OSMOSIS: Enabling Multi-Tenancy in Datacenter SmartNICs

Multi-tenancy is essential for unleashing SmartNIC's potential in datacenters. Our systematic analysis in this work shows that existing on-path SmartNICs have resource multiplexing limitations. For example, existing solutions lack multi-tenancy capabilities such as performance isolation and QoS provisioning for compute and IO resources. Compared to standard NIC data paths with a well-defined set of offloaded functions, unpredictable execution times of SmartNIC kernels make conventional approaches for multi-tenancy and QoS insufficient. We fill this gap with OSMOSIS, a SmartNICs resource manager co-design. OSMOSIS extends existing OS mechanisms to enable dynamic hardware resource multiplexing on top of the on-path packet processing data plane. We implement OSMOSIS within an open-source RISC-V-based 400Gbit/s SmartNIC. Our performance results demonstrate that OSMOSIS fully supports multi-tenancy and enables broader adoption of SmartNICs in datacenters with low overhead.Comment: 12 pages, 14 figures, 103 reference

Modelização de tarifas de energia elétrica para veículos elétricos

A continua evolução do número de Veículos Elétricos (VEs) como meio de transporte das pessoas é uma tendência que veio para ficar e se perpetuará nos anos que se avizinham nos países mais desenvolvidos. Uma grande preocupação dos operadores dos sistemas elétricos e dos comercializadores de energia prende-se com o comportamento de carregamento dos VEs, uma vez que estes podem influenciar a operação do sistema e consequentemente o preço da energia. Na verdade, o carregamento descoordenado dos VEs, conhecido como dumb charge, pode levar a problemas de congestionamento e tensão principalmente ao nível da rede de distribuição de média e baixa tensão. Neste sentido, os operadores de sistema têm vindo a estudar medidas que levem ao carregamento inteligente dos VEs, de forma a mitigar o impacto negativo que os VEs podem causar no sistema. Em paralelo, os operadores de sistema e comercializadores de energia podem beneficiar com a flexibilidade dos VEs. O operador do sistema tem interesse em escalonar o carregamento dos VEs para períodos em que não haja perigo de problemas de congestionamento e tensão. Similarmente, os comercializadores de energia pretendem que os VEs carreguem nos períodos de menor custo de energia, evitando a compra de energia em períodos mais dispendiosos. Neste contexto, esta dissertação aborda o problema de modelação de tarifas de energia horárias com vista a incentivar a mudança do comportamento do carregamento dos VEs. Mais precisamente, diferentes tarifas elétricas são modeladas com base na relação entre a geração de energia renovável, consumo de energia e o preço do mercado de energia. O caso de estudo usa a Dinamarca como referência, tirando partido da disponibilidade de informação necessária para a correta modelação de tarifas horárias. Uma das mais importantes contribuições desta dissertação reside na modelação de diferentes perfis de condução e de uso dos VEs (nomeadamente, diária, comercial e viagem única) considerando o comportamento dos utilizadores dos VEs através de uma escala de ansiedade que modela o comportamento dos utilizadores face ao preço de energia. Mais precisamente, o comportamento dos utilizadores é representado por um termo socioeconómico capaz de definir a ansiedade dos utilizadores dos VEs face ao valor horário da tarifa elétrica. Uma importante conclusão deste trabalho é a conceção adequada de tarifas horárias de energia com vista a reduzir os custos de energia quer para o utilizador do VE e potencialmente para o comercializador de energia. Mais precisamente, os comercializadores de energia e operadores de sistema podem utilizar a ferramenta desenvolvida para modelar esquemas tarifários que sejam capazes de suavizar a produção intermitente de fontes renováveis, bem como mover a carga de períodos de pico para períodos de vazio.The introduction of Electric Vehicles (EVs) as the mean of transportation is a trend that will prevail in the next years in the developed countries. The charging behavior of EVs is a key concern to system operators and retailers as it can affect the network operation and also the energy price. In fact, the uncontrolled charging of EVs, known as dumb charge, can lead to congestion and voltage problems in the network, especially at the distribution level. In this way, system operators are looking for ways to motivate the smart charging of EVs in order to mitigate their negative impact in the system. In parallel, system operators and retailers may profit from the handling of EVs as flexible loads. The former has interest to move their charging into periods without congestion and voltage problems. Similarly, the latter wants them to only charge at periods when energy is cheaper. In this scope, this dissertation addresses the problem by modelling a real-time tariff to encourage flexible EVs charging owners to charge in convenient periods. More precisely, different electricity tariffs are proposed based on the relation between renewable power generation, load consumption and spot price of the energy market. The tool developed uses Denmark as showcase, taking advantage of the easy access of information needed to develop the proposed tariffs in real-time context. One of the main contributions of this dissertation is the modelling of different profiles of EVs usage (e.g., commute, commercial and single trip behaviors) under different scales of users’ sensibility to the electricity prices. More precisely, a socioeconomic term that defines the anxiety of EVs users’ to be responsive to the tariffs is proposed. An important conclusion of this work is that a proper real-time tariff design can reduce the energy costs for the EVs and potentially to the retailer. More precisely, retailers and system operators can use this tool to model tariffs schemes that smooth the intermittent production of renewable energy, as well as to shift the consumption from peak to off-peak hours

