Techniques for improving the scalability of data center networks

Data centers require highly scalable data and control planes for ensuring good performance of distributed applications. Along the data plane, network throughput and latency directly impact application performance metrics. This has led researchers to propose high bisection bandwidth network topologies based on multi-rooted trees for data center networks. However, such topologies require efficient traffic splitting algorithms to fully utilize all available bandwidth. Along the control plane, the centralized controller for software-defined networks presents new scalability challenges. The logically centralized controller needs to scale according to network demands. Also, since all services are implemented in the centralized controller, it should allow easy integration of different types of network services.^ In this dissertation, we propose techniques to address scalability challenges along the data and control planes of data center networks.^ Along the data plane, we propose a fine-grained trac splitting technique for data center networks organized as multi-rooted trees. Splitting individual flows can provide better load balance but is not preferred because of potential packet reordering that conventional wisdom suggests may negatively interact with TCP congestion control. We demonstrate that, due to symmetry of the network topology, TCP is able to tolerate the induced packet reordering and maintain a single estimate of RTT.^ Along the control plane, we design a scalable distributed SDN control plane architecture. We propose algorithms to evenly distribute the load among the controller nodes of the control plane. The algorithms evenly distribute the load by dynamically configuring the switch to controller node mapping and adding/removing controller nodes in response to changing traffic patterns. ^ Each SDN controller platform may have different performance characteristics. In such cases, it may be desirable to run different services on different controllers to match the controller performance characteristics with service requirements. To address this problem, we propose an architecture, FlowBricks, that allows network operators to compose an SDN control plane with services running on top of heterogeneous controller platforms

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS's can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://github.com/PengZai/ARIC.Comment: Aceepted by AAAI2023. Code and results available at https://github.com/PengZai/ARI

Factors in Long-Term Algal Composition of Cayman Reefs: Determining Effects of Natural Disturbance and Grazer Pressures

Abstract. Current research shows a worldwide shift in the population dynamics of reefs attributed to increasing human disturbance. With increasing nutrient additions, competitive populations of turf and macroalgae bloom and dominate coral reefs leading to decreased health of the reef as a whole. Unfortunately, few studies have been done showing the long-term changes in algae composition on reefs. In order to find the significant factors in the long-term composition of algae on reefs, algae and fish abundance data were collected through the Lawrence University Marine Program and analyzed for this study. Algae were split into three functional groups: encrusting, turf and macroalgae, and relative abundance for each was compared for significance against fish and level of natural disturbance at a site. This study found that the level of natural disturbance was likely an important factor in long-term algal abundance with the highly protected sites hosting high levels of macroalgae and less protected sites hosting high levels of turf algae. Two herbivores, blue tangs (Acanthurus coeruleus) and stoplight parrotfish (Sparisoma viride) were also found to significantly control turf algae. Future studies on fishing pressures in the region could more fully assess the long-term effects of herbivores on reef algae composition

Laser cladding and its potential to reduce particulate matter emissions from the automotive and locomotive sector

Laser Cladding (LC) is an emerging technology which is used both for coating applications as well as near-net shape fabrication. Despite its significant advantages, such as low dilution and metallurgical bond with the substrate, it still faces issues such as process control and repeatability, which restricts the extension to its applications. The following thesis evaluates the LC technology and tests its potential to be applied to reduce particulate matter emissions from the automotive and locomotive sector. The evaluation of LC technology was carried out for the deposition of multi-layer and multi-track coatings. 316L stainless steel coatings were deposited to study the minimisation of geometric distortions in thin-walled samples. Laser power, as well as scan strategy, were the main variables to achieve this goal. The use of constant power, reduction at successive layers, a control loop control system, and two different scan strategies were studied. The closed-loop control system was found to be practical only when coupled with the correct scan strategy for the deposition of thin walls. Three overlapped layers of aluminium bronze were deposited onto a structural steel pipe for multitrack coatings. The effect of laser power, scan speed and hatch distance on the final geometry of coating were studied independently, and a combined parameter was established to effectively control each geometrical characteristic (clad width, clad height and percentage of dilution). LC was then applied to coat commercial GCI brake discs with tool steel. The optical micrography showed that even with preheating, the cracks that originated from the substrate towards the coating were still present. The commercial brake discs emitted airborne particles whose concentration and size depended on the test conditions used for simulation in the laboratory. The contact of LC cladded wheel with rail emitted significantly less ultra-fine particles while maintaining the acceptable values of coefficient of friction

