Adaptive Performance and Power Management in Distributed Computing Systems

The complexity of distributed computing systems has raised two unprecedented challenges for system management. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, system power consumption must be controlled in order to avoid system failures caused by power capacity overload or system overheating due to increasingly high server density. However, most existing work, unfortunately, either relies on open-loop estimations based on off-line profiled system models, or evolves in a more ad hoc fashion, which requires exhaustive iterations of tuning and testing, or oversimplifies the problem by ignoring the coupling between different system characteristics (\ie, response time and throughput, power consumption of different servers). As a result, the majority of previous work lacks rigorous guarantees on the performance and power consumption for computing systems, and may result in degraded overall system performance. In this thesis, we extensively study adaptive performance/power management and power-efficient performance management for distributed computing systems such as information dissemination systems, power grid management systems, and data centers, by proposing Multiple-Input-Multiple-Output (MIMO) control and hierarchical designs based on feedback control theory. For adaptive performance management, we design an integrated solution that controls both the average response time and CPU utilization in information dissemination systems to achieve bounded response time for high-priority information and maximized system throughput in an example information dissemination system. In addition, we design a hierarchical control solution to guarantee the deadlines of real-time tasks in power grid computing by grouping them based on their characteristics, respectively. For adaptive power management, we design MIMO optimal control solutions for power control at the cluster and server level and a hierarchical solution for large-scale data centers. Our MIMO control design can capture the coupling among different system characteristics, while our hierarchical design can coordinate controllers at different levels. For power-efficient performance management, we discuss a two-layer coordinated management solution for virtualized data centers. Experimental results in both physical testbeds and simulations demonstrate that all the solutions outperform state-of-the-art management schemes by significantly improving overall system performance

Energy Network Communications and Expandable Control Mechanisms

A modular, expandable network requiring little or no calibration is something that is well sought after and would offer great benefits when used for distributed energy generation. Intelligent and adaptive control of such a network offers stability of supply from intermittent sources which, to date, has been hard to achieve. Key to the effective use of such control systems is communications, specifically the exchange of commands and status information between the control systems and the attached devices. Power-line communications has been used in various applications for years and would offer a good mechanism for interconnecting devices on a power grid without the expense of laying new cabling. By using clusters of devices managed by an IEMS (Intelligent Energy Management System) in a branching network fashion (not unlike the grid itself) it would be possible to manage large numbers of devices and high speed with relatively low bandwidth usage increasing the usable range of transmission. Implications of this include improving network efficiency through managed power distribution and increased security of supply

Adaptive power control for UMTS

University of Technology, Sydney. Faculty of Engineering.Inner-loop power control is one of the essential radio resource management functions of WCDMA systems. It aims to control the transmission power to ensure that the quality of service for each communication link is adequate and the interference in the system is minimised.Inner-loop power control currently used in UMTS is a SIR-based fixed stepsize power control (FSPC) algorithm. Transmit Power Control (TPC) commands are sent to control transmission power. This kind of power control algorithm has many limitations such as its inability to track rapid changes in radio channel fading. Furthermore, it creates oscillation when the channel is stable. These limitations result in power control error (PCE) in the received signal. High PCE leads to several performance degradations such as more outage probability and an increase in the total interference. In this thesis, new inner-loop power control algorithms are proposed to minimise PCE. One of the new algorithms utilises historical information of TPC commands to intelligently adjust the power control stepsize. The performance of the proposed algorithm is compared with adaptive power control algorithms proposed in the literature. The simulation results show that the proposed adaptive power control algorithm outperforms the conventional fixed stepsize power control algorithm. Furthermore, it outperforms other adaptive power control algorithms in some scenarios. The results from the simulations in this thesis show that delays in the power control feedback channel lead to performance degradations especially for adaptive power control algorithms. A new delay compensation technique named partial time delay compensation (PTDC) is proposed to mitigate the effect of delays. Simulations show that the performance in terms of PCE can be improved using this new compensation technique. Knowledge of the maximum Doppler frequency, which is closely related to user speed, is invaluable for optimisation of radio networks in several aspects. It can be used to improve the performance of inner loop power control. A new parameter named Consecutive TPC ratio (CTR) is originally defined in this thesis. CTR has a correlation with the maximum Doppler frequency so that it can be used to estimate user speed. The simulation results show that with the use of ldB FSPC, user speeds can be accurately estimated up to 45 km/h. A new adaptive power control algorithm, named CAAP, in which the stepsize is adjusted using CTR, is also proposed. The simulation result shows that CAAP can achieve similar performance as that of the adaptive power control algorithm in which the stepsize is adjusted based on perfect knowledge of the optimal fixed stepsize for every user speed. Furthermore, the performance of CTR aided speed estimation can be recursively improved with the use of CAAP

Probabilistic adaptive model predictive power pinch analysis (PoPA) energy management approach to uncertainty

This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day-ahead (DA) and adaptive power pinch strategy

A self-organized resource allocation scheme for heterogeneous macro-femto networks

This paper investigates the radio resource management (RRM) issues in a heterogeneous macro-femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user-deployed femtos is not known a-priori. This makes interference management crucial. In particular, with co-channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross-layer and co-layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro-femto network. Then, we propose a self-organized resource allocation (SO-RA) scheme for an orthogonal frequency division multiple access based macro-femto network to mitigate co-layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse-1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse-1, while the SO-RA scheme achieves improved throughput and fairness performance. SO-RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state-of-the-art resource allocation schemes

Efficient Digital System Management using IEEE 1451.0 Enabled Control Architecture

The IEEE and National Institute of Standards and Technology have formulated an open universal standard called IEEE 1451 for ‘Smart Transducer Interface’ with digital systems. The objectives of this paper is to propose IEEE 21450 enabled control architectures for efficient management of power system with embedded system parameters as electronic documentation. The control architecture accommodates appropriate number of transducer interface module along with transducer electronic data sheet, which enables active calibration, adaptive tuning and failure proof operation of system management

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results

Adaptive load frequency control of electrical power systems

The thesis describes Load Frequency Control techniques which may be used for real-time on-line control of large electrical power systems. Traditionally the frequency control of power systems has been carried out using standard fixed parameter control schemes, which give control over the immediate steady- state error and the long term accumulated frequency error, but do not account for the fact that system conditions can alter due to the change in consumer load and generating patterns. The thesis presents a method of controlling the system frequency using adaptive control techniques, which ensure that optimal control action is calculated based on the present system conditions. It enables the system operating point to be monitored so that optimal control may continue to be calculated as the system operating point alters. The proposed method of frequency control can be extended to meet the problems of system interconnection and the control of inter-area power flows. The thesis describes the work carried out at Durham on a fixed parameter control scheme which led to the development of an adaptive control scheme. The controller was validated against a real-time power system simulator with full Energy Management software. Results are also presented from work carried out at the Central Electricity Research Laboratories under the C.A.S.E award scheme. This led to the development of a power system simulator, which along with the controller was validated on-line with the Dispatch Project used by the Central Electricity Generating Board