Modeling and Control of Server-based Systems

When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution

Mixed Climatology, Non-synoptic Phenomena and Downburst Wind Loading of Structures

Modern wind engineering was born in 1961, when Davenport published a paper in which meteorology, micrometeorology, climatology, bluff-body aerodynamics and structural dynamics were embedded within a homogeneous framework of the wind loading of structures called today \u201cDavenport chain\u201d. Idealizing the wind with a synoptic extra-tropical cyclone, this model was so simple and elegant as to become a sort of axiom. Between 1976 and 1977 Gomes and Vickery separated thunderstorm from non-thunderstorm winds, determined their disjoint extreme distributions and derived a mixed model later extended to other Aeolian phenomena; this study, which represents a milestone in mixed climatology, proved the impossibility of labelling a heterogeneous range of events by the generic term \u201cwind\u201d. This paper provides an overview of this matter, with particular regard to the studies conducted at the University of Genova on thunderstorm downbursts

Application of Control Theory to a Commercial Mobile Service Support System

The Mobile Service Support system (MSS), which Ericsson AB develops, handles the setup of new subscribers and services into a mobile network. Experience from deployed systems show that traffic monitoring and control of the system will be crucial for handling overload situations that may occur at sudden traffic surges. In this paper we identify and explore some important control challenges for this type of systems. Further, we present analysis and experiments showing some advantages of proposed solutions. First, we develop a load-dependent server model for the system, which is validated in testbed experiments. Further, we propose a control design based on the model, and a method for estimation of response times and arrival rates. The main contribution of this paper is that we show how control theory methods and analysis can be used for commercial telecom systems. Parts of our results have been implemented in commercial products, validating the strength of our work

Aeroelastic Flight Data Analysis with the Hilbert-Huang Algorithm

This report investigates the utility of the Hilbert Huang transform for the analysis of aeroelastic flight data. It is well known that the classical Hilbert transform can be used for time-frequency analysis of functions or signals. Unfortunately, the Hilbert transform can only be effectively applied to an extremely small class of signals, namely those that are characterized by a single frequency component at any instant in time. The recently-developed Hilbert Huang algorithm addresses the limitations of the classical Hilbert transform through a process known as empirical mode decomposition. Using this approach, the data is filtered into a series of intrinsic mode functions, each of which admits a well-behaved Hilbert transform. In this manner, the Hilbert Huang algorithm affords time-frequency analysis of a large class of signals. This powerful tool has been applied in the analysis of scientific data, structural system identification, mechanical system fault detection, and even image processing. The purpose of this report is to demonstrate the potential applications of the Hilbert Huang algorithm for the analysis of aeroelastic systems, with improvements such as localized online processing. Applications for correlations between system input and output, and amongst output sensors, are discussed to characterize the time-varying amplitude and frequency correlations present in the various components of multiple data channels. Online stability analyses and modal identification are also presented. Examples are given using aeroelastic test data from the F-18 Active Aeroelastic Wing airplane, an Aerostructures Test Wing, and pitch plunge simulation

Coping with Time-Varying Demand When Setting Staffing Requirements for a Service System

We review queueing-theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long-run steady-state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time-dependent performance is captured and staffing requirements can be set. Relatively little modification of straightforward stationary analysis applies in systems where service times are short and the targeted quality of service is high. When service times are moderate and the targeted quality of service is still high, time-lag refinements can improve traditional stationary independent period-by-period and peak-hour approximations. Time-varying infinite-server models help develop refinements, because closed-form expressions exist for their time-dependent behavior. More difficult cases with very long service times and other complicated features, such as end-of-day effects, can often be treated by a modified-offered-load approximation, which is based on an associated infinite-server model. Numerical algorithms and deterministic fluid models are useful when the system is overloaded for an extensive period of time. Our discussion focuses on telephone call centers, but applications to police patrol, banking, and hospital emergency rooms are also mentioned

Control Strategies for Improving Cloud Service Robustness

This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and fire, are unexpected events on larger scale that threatens the integrity of the underlying infrastructure on which an application is deployed.First, the self-adaptive software concept of brownout is extended to replicated cloud applications. By monitoring the performance of each application replica, brownout is able to counteract temporary overload situations by reducing the computational complexity of jobs entering the system. To avoid existing load balancers interfering with the brownout functionality, brownout-aware load balancers are introduced. Simulation experiments show that the proposed load balancers outperform existing load balancers in providing a high quality of service to as many end users as possible. Experiments in a testbed environment further show how a replicated brownout-enabled application is able to maintain high performance during overloads as compared to its non-brownout equivalent.Next, a feedback controller for cloud autoscaling is introduced. Using a novel way of modeling the dynamics of typical cloud application, a mechanism similar to the classical Smith predictor to compensate for delays in reconfiguring resource provisioning is presented. Simulation experiments show that the feedback controller is able to achieve faster control of the response times of a cloud application as compared to a threshold-based controller.Finally, a solution for handling the trade-off between performance and disaster tolerance for geo-replicated cloud applications is introduced. An automated mechanism for differentiating application traffic and replication traffic, and dynamically managing their bandwidth allocations using an MPC controller is presented and evaluated in simulation. Comparisons with commonly used static approaches reveal that the proposed solution in overload situations provides increased flexibility in managing the trade-off between performance and data consistency

Earthquake Ground Motion Simulation using Novel Machine Learning Tools

A novel method of model-independent probabilistic seismic hazard analysis(PSHA) and ground motion simulation is presented and verified using previously recorded data and machine learning. The concept of “eigenquakes” is introduced as an orthonormal set of basis vectors that represent characteristic earthquake records in a large database. Our proposed procedure consists of three phases, (1) estimation of the anticipated level of shaking for a scenario earthquake at a site using Gaussian Process regression, (2) extraction of the eigenquakes from Principal Component Analysis (PCA) of data, and (3) optimal combination of the eigenquakes to generate time-series of ground acceleration with spectral ordinates obtained in phase (1). The benefits of using a model-independent method of PSHA and ground motion simulation, particularly in large urban areas where dense instrumentation is available or expected, are argued. The effectiveness of the proposed methodology is exhibited using eight scenario examples for downtown areas of Los Angeles and San Francisco where it is shown that no dependency on specific ground motion prediction equations or processes of selection and scaling would be needed in our procedure. Furthermore, PCA allows systematic analysis of large databases of ground motion records that are otherwise very difficult to handle by conventional methods of data analysis. Advantages, disadvantages, and future research needs are highlighted at the end

Inferring collective dynamical states from widely unobserved systems

When assessing spatially-extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain's collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary figures. Title changed to match journal referenc

The effects of oil price shocks on the Iranian economy

The Iranian economy is highly vulnerable to oil price fluctuations. This paper analyzes the dynamic relationship between oil price shocks and major macroeconomic variables in Iran by applying a VAR approach. The study points out the asymmetric effects of oil price shocks; for instance, positive as well as negative oil price shocks significantly increase inflation. We find a strong positive relationship between positive oil price changes and industrial output growth. Unexpectedly, we can only identify a marginal impact of oil price fluctuations on real government expenditures. Furthermore, we observe the Dutch Disease syndrome through significant real effective exchange rate appreciation. --macroeconomic uctuations,oil price shocks,developing economies,Iran,VAR modelling