Bottleneck Bandwidth And Round-Trip Propagation Time Algorithm Using TFRC Protocol By Artificial Intelligence Algorithm

Abstract

E-government services will play an outstanding role in the next generation of the internet. With growing real-time requirements,
internet generation has to offer Quality of Service (QoS) for various types of real-time streaming services. When the bandwidth required
exceeds the to be had community resources, community paths can get congested, which leads to a postponement in packet transport
and packet loss. This situation leads to the design of new techniques for congestion avoidance and control. One of the popular and
suitable congestion management mechanisms which are useful in transmitting multimedia applications in the transport layer is TCP
Friendly Rate Control Protocol (TFRC). The fact that bridging the existing e-government security service gap between developed and
developing countries. In addition, revealed that system security is one of the major barriers that prevent citizens from adopting EGovernment
services. Therefore, the network reliable and secure communication channel among government agencies and the entire
system, and the Gap related to system quality were found to the security issues among the strongest barriers to hindering E-Government
performance. The aim of this study is to examine some of the emerging issues surrounding the Security detection of E-Government
Systems in Jordan. Furthermore, the main objective of this research enhances the speed up of training the TFRC protocol through the
receiver to group losses and marks that occurred during the same round-trip time. In this paper, we increase stop-to-stop probing
strategies which could degree bottleneck bandwidth alongside arbitrary, Accurate measurement of network bandwidth is crucial for
network management applications as well as flexible Internet applications and protocols which actively manage and dynamically adapt
to changing utilization of network resources. The constructed dataset physically resides in the (dataset) file stored on the data computer,
but the configuration files for the dataset reside within a directory for software programs. A software program contains a set of
configuration files that construct a dataset (including its extended dimensions) for a specific analysis purpose. It's not easily possible
with standard testing approaches such as JUnit, because a node relies on the KNIME Core framework that handles tasks such as
execution and data handling. Therefore, a node must be tested within a running KNIME instance.