A semi-markov model for the performance of multiple-bus systems
IEEE Transactions on Computers
High-performance operating system primitives for robotics and real-time control systems
ACM Transactions on Computer Systems (TOCS)
Multiprocessor Organization—a Survey
ACM Computing Surveys (CSUR)
Approximate Methods for Analyzing Queueing Network Models of Computing Systems
ACM Computing Surveys (CSUR)
Computational algorithms for product form queueing networks
Communications of the ACM
Computational algorithms for closed queueing networks with exponential servers
Communications of the ACM
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Computer Architecture and Parallel Processing
Computer Architecture and Parallel Processing
A study in memory interference models (performance evaluation, semi-markov process, markov chain, multiprocessor, multiple-bus system)
Performance Modeling of Hierarchical Crossbar-Based Multicomputer Systems
IEEE Transactions on Computers
Modeling and verification of a class of real-time systems by the use of high level petri nets
Journal of Systems and Software
| Hi-index | 14.98 |
A general discrete time semi-Markov model is developed to investigate the effects of task priorities on the system performance of a multiprocessor system with crossbar interconnection network. The number of priority levels associated with the tasks in the system, connection times of different priority level requests, interrequest time, number of processing elements, and the number of shared resources are the parameters involved in estimation of the performance of the system. The bandwidth, queue length at a memory, waiting time for requests at different priority levels, and processor utilization are the performance measures quantified from the analysis. The results reveal the advantage received by the tasks at higher priority levels and the starvation experienced by the lower priority tasks. This information should be useful in the real-time task scheduling, load balancing, and performance optimization. The results obtained are validated with simulation.