When dealing with latency-critical applications in Kubernetes, a common strategy is to over-allocate resources to ensure the application can meet its latency guarantees during traffic surges. However, this practice often leads to resource underutilizationas the application will not fully utilize its reserved resources. The Kubernetes scheduler cannot initiate new workloads on the node because of perceived full resource utilization. This study explored the utility of two existing methods, Container Runtime Interface (CRI-RM), which we configured to use the 'balloon policy' and the vertical Pod Autoscaler (VPA) in addressing resource underutilization problems on single node Kubernetes clusters while maintaining latency-grantees of certain pods. Utilizing tc-sim, a network traffic simulator, we deployed four latency-critical and two non-latency-critical pods, all subjected to overallocation. Our finding reveals that VPA was ineffectivein detecting and addressing the underutilization of resources because of its slow response in adjusting requests inside the pods. Moreover, it worsened the underutilization issues of the node. Our configuration of the 'balloon policy' failed to detect theover-allocation issues and further led to performance degradation in the simulator, potentially due to the overhead introduced by CRI-RM. These results underscore the intricacy of over-allocation challenges in latency-critical applications, emphasizing the need for proposed-designed solutions that enable quick and dynamic exchange of resources between pods.

[pt] Esse estudo apresenta o modelo de negócios de multi-family offices e propõe uma abordagem prescritiva específica para a elaboração e manutenção de portfolios de investimentos, com objetivo de torna-los mais eficientes em servir os interesses de seus clientes. O aconselhamento financeiro – atividade central destas empresas – tem por essência guiar os clientes a tomarem decisões melhores. Para esse fim, a abordagem proposta incorpora elementos da teoria financeira comportamental, que confrontam a teoria econômica clássica para explicar o comportamento do investidor normal – isto é, sujeito a problemas de autocontrole, a dor do arrependimento, a vieses cognitivos, etc.. Entende-se que o processo de elaboração e manutenção de portfólios deva ser iterativo e dinâmico, movido pelo relacionamento e pela qualidade da comunicação entre as partes (gestor e investidor). Foi desenvolvido um modelo-base para nortear esse processo, identificando e estabelecendo uma relação entre os diferentes componentes de risco envolvidos, dentre eles a tolerância, a capacidade e a atitude ao risco. Além disso, são incorporados os objetivos do cliente, seus valores, o horizonte de investimento, a filosofia da empresa e a aculturação comportamental financeira – tanto do investidor quanto do gestor. Essa abordagem pressupõe os gestores como verdadeiros educadores, apresentando recomendações práticas para que entendam o seu próprio comportamento, melhorando as suas decisões e o seu entendimento sobre as motivações de seus clientes. Isso os tornará melhores em servi-los e educa-los, favorecendo a retenção no longo prazo. / [en] This study presents the multi-family office business model and proposes a specific approach to the construction and management of investment portfolios, in order to make them more efficient at serving their clients interests. Financial advisory – the multi-family offices core activity – is aimed at guiding clients towards making better decisions. For this purpose, the proposed framework incorporates elements of behavioral finance, which confronts standard economic theory in order to explain the behavior of normal investors; that is, subject to selfcontrol problems, the pain of regret, cognitive biases and so on. It is understood that the portfolio elaboration and management process must be iterative and dynamic, relationship-driven and guided by the quality of communication between the parties (manager and client). A base-model was developed to guide this process, identifying and relating the different risk components that are involved, such as tolerance, capacity and risk attitude. Furthermore, it incorporates the client s objectives, values, investment horizon, the company s philosophy and both the client s and manager s behavioral finance knowledge. This approach foreshadows the manager as a true educator, presenting practical recommendations so that they can better understand their own behavior, improving their decisions and knowledge regarding their clients motivations. This will make managers better at serving and educating them, favoring long-term retention.

