Autonomic load-testing framework
Proceedings of the 8th ACM international conference on Autonomic computing
Mitigating DoS Attacks Using Performance Model-Driven Adaptive Algorithms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Applications of queueing network models to computer system performance prediction typically involve the computation of their equilibrium solution. When numerous alternative systems are to be examined and the numbers of devices and customers are large, however, the expense of computing the exact solutions may not be warranted by the accuracy required. In such situations, it is desirable to be able to obtain bounds on the system solution with very little computation. Asymptotic bound analysis (ABA) is one technique for obtaining such bounds. In this paper, we introduce another bounding technique, called balanced job bounds (BJB), which is based on the analysis of systems in which all devices are equally utilized. These bounds are tighter than ABA bounds in many cases, but they are based on more restrictive assumptions (namely, those that lead to separable queueing network models).