior comprising a bursty state representing heavy traffic conditions and an idle state representing light traffic conditions. A transition window defines the upper time interval for the receipt of packets in the bursty state and the lower time interval for the receipt of packets in the idle state. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model.

대표청구항 ▼

What is claimed is: 1. In a computer-implemented system comprising a multilevel model for tracking dynamic changes in asynchronous data transfer that is bursty and self similar, said model comprising (a) a Markov modulated Poisson process (MMPP) algorithm to analyze network traffic at a first level

What is claimed is: 1. In a computer-implemented system comprising a multilevel model for tracking dynamic changes in asynchronous data transfer that is bursty and self similar, said model comprising (a) a Markov modulated Poisson process (MMPP) algorithm to analyze network traffic at a first level representing a given time scale; (b) replication means for using the MMPP algorithm to analyze the network traffic on at least one level representing a time scale that differs from the time scale on the first level, and (c) a transition window for determining the transition from one time scale to another. 2. The system according to claim 1 wherein the multilevel model consists of two states of network traffic behavior comprising a bursty state representing heavy traffic conditions and an idle state representing light traffic conditions. 3. The system according to claim 2 wherein the transition window is represented as [λBmax, λImin] and wherein λBmax is the upper boundary for heavy traffic arrival in the bursty state and λImin is the lower boundary for light traffic arrival in the idle state. 4. The system according to claim 1 wherein the model includes a default to a single state model as the mean inter-arrival times of packets in the bursty state and in the idle state become approximately equal. 5. The system according to claim 1 further including the capability of using a bottom-up analysis of traffic at each level to determine the traffic pattern at the next time scale. 6. The system according to claim 5 wherein the analysis comprises the ability to generate a trace of the traffic pattern for a given time scale. 7. The system according to claim 6 wherein the analysis of the generated trace at the given time scale can generate a trace of the next higher time scale pattern. 8. The system according to claim 1 wherein the model has the capability of adapting to changes in traffic patterns within a given time scale. 9. The system according to claim 3 wherein the model has the further ability to dynamically adjust the parameters of the transition window in response to an incoming traffic load. 10. The system according to claim 9 wherein the transition window is useful for managing the process of synchronization of low-latency, high-bandwidth networks. 11. A computer implemented method for a multi-level tracking of dynamic changes in asynchronous network traffic in a computer network, the method comprising the steps of: a) generating a trace of a low-level traffic pattern; b) analyzing the low-level pattern to generate a trace representing an approximation of the next time scale pattern; c) repeating the trace generation and analysis for each successive higher level traffic pattern; and d) using a Markov modulated Poisson process (MMPP) algorithm to generate each model, wherein each model consists of two states of network traffic behavior, wherein the two states of network traffic behavior comprise a bursty state representing the inter-arrival time of data packet during heavy traffic conditions and an idle state representing the inter-arrival time of data packet during light traffic conditions. 12. The method according to claim 11 wherein if the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. 13. The method according to claim 11 further including a bottom-up analysis step to determine the different time scales. 14. The method according to claim 13 wherein the analysis step comprises generating a trace of a traffic pattern for a given time scale. 15. The method according to claim 11 including adapting to changes in traffic pattern within a given time scale. 16. The method according to claim 11 wherein the transition window is represented as [λBmax, λImin] and wherein λBmax is the upper boundary for heavy traffic arrival in the bursty state and λImin is the lower boundary for light traffic arrival in the idle state. 17. The method according to claim 16 including the step of dynamically adjusting the parameters of the transition window in response to the incoming traffic load. 18. The method according to claim 17 including the use of the transition window for managing the process of synchronization of low-latency, high-bandwidth networks. 19. An article comprising a computer-readable medium which stores computer-executable instructions for processing traffic flow patterns associated with network data transmission, when executed by a computer, the instructions causing the computer to: (a) receive traffic pattern data associated with the network transmission of data packets relating to the inter-arrival time of the data packets during a time level when the traffic is self-similar; (b) apply a Markov modulated Poisson process (MMPP) algorithm to the received pattern; and (c) repeat the steps (a) and (b) one or more additional times at different time levels. 20. The article according to claim 19 wherein the instructions cause a trace to be generated at the first time level to represent the traffic at that level, and to be analyzed to generate the trace of the pattern at the next time level. 21. The article according to claim 20 wherein the instructions utilize a transition window to predict traffic flow as being bursty or as idle. 22. The article according to claim 21 wherein the transition window is represented as [λBmax, λImin] and wherein λBmax is the upper boundary for heavy traffic arrival in the bursty state and λImin is the lower boundary for light traffic arrival in the idle state.