Research on Time-of-day Internet Access Management by Quota-based Priority Control

There exists abusive and unfair Internet access during peak hours by users of a free-of-charge or flat-rate network even under a quota-based priority control (QPC). To effectively managing the Internet access over time based on QPC, this thesis studies and analyzes two classes of schemes: time-of-day pricing (TDP) and quota scheduling (QS). TDP is an incentive control method, where users can flexibly allocate the daily quota by virtual price. QS allocates the daily quota to individual time periods to directly and forcedly limit the maximum volume usage of each user during peak hours.
The TDP design takes advantage of the empirical data to characterize user demand and quota-allocation behavior with respect to time and pricing. In-depth analyses of empirical data reveal distinctive behavior patterns of myopic and prudent quota allocations over time and both patterns indicate high preference for peak-hour access. The user models adopt general utility functions and capture how pricing affects user behavior as prudent or myopic. Preference parameters of users’ utility over time are then estimated by collecting easily measurable user volumes. The TDP design problem is then formulated and solved as a Stackelberg game. Numerical results shows that the TDP design leads to significant improvements in peak-hour abuse and fairness, peak shaving and load balancing over pure QPC. The methodology of TDP requires only two simple and short-period data collections from an operational network. One is from the network with QPC; the other is from the network without quota control. Results demonstrate the effectiveness of TDP design methodology when applied to Internet access environments with frequent changes.
Two QS schemes, load balancing-based quota scheduling (LB-QS) and peak shaving-based quota scheduling (PS-QS), are proposed. LB-QS intends to equalize average traffic over time by proportional quota allocation to time periods of control. There is no empirical data of traffic usage needed for the LB-QS design. PS-QS aims at reducing total traffic of peak hours by utilizing an aggregate empirical data-based user model. This model needs the measurement data collected from a network with QPC to approximate user quota allocation behavior over time. Both QS schemes are compulsive control measures.
Performances of TDP and QS are evaluated and compared over the empirical data of a 5000-user network. Results demonstrate TDP significantly outperforms both LB-QS and PS-QS in regulating the Internet access over time. This is because TDP exploits user behavior modeling and pricing to induce user behavior over time, avoiding congestion at the time of quota renewal. As for calculation complexity, the TDP design needs to solve an optimization problem, while the QS design only requires simple mathematical operations. However, the CPU time for TDP calculation takes about 1 minute. Recommendations are given for selecting an effective Internet access scheme based on data availability and traffic pattern over time.
We further study how to manage the user traffic over a profitable and multi-service network by designing pricing and bandwidth allocation at the same time. Although pricing and bandwidth allocation of individual services are two important and coupled resource management functions, they are treated separately in most of the literature. In this thesis, we design for a service provider an integrated pricing and bandwidth allocation (IPBA) scheme for a popular network service, where each user is guaranteed with a minimum bandwidth for transmission according to the service class subscribed. Revenue maximization of service provisioning is the service provider’s objective. The design problem is formulated as a nonlinear programming problem. It adopts an empirical user demand model, where a user’s usage time for a service class is a function of prices. Constrained by the total bandwidth limitation, the revenue-maximizing price design induces user demands for individual classes, which in turn determines an optimal allocation of bandwidth. Analyses of the IPBA solution demonstrate that the price increases with traffic intensity while the bandwidth allocation is insensitive to the variation. Results also reveal that when users’ demand for a class is relatively sensitive to the price of other class. Over the same network capacity, the total revenue of offering more than one service classes is higher than that of offering only single service class.