Coupling of ecological and water quality models for improved water resource and fish management

In recent years new ideas for nutrient management to control eutrophication in estuarine environments have been under consideration. One popular approach being considered in the Chesapeake Bay Program is called the “top down” approach based on the premise that restoring algal predators, such as oysters and menhaden, will limit excess phytoplankton production and possibly eliminate costly nutrient control programs. The approach is being considered to replace or use in conjunction with the “bottom up” approach of reducing nutrient loads. The ability to model higher trophic levels such as fish, as well as the eutrophication processes driving production of primary producers in an aquatic ecosystem is needed. CE-QUAL-ICM (ICM) and Ecopath were two models selected for this research. ICM is a time- and spatial-varying eutrophication model that uses nutrient loads to predict primary producers, while Ecopath is a static mass balance model representing an average time period (e.g., season or year) and uses values of primary producers and other groups to predict fish biomass. Linking the two models will provide the means of going up the food chain by trophic levels. The Chesapeake Bay was chosen as the study site since both models are in use there. Before coupling ICM and Ecopath, common links between the two models were found. Ten groups were identified with such variables as production rates, consumption rates, and unassimilated food/consumption. A post-processor/subroutine was developed for ICM to aggregate output data from 3-D to 0-D to be used in Ecopath. Two Ecopath runs were developed with data from ICM and the Chesapeake Bay (CB) Ecopath model to see how network interactions differed with data representing the same system. Four additional runs were made, creating perturbations (i.e., increased phytoplankton production) using the CB Ecopath model and replacing the primary producers with data from ICM. Final runs of ICM were conducted looking at adjusting three parameters to try to restore the Bay back to 1950 conditions. It was demonstrated that ICM data can be coupled with Ecopath to study management strategies in eutrophication. Because of model formulations there was no data exchange from Ecopath back to ICM

Stratospheric aircraft exhaust plume and wake chemistry studies

This report documents progress to date in an ongoing study to analyze and model emissions leaving a proposed High Speed Civil Transport (HSCT) from when the exhaust gases leave the engine until they are deposited at atmospheric scales in the stratosphere. Estimates are given for the emissions, summarizing relevant earlier work (CIAP) and reviewing current propulsion research efforts. The chemical evolution and the mixing and vortical motion of the exhaust are analyzed to track the exhaust and its speciation as the emissions are mixed to atmospheric scales. The species tracked include those that could be heterogeneously reactive on the surfaces of the condensed solid water (ice) particles and on exhaust soot particle surfaces. Dispersion and reaction of chemical constituents in the far wake are studied with a Lagrangian air parcel model, in conjunction with a radiation code to calculate the net heating/cooling. Laboratory measurements of heterogeneous chemistry of aqueous sulfuric acid and nitric acid hydrates are also described. Results include the solubility of HCl in sulfuric acid which is a key parameter for modeling stratospheric processing. We also report initial results for condensation of nitric acid trihydrate from gas phase H2O and HNO3

