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 últimas décadas, sistemas compartilhados contribuíram para a popularidade de muitas tecnologias. Desde Sistemas Operacionais até a Internet, esses sistemas trouxeram economias significativas ao permitir que a infraestrutura subjacente fosse compartilhada. Um desafio comum a esses sistemas é garantir que os recursos sejam divididos de forma justa, sem comprometer a eficiência 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 eficiência como a justiça. Primeiro é apresentado o sistema Sprayer, que usa espalhamento para direcionar pacotes entre os núcleos 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, é apresentado o SDRF, uma política de alocação de tarefas para datacenters que considera as alocações passadas e garante justiça ao longo do tempo. Prova-se que o SDRF mantém as propriedades fundamentais do DRF—a política de alocação em que ele se baseia—enquanto beneficia os usuários com menor utilização. Para implementar o SDRF de forma eficiente, também é introduzida a árvore viva, uma estrutura de dados genérica que mantém ordenados elementos cujas prioridades variam com o tempo. Simulações com dados reais indicam que o SDRF reduz o tempo de espera na média. Isso melhora a justiça, ao aumentar o número de tarefas completas dos usuários com menor demanda, tendo um impacto pequeno nos usuários de maior demanda

Scheduling multiple bags of tasks on heterogeneous master- worker platforms: centralized versus distributed solutions

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper we consider the problem of scheduling applications to ensure fair and efficient execution on master-worker platforms where the communication is restricted to a tree embedded in the network. The goal of the scheduling is to obtain the best throughput while enforcing some fairness between applications. We show how to derive an asymptotically optimal periodic schedule by solving a linear program expressing all problem constraints. For single-level trees, the optimal solution can be analytically computed. For large-scale platforms, gathering the global knowledge needed by the linear programming approach might be unrealistic. One solution is to adapt the multi-commodity flow algorithm of Awerbuch and Leighton, but it still requires some global knowledge. Thus, we also investigates heuristic solutions using only local information, and test them via simulations. The best of our heuristics achieves the optimal performance on about two-thirds of our test cases, but is far worse in a few cases

REQUIREMENT- AWARE STRATEGIES FOR SCHEDULING MULTIPLE DIVISIBLE LOADS IN CLUSTER ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Dealing With Misbehavior In Distributed Systems: A Game-Theoretic Approach

Most distributed systems comprise autonomous entities interacting with each other to achieve their objectives. These entities behave selfishly when making decisions. This behavior may result in strategical manipulation of the protocols thus jeopardizing the system wide goals. Micro-economics and game theory provides suitable tools to model such interactions. We use game theory to model and study three specific problems in distributed systems. We study the problem of sharing the cost of multicast transmissions and develop mechanisms to prevent cheating in such settings. We study the problem of antisocial behavior in a scheduling mechanism based on the second price sealed bid auction. We also build models using extensive form games to analyze the interactions of the attackers and the defender in a security game involving honeypots. Multicast cost sharing is an important problem and very few distributed strategyproof mechanisms exist to calculate the costs shares of the users. These mechanisms are susceptible to manipulation by rational nodes. We propose a faithful mechanism which uses digital signatures and auditing to catch and punish the cheating nodes. Such mechanism will incur some overhead. We deployed the proposed and existing mechanisms on planet-lab to experimentally analyze the overhead and other relevant economic properties of the proposed and existing mechanisms. In a second price sealed bid auction, even though the bids are sealed, an agent can infer the private values of the winning bidders, if the auction is repeated for related items. We study this problem from the perspective of a scheduling mechanism and develop an antisocial strategy which can be used by an agent to inflict losses on the other agents. In a security system attackers and defender(s) interact with each other. Examples of such systems are the honeynets which are used to map the activities of the attackers to gain valuable insight about their behavior. The attackers want to evade the honeypots while the defenders want them to attack the honeypots. These interesting interactions form the basis of our research where we develop a model used to analyze the interactions of an attacker and a honeynet system

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