Distributed Selfish Coaching

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

Efficiency of cache-replacement algorithms while retrieving data from a relational database and XML files in a web based system

Caching has been applied in Web based information systems in order toreduce the transmission of redundant network traffic and response latency by savingcopies of the content obtained from the Web closer to the end user. The efficiencyof caching is influenced to a significant extent by the cache replacement algorithmswhich are triggered when the cache becomes full and old objects must be evicted tomake space for the new ones.This paper presents a framework that can be used in future work to tunecache-replacement algorithms while data is simultaneously retrieved from arelational database and XML files in a web based environment, by a large numberof end-users. Three replacement policies are considered: Least Recently Used(LRU), Least Frequently Used (LFU) and Lowest Latency First (LLF). Theexperimental results obtained from the framework show that data caching greatlyimproves the overall performance of web based systems, and the type of the appliedcache replacement policy also plays an important role in the performance. In thescenarios considered in this paper, the LLF algorithm produced the bestperformance when retrieving data from a relational database, while the LFUalgorithm was the most efficient algorithm when data was retrieved from an XMLfile

A Software Approach to Unifying Multicore Caches

Multicore chips will have large amounts of fast on-chip cache memory, along with relatively slow DRAM interfaces. The on-chip cache memory, however, will be fragmented and spread over the chip; this distributed arrangement is hard for certain kinds of applications to exploit efficiently, and can lead to needless slow DRAM accesses. First, data accessed from many cores may be duplicated in many caches, reducing the amount of distinct data cached. Second, data in a cache distant from the accessing core may be slow to fetch via the cache coherence protocol. Third, software on each core can only allocate space in the small fraction of total cache memory that is local to that core. A new approach called software cache unification (SCU) addresses these challenges for applications that would be better served by a large shared cache. SCU chooses the on-chip cache in which to cache each item of data. As an application thread reads data items, SCU moves the thread to the core whose on-chip cache contains each item. This allows the thread to read the data quickly if it is already on-chip; if it is not, moving the thread causes the data to be loaded into the chosen on-chip cache. A new file cache for Linux, called MFC, uses SCU to improve performance of file-intensive applications, such as Unix file utilities. An evaluation on a 16-core AMD Opteron machine shows that MFC improves the throughput of file utilities by a factor of 1.6. Experiments with a platform that emulates future machines with less DRAM throughput per core shows that MFC will provide benefit to a growing range of applications.This material is based upon work supported by the National Science Foundation under grant number 0915164

Distribuição de conteúdos over-the-top multimédia em redes sem fios

mestrado em Engenharia Eletrónica e TelecomunicaçõesHoje em dia a Internet é considerada um bem essencial devido ao facto de haver uma constante necessidade de comunicar, mas também de aceder e partilhar conteúdos. Com a crescente utilização da Internet, aliada ao aumento da largura de banda fornecida pelos operadores de telecomunicações, criaram-se assim excelentes condições para o aumento dos serviços multimédia Over-The-Top (OTT), demonstrado pelo o sucesso apresentado pelos os serviços Netflix e Youtube. O serviço OTT engloba a entrega de vídeo e áudio através da Internet sem um controlo direto dos operadores de telecomunicações, apresentando uma proposta atractiva de baixo custo e lucrativa. Embora a entrega OTT seja cativante, esta padece de algumas limitações. Para que a proposta se mantenha em crescimento e com elevados padrões de Qualidade-de-Experiência (QoE) para os consumidores, é necessário investir na arquitetura da rede de distribuição de conteúdos, para que esta seja capaz de se adaptar aos diversos tipos de conteúdo e obter um modelo otimizado com um uso cauteloso dos recursos, tendo como objectivo fornecer serviços OTT com uma boa qualidade para o utilizador, de uma forma eficiente e escalável indo de encontro aos requisitos impostos pelas redes móveis atuais e futuras. Esta dissertação foca-se na distribuição de conteúdos em redes sem fios, através de um modelo de cache distribuída entre os diferentes pontos de acesso, aumentando assim o tamanho da cache e diminuindo o tráfego necessário para os servidores ou caches da camada de agregação acima. Assim, permite-se uma maior escalabilidade e aumento da largura de banda disponível para os servidores de camada de agregação acima. Testou-se o modelo de cache distribuída em três cenários: o consumidor está em casa em que se considera que tem um acesso fixo, o consumidor tem um comportamento móvel entre vários pontos de acesso na rua, e o consumidor está dentro de um comboio em alta velocidade. Testaram-se várias soluções como Redis2, Cachelot e Memcached para servir de cache, bem como se avaliaram vários proxies para ir de encontro ás características necessárias. Mais ainda, na distribuição de conteúdos testaram-se dois algoritmos, nomeadamente o Consistent e o Rendezvouz Hashing. Ainda nesta dissertação utilizou-se uma proposta já existente baseada na previsão de conteúdos (prefetching ), que consiste em colocar o conteúdo nas caches antes de este ser requerido pelos consumidores. No final, verificou-se que o modelo distribuído com a integração com prefecthing melhorou a qualidade de experiência dos consumidores, bem como reduziu a carga nos servidores de camada de agregação acima.Nowadays, the Internet is considered an essential good, due to the fact that there is a need to communicate, but also to access and share information. With the increasing use of the Internet, allied with the increased bandwidth provided by telecommunication operators, it has created conditions for the increase of Over-the-Top (OTT) Multimedia Services, demonstrated by the huge success of Net ix and Youtube. The OTT service encompasses the delivery of video and audio through the Internet without direct control of telecommunication operators, presenting an attractive low-cost and pro table proposal. Although the OTT delivery is captivating, it has some limitations. In order to increase the number of clients and keep the high Quality of Experience (QoE) standards, an enhanced architecture for content distribution network is needed. Thus, the enhanced architecture needs to provide a good quality for the user, in an e cient and scalable way, supporting the requirements imposed by future mobile networks. This dissertation aims to approach the content distribution in wireless networks, through a distributed cache model among the several access points, thus increasing the cache size and decreasing the load on the upstream servers. The proposed architecture was tested in three di erent scenarios: the consumer is at home and it is considered that it has a xed access, the consumer is mobile between several access points in the street, the consumer is in a high speed train. Several solutions were evaluated, such as Redis2, Cachelot and Memcached to serve as caches, along with the evaluation of several proxies server in order to ful ll the required features. Also, it was tested two distributed algorithms, namely the Consistent and Rendezvous Hashing. Moreover, in this dissertation it was integrated a prefetching mechanism, which consists of inserting the content in caches before being requested by the consumers. At the end, it was veri ed that the distributed model with prefetching improved the consumers QoE as well as it reduced the load on the upstream servers