Disasters are characterized by large numbers of victims and required resources, overwhelming the available resources. Disaster response involves various entities like Incident Commanders, dispatch centers, emergency operations centers, area command and hospitals. An effective emergency response system should facilitate coordination between these various entities. Victim triage, emergency resource allocation and victim dispatch to hospitals form an important part of an emergency response system. In this present research effort, an emergency response system with the aforementioned components is developed. Triage is the process of prioritizing mass casualty victims based on severity of injuries. The system presented in this thesis is a low-cost victim triage system with RFID tags that aggregate all victim information within a database. It will allow first responders' movements to be tracked using GPS. A web-based real time resource allocation tool that can assist the Incident Commanders in resource allocation and transportation for multiple simultaneous incidents has been developed. This tool ensures that high priority resources at emergency sites are received in least possible time. This web-based tool also computes the patient dispatch schedule from each disaster site to each hospital. Patients are allocated to nearest hospitals with available medical facilities. This tool can also assist resource managers in emergency resource planning by computing the time taken to receive required resources from the nearest depots using Google Maps. These web-based tools complements emergency response systems by providing decision-making capabilities.

The geographic mosaic theory of coevolution predicts that spatial differences in species interactions result in a patchwork of evolutionary hot and cold spots across a landscape. We used horsenettle (Solanum carolinense L.), a perennial weed with a diverse insect community found in old fields and meadows, to examine local adaptation and resource-mediated selection. The goals of this study were to (1) determine the potential for a selection mosaic by identifying local adaptation through trait-interaction matching with herbivores, pollinations and plant competitors, and (2) determine the potential for indirect selection through resource allocation tradeoffs. The potential for local adaptation was determined by measuring interactions in four populations and relating those findings to plant traits measured on offspring grown from those populations in a ‘common garden.’ Allocation tradeoffs between growth, herbivore resistance, and floral traits were also assessed in the common garden. We found high herbivore damage in the field associated with decreased root:shoot ratios in greenhouse-grown plants, which may indicate an herbivore-mediated effect on life-history through selection for a more annual strategy. By examining allocation tradeoffs we found evidence of two distinct reproductive strategies in this perennial plant. Negative correlations between reproductive traits and both growth and defense suggest that individuals either favor current growth and reproduction over defense, or invest in current survival and defense while delaying reproduction. Overall, this study sheds light on how selection changes over space and time, which are of many of the fascinating traits we find in plants and animals today.

The Internet architecture has proven its success by completely changing people’s lives. However, making significant architecture improvements has become extremely difficult since it requires competing Internet Service Providers to jointly agree. Re- cently, network virtualization has attracted the attention of many researchers as a solution to this ossification problem. A network virtualization environment allows multiple network architectures to coexist on a shared physical resource. However, most previous research has focused on network virtualization in a wired network en- vironment. It is well known that wireless networks have become one of the main access technologies. Due to the probabilistic nature of the wireless environment, vir- tualization becomes more challenging. This thesis consider virtualization in wireless networks with a focus on the challenges due to randomness. First, I apply mathe- matical tools from stochastic geometry on the random system model, with transport capacity as the network performance metric. Then I design an algorithm which can allow multiple virtual networks working in a distributed fashion to find a solution such that the aggregate satisfaction of the whole network is maximized. Finally, I proposed a new method of charging new users fairly when they ask to enter the system. I measure the cost of the system when a new user with a virtual network request wants to share the resource and demonstrate a simple method for estimating this “price”.

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Algorithms that use relational data are increasingly used to allocate resources within society. As researchers and decision-makers have adapted the role of algorithms from a descriptive one (describing patterns in data) to a prescriptive one (making decisions in predictive systems), there is an increasing concern that algorithms may replicate and even amplify societal bias, allocating worse or less resources to minorities and underrepresented groups. This dissertation proposes methodology for diagnosing when and how algorithms amplify inequality on networks as well as designing interventions for mitigating algorithmic bias. We leverage methods from network modeling, algorithmic game theory, and fair machine learning to uncover the root driver of bias in network data and to leverage this knowledge in order to design fair algorithms. In this thesis, we mostly focus on unsupervised learning problems, which present unique challenges that require a multi-faceted approach. We propose a unifying formulation for unifying different problems in unsupervised learning on networks and use it to propose methods to find the root cause of bias through modeling patterns of connections and embeddings. We leverage this knowledge to design fairer algorithms as well as to define diagnoses metrics for evaluating inequality before and after an algorithm is introduced. Furthermore, we argue for the need to bridge optimization-based learning and utility-based learning in creating stable, efficient, and useful systems. We use network models and mathematical formulations of distributional inequality in diagnosing the algorithmic amplification of bias in social recommendations and ranking algorithms. We find that the most common and neutral algorithms may further underrepresent minority groups in creating new connections or achieving high levels of visibility in networks that exhibit competition in increasing social capital and homophily (the tendency of people to connect with those similar to them). We uncover the role of homophily in helping a minority group overcome their initial disadvantage and we leverage it to design fairer information campaigns that equitable distribute messages across a population. Akin to this goal, we incorporate notions of utility and welfare in our algorithmic design, re-designing heuristics for grouping and clustering that improve the diversity of groups while preserving their usefulness, with applications in political and educational districting. Overall, this set of results aims to investigate the impact of algorithms on the outcomes of different populations and to open new avenues for inter-disciplinary research methods that can alleviate algorithmic bias. We close by discussing connections between different fields and methods as well as directions for future research.

