Bounds for performance measures of token rings
IEEE/ACM Transactions on Networking (TON)
Performance modeling of elastic traffic in overload
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Analysis of customers' impatience in queues with server vacations
Queueing Systems: Theory and Applications
Analysis of a dynamic assignment of impatient customers to parallel queues
Queueing Systems: Theory and Applications
The M/M/1 queue with synchronized abandonments
Queueing Systems: Theory and Applications
Analysis of a queueing system with impatient customers and working vacations
Proceedings of the 6th International Conference on Queueing Theory and Network Applications
Queues in tandem with customer deadlines and retrials
Queueing Systems: Theory and Applications
Measurement and modeling of paging channel overloads on a cellular network
Computer Networks: The International Journal of Computer and Telecommunications Networking
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A system is operating as an M/M/∞ queue. However, when it becomes empty, it is assigned to perform another task, the duration U of which is random. Customers arriving while the system is unavailable for service (i.e., occupied with a U-task) become impatient: Each individual activates an “impatience timer” having random duration T such that if the system does not become available by the time the timer expires, the customer leaves the system never to return. When the system completes a U-task and there are waiting customers, each one is taken immediately into service. We analyze both multiple and single U-task scenarios and consider both exponentially and generally distributed task and impatience times. We derive the (partial) probability generating functions of the number of customers present when the system is occupied with a U-task as well as when it acts as an M/M/∞ queue and we obtain explicit expressions for the corresponding mean queue sizes. We further calculate the mean length of a busy period, the mean cycle time, and the quality of service measure: proportion of customers being served.