We study an ordinary differential equation (ODE) arising as the many-server heavy-traffic fluid limit of a sequence of overloaded Markovian queueing models with two customer classes and two service pools. The system, known as the X model in the call-center literature, operates under 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 unanticipated overload. Each pool serves only its own class until a threshold is exceeded; then one-way sharing is activated with all customer-server assignments then driving the two queues toward a fixed ratio. For large systems, that fixed ratio is achieved approximately. The ODE describes system performance during an overload. The control is driven by a queue-difference stochastic process, which operates in a faster time scale than the queueing processes themselves, thus achieving a time-dependent steady state instantaneously in the limit. As a result, for the ODE, the driving process is replaced by its long-run average behavior at each instant of time; i.e., the ODE involves a heavy-traffic averaging principle (AP).1. Introduction. We study an ordinary differential equation (ODE) that arises as the many-server heavy-traffic (MS-HT) fluid limit of a sequence of overloaded Markovian X queueing models under the fixed-queue-ratiowith-thresholds (FQR-T) control. The ODE is especially interesting, because it involves a heavy-traffic averaging principle (AP).The system consists of two large service pools that are designed to operate independently, but can help each other when one of the pools, or both, encounter an unexpected overload, manifested by an instantaneous shift in the arrival rates. We assume that the time that the arrival rates shift and the values of the new arrival rates are not known when the overload
We consider how two networked large-scale service systems that normally operate separately, such as call centers, can help each other when one encounters an unexpected overload and is unable to immediately increase its own staffing. Our proposed control activates serving some customers from the other system when a ratio of the two queue lengths (numbers of waiting customers) exceeds a threshold. Two thresholds, one for each direction of sharing, automatically detect the overload condition and prevent undesired sharing under normal loads. After a threshold has been exceeded, the control aims to keep the ratio of the two queue lengths at a specified value. To gain insight, we introduce an idealized stochastic model with two customer classes and two associated service pools containing large numbers of agents. To set the important queue-ratio parameters, we consider an approximating deterministic fluid model. We determine queue-ratio parameters that minimize convex costs for this fluid model. We perform simulation experiments to show that the control is effective for the original stochastic model. Indeed, the simulations show that the proposed queue-ratio control with thresholds outperforms the optimal fixed partition of the servers given known fixed arrival rates during the overload, even though the proposed control does not use information about the arrival rates.service systems, call centers, overload controls, queue-ratio routing, many-server queues, deterministic fluid models
In previous papers we developed a deterministic fluid approximation 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 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. The control aims to keep the two queues at a pre-specified fixed ratio. We supported the fluid approximation by establishing a functional weak law of large numbers (FWLLN) involving a stochastic averaging principle. In this paper we develop a refined diffusion approximation for the same model based on a many-server heavy-traffic functional central limit theorem (FCLT).Since we are considering the case when sharing is taking place with class-1 customers receiving help, we essentially need only consider r 1,2 , which we henceforth denote by r, i.e., r ≡ r 1,2 .
We study an ordinary differential equation (ODE) arising as the many-server heavy-traffic fluid limit of a sequence of overloaded Markovian queueing models with two customer classes and two service pools. The system, known as the X model in the call-center literature, operates under 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 unanticipated overload. Each pool serves only its own class until a threshold is exceeded; then one-way sharing is activated with all customer-server assignments then driving the two queues toward a fixed ratio. For large systems, that fixed ratio is achieved approximately. The ODE describes system performance during an overload. The control is driven by a queue-difference stochastic process, which operates in a faster time scale than the queueing processes themselves, thus achieving a time-dependent steady state instantaneously in the limit. As a result, for the ODE, the driving process is replaced by its long-run average behavior at each instant of time; i.e., the ODE involves a heavy-traffic averaging principle (AP).1. Introduction. We study an ordinary differential equation (ODE) that arises as the many-server heavy-traffic (MS-HT) fluid limit of a sequence of overloaded Markovian X queueing models under the fixed-queue-ratiowith-thresholds (FQR-T) control. The ODE is especially interesting, because it involves a heavy-traffic averaging principle (AP).The system consists of two large service pools that are designed to operate independently, but can help each other when one of the pools, or both, encounter an unexpected overload, manifested by an instantaneous shift in the arrival rates. We assume that the time that the arrival rates shift and the values of the new arrival rates are not known when the overload
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-withthresholds (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.1. Introduction. In this paper we prove that the deterministic fluid approximation for the overloaded X call-center model, suggested in Perry and Whitt [36] and analyzed in Perry and Whitt [37], arises as the manyserver heavy-traffic fluid limit of a properly scaled sequence of overloaded Markovian X models under the fixed-queue-ratio-with-thresholds (FQR-T) control. (A list of all the acronyms appears in §F in the appendix.) The X model has two classes of customers and two service pools, one for each class, but with both pools capable of handling customers from either class. The service-time distributions depend on both the class and the pool. The FQR-T control was suggested in Perry and Whitt [35] as a way to automatically initiate sharing (i.e., sending customers from one class to the other service pool) when the system encounters an unexpected overload, while ensuring that sharing does not take place when it is not needed.
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