When people multi-task with inputs that demand attention, processing, andencoding, sensory interference is possible at almost any level. Multiple Resource Theory (MRT) suggests that such interference may be avoided by drawing from separate pools of resources available when using different sensory channels, memory processes, and even different response modes. Thus, there should be advantages in dividing tasks among different sensory channels to tap independent pools of attentional resources. For example, people are better with two tasks using the eye and ear, than when using two auditory or two visual inputs. The majority of the research on MRT involves visual to auditory comparisons, i.e., the prime distance senses. The unstated implication is that the theory can be easily applied to other sensory systems, such as touch, but this is untested. This overlooks the fact that each sensory system has different characteristics that can influence how information processing is allocated in a multiple-task environment. For example, vision requires a directed gaze that is not required for sound or touch. Testing MRT with touch, not only eliminates competing theories, but helps establish its robustness across the senses. Three experiments compared the senses of touch and hearing to determine if the characteristics of those sensory modalities alter the allocation of processing resources. Specifically, it was hypothesized that differences in sensory characteristics would affect performance on a simple targeting task. All three experiments used auditory shadowing as the dual task load. In the first and third experiments a target was placed to the left or right of the participant and the targeting cue (either tactile, auditory, or combined) used to locate the target originated from the side on which the target was located. The only difference between experiments 1 and 3 was that in experiment 1 the auditory targeting cue was delivered by headphones, while in experiment 3 it was delivered by speakers. Experiment 2 was more difficult both in auditory perception and in processing. In this study the targeting cues came from in front of or behind the participant. Cues coming from in front of the participant meant the target was to the left, and conversely if the cue came from behind it meant that the target was to the right. The results of experiments 1 and 3 showed that when the signals originated from the sides, there was no difference in performance between the auditory and tactile targeting cues, whether by proximal or distal stimulation. However, in experiment 2, the participants were significantly slower to locate the target when using the auditory targeting cue than when using the tactile targeting cue, with nearly twice the losses when dual-tasking. No significant differences were found on performance of the shadowing task across the three experiments. The overall findings support the hypothesis that the characteristics of the sensory system itself influence the allocation of processing resources. For example, the differences in experiment 2 are likely due to front-back reversal, a common problem found with auditory stimuli located in front of or behind, but not with tactile stimuli.

While existing grid environments cater to specific needs of a particular user community, we need to go beyond them and consider general-purpose large-scale distributed systems consisting of large collections of heterogeneous computers and communication systems shared by a large user population with very diverse requirements. Coordination, matchmaking, and resource allocation are among the essential functions of large-scale distributed systems. Although deterministic approaches for coordination, matchmaking, and resource allocation have been well studied, they are not suitable for large-scale distributed systems due to the large-scale, the autonomy, and the dynamics of the systems. We have to seek for nondeterministic solutions for large-scale distributed systems. In this dissertation we describe our work on a coordination service, a matchmaking service, and a macro-economic resource allocation model for large-scale distributed systems. The coordination service coordinates the execution of complex tasks in a dynamic environment, the matchmaking service supports finding the appropriate resources for users, and the macro-economic resource allocation model allows a broker to mediate resource providers who want to maximize their revenues and resource consumers who want to get the best resources at the lowest possible price, with some global objectives, e.g., to maximize the resource utilization of the system.