Modelling the Self-Heating of Steel Stockpiles

Understanding the uncertainty of model parameters is crucial for building predictive models. Within the field of spontaneous ignition a slight variation in the model parameters can cause a significant variation in our ability to determine if ignition occurs. We consider this problem through an application to the steel industry. A byproduct of the steelmaking process is stockpiled where oxidation can induce ignition. The resulting ignition process sinters the filter improving the durability. Understanding this process requires careful modelling and consideration of the uncertainty in the reaction kinetics. We examine some experimental data on the filter cake to determine these reaction kinetics. Due to the complex nature of the filter cake, standard estimation techniques are difficult to apply and the uncertainty in our parameters cannot be an input into the larger stockpiles. We apply a Bayesian framework for parameter estimation that considers a distribution for the parameters rather than a point estimation with an uncertainty. Using this approach we construct a Markov Chain Monte Carlo (MCMC) algorithm to sample this distribution and test this against simulated data; we capture the true values of the reaction kinetics within the target distribution. Our approach uses the experimental data, and we construct meaningful estimates for the reaction kinetics. Once we consider multiple sets of experimental data it highlights some issues with our proposed reaction scheme and methodology. Our Sequential Monte Carlo approach identifies a discrepancy between the estimates for different experiments. We consider multiple different avenues to resolve this discrepancy. The most significant of these methods was to consider a different reaction scheme. In addition to this, the initial reactant concentrations were found to have a significant effect on the posterior distribution of parameter estimates; if we change these values then our distributions would not be overlapping. Additionally, we then consider a different approach to combining the multiple experiments; combining the parameter distributions from the MCMC method, we propose a new prior that contains this information and use this to combine the experimental data. This is effective at generating a new sample, though it does not address any of the issues that may arise from model mis-specification

Artificial intelligence transformation and implementation frameworks

In modern days there are many new technologies that are being implemented into organizations to gain competitive advantage and keep up with the evolving landscape. Artificial intelligence has become one of these new technologies and it even has been said to be one of the general purpose technology as likes of steam engine, electricity and internet. Usually reaping full benefits of new technologies has been troublesome for organizations. Therefore finding a working framework that would help with this burden and aim to increase the probability of revenue increase and cost cutting benefits is needed. There are many factors that have to be weighed in to generate a working AI framework for the organization. The aim of this research was therefore to answer the questions how organizations can start their AI journey and how they can implement AI initiatives. This can be answered by developing and AI transformation framework and a more detail AI implementation framework. Development of the model was conducted with Design science research methodology. DSR-methodology was built on literature reviews and empirical part that was done by subject matter interview. In the study two frameworks were developed that answer the research questions. A four stepped process was developed for the AI transformation process that can be followed to meet strategic alignment. AI implementation framework was also developed with consideration on approach, team, tools and methodology. These frameworks developed in this study are intended to be used with AI endeavors in Finnish companies

Microbial Community Analysis Coupled With Geochemical Studies Reveal Factors Affecting Biotic Mn(II) Oxidation In Situ

Factors that affect biogeochemical cycling are important in ecosystems susceptible to anthropogenic input such as caves. The extent to which anthropogenic input can modulate the pathways and Mn cycling in caves is largely unknown. Microbial communities associated with Mn(III/IV) oxide deposits were assessed in both relatively pristine and anthropogenically impacted caves in the southern Appalachians. Fungal communities appeared to be independent of carbon infiltration, while the bacterial and archaeal communities appeared to be driven by long-term exogenous carbon loading. Cave sites were amended with various carbon sources that are commonly associated with anthropogenic input to determine whether exogenous anthropogenic input would stimulate biotic Mn(II) oxidation in situ. Sites where carbon incubations stimulated biotic Mn(II) oxidation microbial communities were significantly impacted, indicating that anthropogenic input can enhance biotic Mn(II) oxidation potentially shaping community dynamics. Geochemical analyses of sediment substrates suggest that some biotic Mn(II) oxidation pathways (e.g. MnP mediated oxidation) can be limited by carbon and low C:N ratios. Bacterially selective Mn(II)-oxidizer media supplemented with 100 µM Cu(II) inhibited Mn(II) oxidation, indicating that bacteria were using superoxide to oxidize Mn(II). In contrast, fungally selective media with Cu(II) stimulated Mn(II) oxidation, indicating that fungi were using MCO enzymes for Mn(II) oxidation