Modelling a multiprocessor system with preemptive priorities
Management Science
A review of L=&lgr;W and extensions
Queueing Systems: Theory and Applications
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
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
Priority Mechanisms for OLTP and Transactional Web Applications
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
How many servers are best in a dual-priority M/PH/k system?
Performance Evaluation
Stochastic analysis of multiserver systems
ACM SIGMETRICS Performance Evaluation Review
Stationary delays for a two-class priority queue with impatient customers
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Time-dependent performance analysis of a discrete-time priority queue
Performance Evaluation
Waiting and sojourn times in a multi-server queue with mixed priorities
Queueing Systems: Theory and Applications
Towards more effective utilization of computer systems
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Live-chat agent assignments to heterogeneous e-customers under imperfect classification
ACM Transactions on Management Information Systems (TMIS)
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We present the first near-exact analysis of an M/PH/k queue with m 2 preemptive-resume priority classes. Our analysis introduces a new technique, which we refer to as Recursive Dimensionality Reduction (RDR). The key idea in RDR is that the m-dimensionally infinite Markov chain, representing the m class state space, is recursively reduced to a 1-dimensionally infinite Markov chain, that is easily and quickly solved. RDR involves no truncation and results in only small inaccuracy when compared with simulation, for a wide range of loads and variability in the job size distribution.Our analytic methods are then used to derive insights on how multi-server systems with prioritization compare with their single server counterparts with respect to response time. Multi-server systems are also compared with single server systems with respect to the effect of different prioritization schemes--"smart" prioritization (giving priority to the smaller jobs) versus "stupid" prioritization (giving priority to the larger jobs). We also study the effect of approximating m class performance by collapsing the m classes into just two classes.