Combined state and parameter estimation for on-line applications,

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1972.Bibliography: leaves 354-361.by Peter S. Maybeck.Ph.D

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Resource management of virtualized servers in data-centres has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multi-tier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done towards adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high intensity workload changes. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark web-site deployed on a prototype Xen-virtualized cluster

Stochastic models, estimation, and control

This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws

Stochastic models

Stochastic Models: Estimation and Control: v. 1

Cost-Function-Based Hypothesis Control Techniques for Multiple Hypothesis Tracking

The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer’s n-scan memory filter, Salmond’s joining filter, and Chen and Liu’s Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides mean track life which is significantly greater than that of the compared techniques using similar numbers of mixture components, and mean track life competitive with that of the compared algorithms for similar mean computation times. Abstract © Elsevie

A New Generalized Residual Multiple Model Adaptive Estimator of Parameters and States

This article develops a modification to the standard Multiple Model Adaptive Estimator (MMAE) which allows the use of a new “generalized residual” in the hypothesis conditional probability calculation. The generalized residual is a linear combination of the traditional Kalman filter residual and the “post-fit” Kalman filter residual which is calculated after measurement incorporation. This new modified MMAE is termed a Generalized Residual Multiple Model Adaptive Estimator (GRMMAE). A derivation is provided for the hypothesis conditional probability formula which the GRMMAE uses to calculate probabilities that each elemental filter contains the correct parameter value.Abstract excerpt © Elsevie

Cost-Function-Based Hypothesis Control Techniques for Multiple Hypothesis Tracking

The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer’s n-scan memory filter, Salmond’s joining filter, and Chen and Liu’s Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides track life performance which is significantly better than the compared techniques using similar numbers of mixture components, and performance competitive with the compared algorithms for similar mean computation times