21 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Fair Mixing: the Case of Dichotomous Preferences
We consider a setting in which agents vote to choose a fair mixture of public outcomes. The agents have dichotomous preferences: each outcome is liked or disliked by an agent. We discuss three outstanding voting rules. The Conditional Utilitarian rule, a variant of the random dictator, is strategyproof and guarantees to any group of like-minded agents an influence proportional to its size. It is easier to compute and more efficient than the familiar Random Priority rule. Its worst case (resp. average) inefficiency is provably (resp. in numerical experiments) low if the number of agents is low. The efficient Egalitarian rule protects individual agents but not coalitions. It is excludable strategyproof: I do not want to lie if I cannot consume outcomes I claim to dislike. The efficient Nash Max Product rule offers the strongest welfare guarantees to coalitions, who can force any outcome with a probability proportional to their size. But it even fails the excludable form of strategyproofness
Dynamic Pricing Problems Arising in the Adoption of Renewable Energy
There are two problems at the interface of electrical power and economics that are examined
in this thesis. The first problem addresses the issue of optimally operating electric vehicle (EV)
charging stations, where price as well as scheduling of purchasing, storing, and charging play key
roles. The second problem addresses the challenge faced by electric power system operators who
have to balance power generation and demand at all times, and are faced with the task of maximizing
the social welfare of all affected entities comprised of producers, consumers and prosumers
(e.g., homes with solar panels who may be producers at some times and consumers at other times).
For the first problem, we have developed a layered decomposition approach that permits a
holistic solution to solving the scheduling, storage and pricing problems of charging stations. The
key idea is to decompose problems by time-scale.
For the second problem, we have shown that for the special case of LQG agents, by careful
construction of a sequence of layered VCG payments over time, the intertemporal effect of current
bids on future payoffs can be decoupled, and truth-telling of dynamic states is guaranteed if system
parameters are known and agents are rational. We have also shown that a modification of the VCG
payments, called scaled-VCG payments, achieves Budget Balance and Individual Rationality for a
range of scaling, under a certain identified Market Power Balance condition
Dynamic Pricing Problems Arising in the Adoption of Renewable Energy
There are two problems at the interface of electrical power and economics that are examined
in this thesis. The first problem addresses the issue of optimally operating electric vehicle (EV)
charging stations, where price as well as scheduling of purchasing, storing, and charging play key
roles. The second problem addresses the challenge faced by electric power system operators who
have to balance power generation and demand at all times, and are faced with the task of maximizing
the social welfare of all affected entities comprised of producers, consumers and prosumers
(e.g., homes with solar panels who may be producers at some times and consumers at other times).
For the first problem, we have developed a layered decomposition approach that permits a
holistic solution to solving the scheduling, storage and pricing problems of charging stations. The
key idea is to decompose problems by time-scale.
For the second problem, we have shown that for the special case of LQG agents, by careful
construction of a sequence of layered VCG payments over time, the intertemporal effect of current
bids on future payoffs can be decoupled, and truth-telling of dynamic states is guaranteed if system
parameters are known and agents are rational. We have also shown that a modification of the VCG
payments, called scaled-VCG payments, achieves Budget Balance and Individual Rationality for a
range of scaling, under a certain identified Market Power Balance condition
Improving software middleboxes and datacenter task schedulers
Over the last decades, shared systems have contributed to the popularity of many technologies. From Operating Systems to the Internet, they have all brought significant cost savings by allowing the underlying infrastructure to be shared. A common challenge in these systems is to ensure that resources are fairly divided without compromising utilization efficiency. In this thesis, we look at problems in two shared systems—software middleboxes and datacenter task schedulers—and propose ways of improving both efficiency and fairness. We begin by presenting Sprayer, a system that uses packet spraying to load balance packets to cores in software middleboxes. Sprayer eliminates the imbalance problems of per-flow solutions and addresses the new challenges of handling shared flow state that come with packet spraying. We show that Sprayer significantly improves fairness and seamlessly uses the entire capacity, even when there is a single flow in the system. After that, we present Stateful Dominant Resource Fairness (SDRF), a task scheduling policy for datacenters that looks at past allocations and enforces fairness in the long run. We prove that SDRF keeps the fundamental properties of DRF—the allocation policy it is built on—while benefiting users with lower usage. To efficiently implement SDRF, we also introduce live tree, a general-purpose data structure that keeps elements with predictable time-varying priorities sorted. Our trace-driven simulations indicate that SDRF reduces users’ waiting time on average. This improves fairness, by increasing the number of completed tasks for users with lower demands, with small impact on high-demand users.Nas Ăşltimas dĂ©cadas, sistemas compartilhados contribuĂram para a popularidade de muitas tecnologias. Desde Sistemas Operacionais atĂ© a Internet, esses sistemas trouxeram economias significativas ao permitir que a infraestrutura subjacente fosse compartilhada. Um desafio comum a esses sistemas Ă© garantir que os recursos sejam divididos de forma justa, sem comprometer a eficiĂŞncia de utilização. Esta dissertação observa problemas em dois sistemas compartilhados distintos—middleboxes em software e escalonadores de tarefas de datacenters—e propõe maneiras de melhorar tanto a eficiĂŞncia como a justiça. Primeiro Ă© apresentado o sistema Sprayer, que usa espalhamento para direcionar pacotes entre os nĂşcleos em middleboxes em software. O Sprayer elimina os problemas de desbalanceamento causados pelas soluções baseadas em fluxos e lida com os novos desafios de manipular estados de fluxo, consequentes do espalhamento de pacotes. É mostrado que o Sprayer melhora a justiça de forma significativa e consegue usar toda a capacidade, mesmo quando há apenas um fluxo no sistema. Depois disso, Ă© apresentado o SDRF, uma polĂtica de alocação de tarefas para datacenters que considera as alocações passadas e garante justiça ao longo do tempo. Prova-se que o SDRF mantĂ©m as propriedades fundamentais do DRF—a polĂtica de alocação em que ele se baseia—enquanto beneficia os usuários com menor utilização. Para implementar o SDRF de forma eficiente, tambĂ©m Ă© introduzida a árvore viva, uma estrutura de dados genĂ©rica que mantĂ©m ordenados elementos cujas prioridades variam com o tempo. Simulações com dados reais indicam que o SDRF reduz o tempo de espera na mĂ©dia. Isso melhora a justiça, ao aumentar o nĂşmero de tarefas completas dos usuários com menor demanda, tendo um impacto pequeno nos usuários de maior demanda