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Abstract: This paper presents and compares the performance of three popular packet rate estimators used in predicting the rate of incoming traffic to a network host. The paper proposes a fourth and novel packet rate estimator. Unlike other estimators, the proposed estimator is cost effective as its computation is simple and is not performed upon the arrival of every incoming packet. We evaluate and compare the quality and performance of all estimators in terms of accuracy, agility, stability, and cost. The performance evaluation is primarily conducted by using discrete-event simulation. Our simulation accounts for constant traffic as well as synthesized bursty traffic with empirical packet sizes. We also implement and measure the actual computational cost of two time-window based estimators. The cost is measured in terms of consumed CPU cycles for packet and bit rate estimations. In addition, we investigate how to optimize the performance of our proposed estimator by examining the impact of tuning its key configurable estimation parameters. [ABSTRACT FROM AUTHOR]

Abstract: This paper implements and evaluates experimentally the performance of a simple packet rate estimator that can be considerably useful in predicting the rate of incoming traffic to a network host. The quality and performance of the estimator are evaluated experimentally in terms of cost, accuracy, agility, and stability. Considerable details and guidance are given on how to implement the estimator in Linux and also how to generate highly fluctuating traffic using publicly available traffic generators. The Linux code of the estimator was written in a way to minimize computational overhead by using shift operations and thus avoiding the CPU expensive operations of multiplication and division. The code is also written to be generic enough so it can be easily implemented in other operation systems. The paper also investigates experimentally the performance impact under two key estimator''s parameters (viz. time-window size and weighting factor). [Copyright &y& Elsevier]