In Pursuit of Desirable Equilibria in Large Scale Networked Systems

This thesis addresses an interdisciplinary problem in the context of engineering, computer science and economics: In a large scale networked system, how can we achieve a desirable equilibrium that benefits the system as a whole? We approach this question from two perspectives. On the one hand, given a system architecture that imposes certain constraints, a system designer must propose efficient algorithms to optimally allocate resources to the agents that desire them. On the other hand, given algorithms that are used in practice, a performance analyst must come up with tools that can characterize these algorithms and determine when they can be optimally applied. Ideally, the two viewpoints must be integrated to obtain a simple system design with efficient algorithms that apply to it. We study the design of incentives and algorithms in such large scale networked systems under three application settings, referred to herein via the subheadings: Incentivizing Sharing in Realtime D2D Networks: A Mean Field Games Perspective, Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids, Dynamic Adaptability Properties of Caching Algorithms, and Accuracy vs. Learning Rate of Multi-level Caching Algorithms. Our application scenarios all entail an asymptotic system scaling, and an equilibrium is defined in terms of a probability distribution over system states. The question in each case is to determine how to attain a probability distribution that possesses certain desirable properties. For the first two applications, we consider the design of specific mechanisms to steer the system toward a desirable equilibrium under self interested decision making. The environments in these problems are such that there is a set of shared resources, and a mechanism is used during each time step to allocate resources to agents that are selfish and interact via a repeated game. These models are motivated by resource sharing systems in the context of data communication, transportation, and power transmission networks. The objective is to ensure that the achieved equilibria are socially desirable. Formally, we show that a Mean Field Game can be used to accurately approximate these repeated game frameworks, and we describe mechanisms under which socially desirable Mean Field Equilibria exist. For the third application, we focus on performance analysis via new metrics to determine the value of the attained equilibrium distribution of cache contents when using different replacement algorithms in cache networks. The work is motivated by the fact that typical performance analysis of caching algorithms consists of determining hit probability under a fixed arrival process of requests, which does not account for dynamic variability of request arrivals. Our main contribution is to define a function which accounts for both the error due to time lag of learning the items' popularity, as well as error due to the inaccuracy of learning, and to characterize the tradeoff between the two that conventional algorithms achieve. We then use the insights gained in this exercise to design new algorithms that are demonstrably superior

On Applications of Relational Data

With the advances of technology and the popularity of the Internet, a large amount of data is being generated and collected. Much of these data is relational data, which describe how people and things, or entities, are related to one another. For example, data from sale transactions on e-commerce websites tell us which customers buy or view which products. Analyzing the known relationships from relational data can help us to discover knowledge that can benefit businesses, organizations, and our lives. For instance, learning the products that are commonly bought together allows businesses to recommend products to customers and increase their sales. Hidden or new relationships can also be inferred based on relational data. In addition, based on the connections among the entities, we can approximate the level of relatedness between two entities, even though their relationship may be hard to observe or quantify. This research aims to explore novel applications of relational data that will help to improve our life in various aspects, such as improving business operations, improving experiences in using online services, and improving health care services. In applying relational data in any domain, there are two common challenges. First, the size of the data can be massive, but many applications require that results are obtained within a short time. Second, relational data are often noisy and incomplete. Many relationships are extracted automatically from text resources, and hence they are prone to errors. Our goal is not only to propose novel applications of relational data but also to develop techniques and algorithms that will facilitate and make such applications practical. This work addresses three novel applications of relational data. The first application is to use relational data to improve user experiences in online video sharing services. Second, we propose the use of relational data to find entities that are closely related to one another. Such problems arise in various domains, such as product recommendation and query suggestion. Third, we propose the use of relational data to assist medical practitioners in drug prescription. For these applications, we introduce several techniques and algorithms to address the aforementioned challenges in using relational data. Our approaches are evaluated extensively to demonstrate their effectiveness. The approaches proposed in this work not only can be used in the specific applications we discuss but also can help to facilitate and promote the use of relational data in other application domains

Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability