Optimal scheduling policies for a class of queues with customer deadlines to the beginning of service
- 1 October 1988
- journal article
- Published by Association for Computing Machinery (ACM) in Journal of the ACM
- Vol. 35 (4) , 832-844
- https://doi.org/10.1145/48014.48019
Abstract
Many problems can be modeled as single-server queues with impatient customers. An example is that of the transmission of voice packets over a packet-switched network. If the voice packets do not reach their destination within a certain time interval of their transmission, they are useless to the receiver and considered lost. It is therefore desirable to schedule the customers such that the fraction of customers served within their respective deadlines is maximized. For this measure of performance, it is shown that the shortest time to extinction (STE) policy is optimal for a class of continuous and discrete time nonpreemptive M/G/1 queues that do not allow unforced idle times. When unforced idle times are allowed, the best policies belong to the class of shortest time to extinction with inserted idle time (STEI) policies. An STEI policy requires that the customer closest to his or her deadline be scheduled whenever it schedules a customer. It also has the choice of inserting idle times while the queue is nonempty. It is also shown that the STE policy is optimal for the discrete time G/D/1 queue where all customers receive one unit of service. The paper concludes with a comparison of the expected customer loss using an STE policy with that of the first-come, first-served (FCFS) scheduling policy for one specific queue.Keywords
This publication has 6 references indexed in Scilit:
- Performance Requirements for Integrated Voice/Data NetworksIEEE Journal on Selected Areas in Communications, 1983
- Stochastic Scheduling with Release Dates and Due DatesOperations Research, 1983
- A Combinatorial Approach to Dynamic Scheduling ProblemsOperations Research, 1978
- Digital speech networksProceedings of the IEEE, 1977
- Optimal Issuing Policies for Perishable InventoryManagement Science, 1972
- An n Job, One Machine Sequencing Algorithm for Minimizing the Number of Late JobsManagement Science, 1968