the slow-start algorithm begins with sending one segment, it takes many round-trip times to reach the Efficient implementation of TCP for the Internet optimal operating point, thus resulting in poor requires a precise determination ofcongestion window utilization of the available bandwidth for short by the source TCP agent. This paper proposes a fuzzy transfers which are small compared to the bandwidthimplementation of TCP (Fuzzy TCP), instead of delay product of the path. Second, since a TCP sender current slow-start /congestion avoidance approachfor has no knowledge about the capacity of the available efficiently determining size ofthe congestion window at resources on the networks and uses default parameters each time a new acknowledgement receives. The at the beginning of transmission, the exponential scheme uses the current size ofcongestion window and growth of the congestion window often misleads the the slow-start threshold (ssthresh) as well as the last sender to send too many packets too quickly, thus and previous round trip times. The proposed fuzzy causing a severe buffer overflow at the bottleneck link. controller updates the size of congestion window using This buffer overflow results in multiple packet loss a fuzzy system. The approach is applied to the source from a window of data. When multiple packets in the agent and the destination TCP remains unchanged. same window are lost, only one of the packet losses may be recovered by each fast retransmit; the rest are
Simulation of dynamic complex systems-specifically, those comprised of large numbers of components with stochastic behaviors-for the purpose of probabilistic risk assessment faces challenges in every aspect of the problem. Scenario generation confronts many impediments, one being the problem of handling the large number of scenarios without compromising completeness. Probability estimation and consequence determination processes must also be performed under real world constraints on time and resources. In the approach outlined in this paper, hierarchical planning is utilized to generate a relatively small but complete group of risk scenarios to represent the unsafe behaviors of the system. Multi-level scheduling makes the probability estimation and consequence determination processes more efficient and affordable. The scenario generation and scheduling processes both benefit from an updating process that takes place after a number of simulation runs by fine-tuning the scheduler's level adjustment parameters and refining the planner's high level system model.
Simulation may be the most practical way to assess the risk of systems with complex behaviors such as those that include hardware, software and human elements. However, since under normal design conditions human-designed systems generally perform in familiar and expected ways, a typical simulation will frequently lead to known and anticipated results. As such, the simulation program wastes a lot of time on familiar results without generating new knowledge about the system’s vulnerabilities. In order to increase our knowledge of risk, it would be preferable to push the system toward its limits to test the system’s ability to handle more difficult situations. Such an approach can help system designers to better understand risky situations and close the vulnerability gaps in their design. The primary objective of this study is to develop a risk simulation Planner (SimpraPlan) which generates scenarios that can explore the system’s vulnerabilities and offer a superior assessment of the risks involved. The Planner uses high level engineering knowledge (including the functional requirements and physical structure of the system) to generate scenarios that can exploit the system’s vulnerabilities. In this paper, the scenario generation process is explained in detail and scenarios generated by the SimpraPlan are compared with those generated by classical approaches to risk assessment.
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