Cone Schedules for Processing Systems in Fluctuating Environments
  We consider a generalized processing system having several queues, where the available service rate combinations are fluctuating over time due to reliability and availability variations. The objective is to allocate the available resources, and corresponding service rates, in response to both workload and service capacity considerations, in order to maintain the long term stability of the system. The service configurations are completely arbitrary, including negative service rates which represent forwarding and service-induced cross traffic. We employ a trace-based trajectory asymptotic technique, which requires minimal assumptions about the arrival dynamics of the system.   We prove that cone schedules, which leverage the geometry of the queueing dynamics, maximize the system throughput for a broad class of processing systems, even under adversarial arrival processes. We study the impact of fluctuating service availability, where resources are available only some of the time, and the schedule must dynamically respond to the changing available service rates, establishing both the capacity of such systems and the class of schedules which will stabilize the system at full capacity. The rich geometry of the system dynamics leads to important insights for stability, performance and scalability, and substantially generalizes previous findings.   The processing system studied here models a broad variety of computer, communication and service networks, including varying channel conditions and cross-traffic in wireless networking, and call centers with fluctuating capacity. The findings have implications for bandwidth and processor allocation in communication networks and workforce scheduling in congested call centers. 
Kevin Ross, Nicholas Bambos, and George Michailidis
cs.NI cs.SY math.OC