The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

ANFIS: adaptive-network-based fuzzy inference system

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights. >

/pdf/training-feedforward-networks-with-the-marquardt-algorithm-e1r0710f99.pdf

Training feedforward networks with the Marquardt algorithm

This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A central and vital operation performed in the Kalman filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. The UKF addresses this problem by using a deterministic sampling approach. The state distribution is again approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and when propagated through the true nonlinear system, captures the posterior mean and covariance accurately to the 3rd order (Taylor series expansion) for any nonlinearity. The EKF in contrast, only achieves first-order accuracy. Remarkably, the computational complexity of the UKF is the same order as that of the EKF. Julier and Uhlman demonstrated the substantial performance gains of the UKF in the context of state-estimation for nonlinear control. Machine learning problems were not considered. We extend the use of the UKF to a broader class of nonlinear estimation problems, including nonlinear system identification, training of neural networks, and dual estimation problems. In this paper, the algorithms are further developed and illustrated with a number of additional examples.

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The unscented Kalman filter for nonlinear estimation

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a two-tier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

/pdf/adaptive-control-of-virtualized-resources-in-utility-1ps3xw5r81.pdf

Adaptive control of virtualized resources in utility computing environments

Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.

/pdf/automated-control-of-multiple-virtualized-resources-4y2y4mu57k.pdf

Automated control of multiple virtualized resources

A large fraction of recent work in artificial neural nets uses multilayer perceptrons trained with the back-propagation algorithm described by Rumelhart et. al. This algorithm converges slowly for large or complex problems such as speech recognition, where thousands of iterations may be needed for convergence even with small data sets. In this paper, we show that training multilayer perceptrons is an identification problem for a nonlinear dynamic system which can be solved using the Extended Kalman Algorithm. Although computationally complex, the Kalman algorithm usually converges in a few iterations. We describe the algorithm and compare it with back-propagation using two-dimensional examples.

/pdf/training-multilayer-perceptrons-with-the-extended-kalman-twwc5qmycp.pdf

Training Multilayer Perceptrons with the Extended Kalman Algorithm

Server consolidation has become an integral part of IT planning to reduce cost and improve efficiency in today’s enterprise data centers. The advent of virtualization allows consolidation of multiple applications into virtual containers hosted on a single or multiple physical servers. However, this poses new challenges, including choosing the right virtualization technology and consolidation configuration for a particular set of applications. In this paper, we evaluate two representative virtualization technologies, Xen and OpenVZ, in various configurations. We consolidate one or more multi-tiered systems onto one or two nodes and drive the system with an auction workload called RUBiS. We compare both technologies with a base system in terms of application performance, resource consumption, scalability, low-level system metrics like cache misses and virtualization-specific metrics like Domain-0 consumption in Xen. Our experiments indicate that the average response time can increase by over 400% in Xen and only a modest 100% in OpenVZ as the number of application instances grows from one to four. This large discrepancy is caused by the higher virtualization overhead in Xen, which is likely due to higher L2 cache misses and misses per instruction. A similar trend is observed in CPU consumptions of virtual containers. We present an overhead analysis with kernelsymbol-specific information generated by Oprofile.

http://www.hpl.hp.com/techreports/2007/HPL-2007-59R1.pdf

Performance Evaluation of Virtualization Technologies for Server Consolidation

A system and method that allow a subscriber to have incoming telephone calls automatically screened and directed is described The system allows a subscriber to automatically manage his incoming communications in a way that is easy to control and which requires a minimum of unnecessary interruptions The system includes a graphical user interface (GUI) which is accessible through the subscriber's personal digital assistant (200), a Network Interface (304), a service control module (306), and a database (308) The system and method allow a subscriber to have all of his incoming telephone calls screened in order to identify those that are of the high importance to the subscriber The subscriber controls this system by user-friendly interfaces to a name and telephone number database and an appointment calendar database By entering schedule information into an appointment calendar (including times and locations of meetings and other events) and by entering client's information into the name and telephone number database, the subscriber indicates how to locate the subscriber so that important calls will reach him immediately These databases also tell the system which clients are of high priority to the subscriber so that lower priority calls can be directed to a voice mail system for access at the subscriber's convenience or routed to an attendant for action

Sharad Singhal

Papers

Adaptive control of virtualized resources in utility computing environments

Automated control of multiple virtualized resources

Training Multilayer Perceptrons with the Extended Kalman Algorithm

Performance Evaluation of Virtualization Technologies for Server Consolidation

A system and method for automatically screening and directing incoming calls