Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation.

The work presented in this thesis studies the load and loadeffects of traffic loads on railway bridges. The increasedknowledge of the traffic loads, simulated using fieldmeasurements of actual trains, are employed in a reliabilityanalysis in an attempt at upgrading existing railwaybridges. The study utilises data from a weigh-in-motion site whichrecords, for each train, the train speed, the loads from eachaxle and the axle spacings. This data of actual trainconfigurations and axle loads are portrayed as moving forcesand then used in computer simulations of trains crossing twodimensional simply supported bridges at constant speed. Onlysingle track short to medium span bridges are considered in thethesis. The studied load effect is the moment at mid-span. Fromthe computer simulations the moment history at mid-span isobtained. The load effects are analysed by two methods, the first isthe classical extreme value theory where the load effect ismodelled by the family of distributions called the generalisedextreme value distribution (GEV). The other method adopts thepeaks-over-threshold method (POT) where the limiting family ofdistributions for the heights to peaks-over-threshold is theGeneralised Pareto Distribution (GPD). The two models aregenerally found to be a good representation of the data. The load effects modelled by either the GEV or the GPD arethen incorporated into a reliability analysis in order to studythe possibility of raising allowable axle loads on existingSwedish railway bridges. The results of the reliabilityanalysis show that they are sensitive to the estimation of theshape parameter of the GEV or the GPD. While the study is limited to the case of the ultimate limitstate where the effects of fatigue are not accounted for, thefindings show that for the studied cases an increase inallowable axle load to 25 tonnes would be acceptable even forbridges built to the standards of 1940 and designed to LoadModel A of that standard. Even an increase to both 27.5 and 30tonnes appears to be possible for certain cases. It is alsoobserved that the short span bridges ofapproximately fourmetres are the most susceptible to a proposed increase inpermissible axle load. <b>Keywords:</b>bridge, rail, traffic load, load effect,dynamic amplification factor, extreme value theory,peaks-over-threshold, reliability theory, axle loads, fielddata.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

To address the overload conditions caused by the increasing network traffic volume, recent literature in the network intrusion detection and prevention field has proposed the use of clusters of network intrusion detection and prevention systems (NIDPSs). We observe that simple traffic distribution schemes are usually used for NIDPS clusters. These schemes have two major drawbacks: (1) the loss of correlation information caused by the traffic distribution because correlated flows are not sent to the same NIDPS and (2) the unbalanced loads of the NIDPSs. The first drawback severely affects the ability to detect intrusions that require analysis of correlated flows. The second drawback greatly increases the chance of overloading an NIDPS even when loads of the others are low. In this thesis, we address these two drawbacks. In particular, we propose two novel traffic distribution systems: the Correlation-Based Load Balancer and the Correlation-Based Load Manager as two different solutions to the NIDPS traffic distribution problem. On the one hand, the Load Balancer and the Load Manager both consider the current loads of the NIDPSs while distributing traffic to provide fine-grained load balancing and dynamic load distribution, respectively. On the other hand, both systems take into account traffic correlation in their distributions, thereby significantly reducing the loss of correlation information during their distribution of traffic. We have implemented prototypes of both systems and evaluated them using extensive simulations and real traffic traces. Overall, the evaluation results show that both systems have low overhead in terms of the delays introduced to the packets. More importantly, compared to the naive hash-based distribution, the Load Balancer significantly improves the anomaly-based detection accuracy of DDoS attacks and port scans -- the two major attacks that require the analysis of correlated flows -- meanwhile, the Load Manager successfully maintains the anomaly-based detection accuracy of these two major attacks of the NIDPSs.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

