Detection of Fractal Breakdowns by the Holder Filter in the Novel Real-Time Traffic Pattern Detector for the Internet Applications

Abstract

The novel real-time traffic pattern detector (RTPD) proposed identifies the Internet traffic pattern on the fly. Firstly it determines if a time series aggregate is stationary. Secondly it confirms if the aggregate exhibits short-range dependence (SRD) or long-range dependence (LRD). Thirdly it detects if the smooth system operation has suddenly become irregular and chaotic. This detection is achieved by computing the instantaneous value of the Holder exponent that has a (0,1) range to accommodate different degrees fractality. A smooth performance distribution such as a time series may embed a varying fractality at different times due to the system dynamics. If the Holder exponent has wandered outside the (0,1) region, fractal breakdown has occurred. The capability of detecting such breakdowns by a real-time application enables it to avoid sudden failure. This feature is of importance to the reliability of digital ecosystems, which reside on the Internet.