Revenue Maximization in Resource Allocation : Applications in Wireless Communication Networks
Sammanfattning: Revenue maximization for network operators is considered as a criterion for resource allocation in wireless cellular networks. A business model encompassing service level agreements between network operators and service providers is presented. Admission control, through price model aware admission policing and service level control, is critical for the provisioning of useful services over a general purpose wireless network. A technical solution consisting of a fast resource scheduler taking into account service requirements and wireless channel properties, a service level controller that provides the scheduler with a reasonable load, and an admission policy to uphold the service level agreements and maximize revenue, is presented.Two different types of service level controllers are presented and implemented. One is based on a scalar PID controller, that adjusts the admitted data rates for all active clients. The other one is obtained with linear programming methods, that optimally assign data rates to clients, given their channel qualities and price models.Two new scheduling criteria, and algorithms based on them, are presented and evaluated in a simulated wireless environment. One is based on a quadratic criterion, and is implemented through approximative algorithms, encompassing a search based algorithm and two different linearizations of the criterion. The second one is based on statistical measures of the service rates and channel states, and is implemented as an approximation of the joint probability of achieving the delay limits while utilizing the available resources efficiently.Two scheduling algorithms, one based on each criterion, are tested in combination with each of the service level controllers, and evaluated in terms of throughput, delay, and computational complexity, using a target test system. Results show that both schedulers can, when feasible, meet explicit throughput and delay requirements, while at the same time allowing the service level controller to maximize revenue by allocating the surplus resources to less demanding services.
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