Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Approximate Methods for Analyzing Queueing Network Models of Computing Systems
ACM Computing Surveys (CSUR)
Linearizer: a heuristic algorithm for queueing network models of computing systems
Communications of the ACM
A comparison of numerical techniques in Markov modeling
Communications of the ACM
Memory management and response time
Communications of the ACM
Hybrid simulation models of computer systems
Communications of the ACM
A decomposition solution to the queueing network model of the centralized DBMS with static locking
SIGMETRICS '83 Proceedings of the 1983 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Efficient approximation for models of multiprogramming with shared domains
SIGMETRICS '84 Proceedings of the 1984 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Analysis of Queueing Network Models with population size constraints and delayed blocked customers
SIGMETRICS '84 Proceedings of the 1984 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Multiple class memory constrained queueing networks
SIGMETRICS '82 Proceedings of the 1982 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Fast approximate solution of multiprogramming models
SIGMETRICS '82 Proceedings of the 1982 ACM SIGMETRICS conference on Measurement and modeling of computer systems
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Hierarchical modeling has been applied with great success in analyzing queueing network models of computer systems, where a direct solution is not possible. A primary example is a memory-constrained timesharing (MCT) system. In a typical two-level model, the analysis of the higher level model is carried out using performance parameters, which are obtained by analyzing the lower-level model. The higher-level model can be usually represented by a multi-dimensional Markov Chain (MC), which is generally very expensive to solve due to the large number of its states. Also the transition probabilities among the states of the MC cannot be obtained or are difficult to obtain in most cases (e.g., models for concurrency control performance). Approximate (iterative) solution methods have been adopted to alleviate the cost of solving linear equations, but the accuracy of such solutions is less predictable than decomposition. In this paper, we discuss the use of simulation for solving the higher level model. This method termed hierarchical simulation is applied to the solution of MCT systems. The accuracy of this technique is checked against direct simulation results for a set of test cases reported in the literature. We then compare the cost of hierarchical simulation against that of direct simulation and comment on its applicability.