To address the overload conditions caused by the increasing network traffic volume, recent literature in the network intrusion detection and prevention field has proposed the use of clusters of network intrusion detection and prevention systems (NIDPSs). We observe that simple traffic distribution schemes are usually used for NIDPS clusters. These schemes have two major drawbacks: (1) the loss of correlation information caused by the traffic distribution because correlated flows are not sent to the same NIDPS and (2) the unbalanced loads of the NIDPSs. The first drawback severely affects the ability to detect intrusions that require analysis of correlated flows. The second drawback greatly increases the chance of overloading an NIDPS even when loads of the others are low. In this thesis, we address these two drawbacks. In particular, we propose two novel traffic distribution systems: the Correlation-Based Load Balancer and the Correlation-Based Load Manager as two different solutions to the NIDPS traffic distribution problem. On the one hand, the Load Balancer and the Load Manager both consider the current loads of the NIDPSs while distributing traffic to provide fine-grained load balancing and dynamic load distribution, respectively. On the other hand, both systems take into account traffic correlation in their distributions, thereby significantly reducing the loss of correlation information during their distribution of traffic. We have implemented prototypes of both systems and evaluated them using extensive simulations and real traffic traces. Overall, the evaluation results show that both systems have low overhead in terms of the delays introduced to the packets. More importantly, compared to the naive hash-based distribution, the Load Balancer significantly improves the anomaly-based detection accuracy of DDoS attacks and port scans -- the two major attacks that require the analysis of correlated flows -- meanwhile, the Load Manager successfully maintains the anomaly-based detection accuracy of these two major attacks of the NIDPSs.

No description available.

With the economic development, the high quality has become a critical issue for service continuous of power companies. To ensure the stable power supply, the asset management of power equipments is applied to prevent the system outage. With voluminous distribution transformers over very wide area, the real time monitoring of temperature has been included in the scope of smart grid. During recent years, the service outage due to transformer overloading has caused customer panic as well as deterioration of service quality. This thesis develops the Transformer Terminal Unit (TTU) by integration of computer chip for power consumption, DSP and sampling circuit of temperature measurement to achieve the functions of real time monitoring of transformer operation condition. When an abnormal operation condition such as overloading or high oil temperature occurs, the TTU can report the contingency back to the control station via the hybrid communication system so that the distribution system operators can take remedy action to prevent the contingency. The actual loading and temperature of transforms are also measured and collected in this study to develop the relationship of temperature and loading levels. By collecting transformer temperature, the power demand of a transformer can be estimated and the load shedding can then be activated to prevent the problem of overloading when the temperature exceeds the operation constraint.

A nonlinear automated parameter fitted analytical model that numerically predicts the load-displacement response of wood frame shear walls subjected to static monotonic loading with and without vertical load is presented. This analytical model referred to as Analytical Model of wood frame SHEar walls subjected to Vertical load (AMSHEV) is based on the kinematic behavior of wood frame shear walls and captures significant characteristics observed from experimental testing through appropriate modeling of three failure mechanisms that can occur within a shear wall under static monotonic load: 1) failure of sheathing-to-framing connectors, 2) failure of vertical studs, and 3) uplift of end studs from bottom sill. Previous models have not accounted for these failure mechanisms as well as the inclusion of vertical load, which has shown to reveal beneficial effects such as increasing the ultimate load capacity and limiting uplift of the wall as noted in experimental tests. Results from the proposed numerical model capture these effects within 7% error of experimental test data even when different magnitudes of vertical load are applied to predict the ultimate load capacity of wood frame shear walls.

The thesis investigates the optimal expansion planning of substations for the distribution system of Taipei City District of Taiwan Power Company. The small area load forecasting is executed with the support of Outage Management System(OMS) database. The capacity expansion of distribution substations is obtained by considering the annual load growth of each service area to achieve the cost effectiveness of substation investment. The geographic information of each service zone has been retrieved form the OMS data. With the land use planning of Taipei City Government, the load density of each small area for the target year is derived according to the final floor area and development strength of the land base. The load forecasting of each small area is then solved by considering the load growth of each customer class, which is then used for the expansion planning of substations. After determining the small area load forecasting for the final target year, the center of gravity method is applied to find the geographic blocks of all substations and the corresponding service areas at the target year. The power loading of each small area is used to calculate the power loading loss of which service area to solve the optimal location within the block for each substation. Based on the annual load forecasting of all small areas, the expansion planning of distribution substations for Taipei City District is derived by Dynamic Programming(DP) and Immune Algorithm(IA) to achieve minimization of power loading loss with subject to the operation constraint. By the proposed methodology, the unit commitment of distribution substations is determined to meet the load growth of service area and achieve power loading loss minimization of distribution systems.