The pointwise stationary approximation for M1/M1/s
Management Science
Queueing Systems: Theory and Applications
Scheduling Flexible Servers with Convex Delay Costs in Many-Server Service Systems
Manufacturing & Service Operations Management
Queue-and-Idleness-Ratio Controls in Many-Server Service Systems
Mathematics of Operations Research
Responding to Unexpected Overloads in Large-Scale Service Systems
Management Science
A Fluid Approximation for Service Systems Responding to Unexpected Overloads
Operations Research
Diffusion approximation for an overloaded X model via a stochastic averaging principle
Queueing Systems: Theory and Applications
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We prove a many-server heavy-traffic fluid limit for an overloaded Markovian queueing system having two customer classes and two service pools, known in the call-center literature as the X model. The system uses the fixed-queue-ratio-with-thresholds FQR-T control, which we proposed in a recent paper as a way for one service system to help another in face of an unexpected overload. Under FQR-T, customers are served by their own service pool until a threshold is exceeded. Then, one-way sharing is activated with customers from one class allowed to be served in both pools. After the control is activated, it aims to keep the two queues at a prespecified fixed ratio. For large systems that fixed ratio is achieved approximately. For the fluid limit, or FWLLN functional weak law of large numbers, we consider a sequence of properly scaled X models in overload operating under FQR-T. Our proof of the FWLLN follows the compactness approach, i.e., we show that the sequence of scaled processes is tight and then show that all converging subsequences have the specified limit. The characterization step is complicated because the queue-difference processes, which determine the customer-server assignments, need to be considered without spatial scaling. Asymptotically, these queue-difference processes operate on a faster time scale than the fluid-scaled processes. In the limit, because of a separation of time scales, the driving processes converge to a time-dependent steady state or local average of a time-varying fast-time-scale process FTSP. This averaging principle allows us to replace the driving processes with the long-run average behavior of the FTSP.