A unified load generator based on formal load specification and load transformation

Abstract

Optimizing the performance of existing networks often demands realistic and valid load generation tools. As the network loads observed may vary significantly over time, there is a strong need to develop flexible and extensible load generators which can reproduce these loads accurately. This paper presents a unified methodology for artificial load generating at different interfaces within the protocol hierarchy of computer networks. A unified load generator which is based on a formal load specification technique and a new approach to load modeling combined with load transformation is established. The methodology of constructing the unified load generator is validated by means of implementing a prototype of such a load generator (UniLoG) which is capable of producing network traffic with different characteristics in a very simple and flexible manner. We present the architectural design, some implementation aspects and investigate the limits of applicability of our prototype. Recent performance evaluation studies with UniLoG indicate that this load generator is a valid tool for QoS studies in computer networks, and can indeed produce some predefined traffic accurately.

Comparing the Accuracy of Network Utilization Performance between Real Network and Simulation Model for Local Area Network (LAN)

“…Equation 1and (2) are derived based on logical model that has been designed to meet requirements for heterogeneous network environment. Logical model is derived and formulated in a single service (homogeneous concept) as in Equations (3), (4), (5), (6) and (7). Then, the logical model is derived to the heterogeneous network environment in Equations (8), (9), (10), (11), (12), (13) and (14).…”

“…The heterogeneous environment model is then analyzed and simulated using mathematical techniques. For example, several flowlevel network traffic models have been proposed to describe/stimulate [5][6][7]. These models have been used to study fairness, response times, queue lengths and loss probabilities under different assumptions and using a variety of mathematical techniques.…”