Modelling a multiprocessor system with preemptive priorities
Management Science
Approximate analysis for heterogeneous multiprocessor systems with priority jobs
Performance Evaluation
On a preemptive Markovian queue with multiple servers and two priority classes
Mathematics of Operations Research
Optimal workload allocation in open networks of multiserver queues
Management Science
On Pooling in Queueing Networks
Management Science
Several Results on the Design of Queueing Systems
Operations Research
The response times of priority classes under preemptive resume in M/G/m queues
SIGMETRICS '84 Proceedings of the 1984 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Queueing Systems: Theory and Applications
Analysis of cycle stealing with switching times and thresholds
Performance Evaluation
Analysis of multi-server systems via dimensionality reduction of markov chains
Analysis of multi-server systems via dimensionality reduction of markov chains
Multi-Server Queueing Systems with Multiple Priority Classes
Queueing Systems: Theory and Applications
Systems with multiple servers under heavy-tailed workloads
Performance Evaluation - Performance 2005
On the price of heterogeneity in parallel systems
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
Generalized QBD processes, spectral expansion and performance modeling applications
Network performance engineering
| Hi-index | 0.00 |
We ask the question, "for minimizing mean response time (sojourn time), which is preferable: one fast server of speed 1, or k slow servers each of speed 1/k?" Our setting is the M/PH/k system with two priority classes of customers, high priority and low priority, where PH is a phase-type distribution. We find that multiple slow servers are often preferable, and we demonstrate exactly how many servers are preferable as a function of the load and service time distribution. In addition, we find that the optimal number of servers with respect to the high priority jobs may be very different from that preferred by low priority jobs, and we characterize these preferences. We also study the optimal number of servers with respect to overall mean response time, averaged over high and low priority jobs. Lastly, we ascertain the effect of the service demand variability of high priority jobs on low priority jobs.