Algorithmic Aspects of Bandwidth Trading

We study algorithmic problems that are motivated by bandwidth trading in next generation networks. Typically, bandwidth trading involves sellers (e.g., network operators) interested in selling bandwidth pipes that offer to buyers a guaranteed level of service for a specified time interval. The buyers (e.g., bandwidth brokers) are looking to procure bandwidth pipes to satisfy the reservation requests of end-users (e.g., Internet subscribers). Depending on what is available in the bandwidth exchange, the goal of a buyer is to either spend the least amount of money to satisfy all the reservations made by its customers, or to maximize its revenue from whatever reservations can be satisfied. We model the above as a real-time non-preemptive scheduling problem in which machine types correspond to bandwidth pipes and jobs correspond to the end-user reservation requests. Each job specifies a time interval during which it must be processed and a set of machine types on which it can be executed. If necessary, multiple machines of a given type may be allocated, but each must be paid for. Finally, each job has a revenue associated with it, which is realized if the job is scheduled on some machine. There are two versions of the problem that we consider. In the cost minimization version, the goal is to minimize the total cost incurred for scheduling all jobs, and in the revenue maximization version the goal is to maximize the revenue of the jobs that are scheduled for processing on a given set of machines. We consider several variants of the problems that arise in practical scenarios, and provide constant factor approximations.

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