results for au:Casale_G in:cs
Integrating a product of linear forms over the unit simplex can be done in polynomial time if the number of variables n is fixed (V. Baldoni et al., 2011). In this note, we highlight that this problem is equivalent to obtaining the normalizing constant of state probabilities for a popular class of Markov processes used in queueing network theory. In light of this equivalence, we survey existing computational algorithms developed in queueing theory that can be used for exact integration. For example, under some regularity conditions, queueing theory algorithms can exactly integrate a product of linear forms of total degree N by solving N systems of linear equations.
Jun 22 2016 cs.DC
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Feb 19 2009 cs.PF
We propose a new exact solution algorithm for closed multiclass product-form queueing networks that is several orders of magnitude faster and less memory consuming than established methods for multiclass models, such as the Mean Value Analysis (MVA) algorithm. The technique is an important generalization of the recently proposed Method of Moments (MoM) which, differently from MVA, recursively computes higher-order moments of queue-lengths instead of mean values. The main contribution of this paper is to prove that the information used in the MoM recursion can be increased by considering multiple recursive branches that evaluate models with different number of queues. This reformulation allows to formulate a simpler matrix difference equation which leads to large computational savings with respect to the original MoM recursion. Computational analysis shows several cases where the proposed algorithm is between 1,000 and 10,000 times faster and less memory consuming than the original MoM, thus extending the range of multiclass models where exact solutions are feasible.