Consumer load modeling and fair mechanisms in the efficient transactive energy market

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy DasTwo significant and closely related issues pertaining to the grid-constrained transactive distribution system market are investigated in this research. At first, the problem of spatial fairness in the allocation of energy among energy consumers is addressed, where consumer agents that are located at large distances from the substation – in terms of grid layout, are charged at higher rates than those close to it. This phenomenon, arising from the grid’s voltage and flow limits is aggravated during demand peaks. Using the Jain’s index to quantify fairness, two auction mechanisms are proposed. Both approaches are bilevel, with aggregators acting as interface agents between the consumers and the upstream distribution system operator (DSO). Furthermore, in spite of maximizing social welfare, neither mechanism makes use of the agents’ utility functions. The first mechanism is cost-setting, with the DSO determining unit costs. It implements the Jain’s index as a second term to the social welfare. Next, a power setting auction mechanism is put forth where the DSO’s role is to allocate energy in response to market equilibrium unit costs established at each aggregator from an iterative bidding process among its consumers. The Augmented Lagrangian Multigradient Approach (ALMA), which is based on vector gradient descent, is proposed in this research for implementation at the upper level. The mechanism’s lower level comprises of multiple auctions realized by the aggregators. The quasi-concavity of the Jain’s index is theoretically established, and it has been shown that ALMA converges to the Pareto front representing tradeoffs between social welfare and fairness. The effectiveness of both mechanisms is established through simulations carried out using a modified IEEE 37-bus system platform. The issue of extracting patterns of energy usage from time series energy use profiles of individual consumers is the focus of the second phase of this research. Two novel approaches for non-intrusive load disaggregation based on non-negative matrix factorization (NMF), are proposed. Both algorithms distinguish between fixed and shiftable load classes, with the latter being characterized by binary OFF and ON cycles. Fixed loads are represented as linear combinations of a set of basis vectors that are learned by NMF. One approach imposes L0 normed constraints on each shiftable load using a new method called binary load decomposition. The other approach models shiftable loads as Gaussian mixture models (GMM), therefore using expectation-maximization for unsupervised learning. This hybrid NMF-GMM algorithm enjoys the theoretical advantage of being interpretable as a maximum-likelihood procedure within a probabilistic framework. Numerical studies with real load profiles demonstrate that both algorithms can effectively disaggregate total loads into energy used by individual appliances. Using disaggregated loads, a maximum-margin regression approach to derive more elaborate, temperature-dependent utility functions of the consumers, is proposed. The research concludes by identifying the various ways gleaning such information can lead to more effective auction mechanisms for multi-period operation

Definition of QoE Fairness in Shared Systems

User-centric service and application management focuses on the Quality of Experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this paper is the definition of a generic QoE fairness index F which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant properties which are met by F. Consequently, the proposed index F may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization

Definition of QoE Fairness in Shared Systems

User-centric service and application management focuses on the Quality of Experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this paper is the definition of a generic QoE fairness index F which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant properties which are met by F. Consequently, the proposed index F may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization

Definition of QoE Fairness in Shared Systems

IEEE Communications Letters Vol.21 Nr.1, 184-187 ; User-centric service and application management focuses on the quality of experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this letter is the definition of a generic QoE fairness index F , which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain's fairness index. Based on results, we argue that neither QoS fairness nor Jain's fairness index meet all of the desirable QoE-relevant Properties, which are met by F . Consequently, the proposed index F may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization