In the Internet of today, there are only very crude controls placed on the amount of network capacity that any one user can consume. All users are expected to slow down when they encounter congestion, but there is little verification that they actually do, and there are no controls that permit a relative allocation of capacity to one user over another. The research in this thesis describes a method to impose a usage limit, or "usage profile" on the behavior of individual users. In particular, this thesis explores the design of usage profiles that allow bursty traffic patterns, as opposed to continuous rate limits. This work describes an effective usage profile algorithm for web traffic which has a very bursty character. The following approach studies the characteristics of web traffic, and introduces the fundamental concepts to establish the necessary framework. Through simulations, it analyzes an existing usage profile, the leaky-bucket scheme for different token rates and different data sets, and points out its limitations in the context of web traffic. Then, it proposes a new usage profile, the Average Rate Control Usage Profile (ARCUP) algorithm, that best regulates web traffic. Several variants of this algorithm are presented throughout. It discusses the characteristics of a good profile in order to facilitate the choice of a specific variant. The selected variant of the ARCUP algorithm is simulated for different target rates and different data sets. The results show that this algorithm will work for any data sets that are heavy-tailed distributed, and for different target rates which represent different usage profiles. This thesis concludes with a summary of findings and suggests possible applications. Acknowledgements I'd like to express my sincere gratitude to professor Dave Clark for his insights, motivation, and encouragement throughout this thesis. I would like to extend my gratitue as well to professor Al Drake who guided me through my dark days here. Finally, I would like to thank my friends who have been instrumental to my success at MIT , in particular Amit Sinha and Eric Brittain. I would like to thank all my professors and advisors who have contributed to my education and success. Finally, my thanks go to those who are dearest to me: my mother, Odilia Nazaire and my father Wilner Elysee for their everlasting love and support.