SbQA: A Self-Adaptable Query Allocation Process

International audienceWe present a flexible query allocation framework, called {\it Satisfaction-based Query Allocation} (SbQA for short), for distributed information systems where both consumers and providers (the participants) have special interests towards queries. A particularity of SbQA is that it allocates queries while considering both query load and participants' interests. To be fair, it dynamically trades consumers' interests for providers' interests based on their satisfaction. In this demo we illustrate the flexibility and efficiency of SbQA to allocate queries on the {\it Berkeley Open Infrastructure for Network Computing} (BOINC). We also demonstrate that SbQA is self-adaptable to the participants' expectations. Finally, we demonstrate that SbQA can be adapted to different kinds of applications by varying its parameters

SbQA: Une Méthode Auto-Adaptative pour l'Allocation de Requêtes

National audienceWe present a flexible query allocation framework, called {\it Satisfaction-based Query Allocation} (SbQA for short), for distributed information systems where both consumers and providers (the participants) have special interests towards queries. A particularity of SbQA is that it allocates queries while considering both query load and participants' interests. To be fair, it dynamically trades consumers' interests for providers' interests based on their satisfaction. In this demo we illustrate the flexibility and efficiency of SbQA to allocate queries on the {\it Berkeley Open Infrastructure for Network Computing} (BOINC). We also demonstrate that SbQA is self-adaptable to the participants' expectations. Finally, we demonstrate that SbQA can be adapted to different kinds of applications by varying its parameters

Managing Virtual Money for Satisfaction and Scale Up in P2P Systems

International audienceIn peer-to-peer data management systems query allocation is a critical issue for the good operation of the system. This task is challenging because participants may prefer to perform some queries than others. Microeconomic mechanisms aim at dealing with this, but, to the best of our knowledge, none of them has ever proposed experimental validations that, beyond query load or response time, use measures that are outside the microeconomic scope. The contribution of this paper is twofold. We present a virtual money-based query allocation process that is suitable for large-scale super peer systems. We compare a non microeconomic mediation with micro-economic ones from a satisfaction point of view. The experimental results show that the providers' invoice phase is as much important as the providers' selection phase for a virtual money-based mediation

A Middleware framework for self-adaptive large scale distributed services

Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.Postprint (published version

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system

Technical debt-aware elasticity management in cloud computing environments

Elasticity is the characteristic of cloud computing that provides the underlying primitives to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of the economies of scale in the cloud, which refers to a drop in the average costs of these computing capacities as a result of the dynamic sharing capability. However, in practice, it is impossible to achieve elasticity adaptations that obtain perfect matches between resource supply and demand, which produces dynamic gaps at runtime. Moreover, elasticity is only a capability, and consequently it calls for a management process with far-sighted economics objectives to maximise the value of elasticity adaptations. Within this context, we advocate the use of an economics-driven approach to guide elasticity managerial decisions. We draw inspiration from the technical debt metaphor in software engineering and we explore it in a dynamic setting to present a debt-aware elasticity management. In particular, we introduce a managerial approach that assesses the value of elasticity decisions to adapt the resource provisioning. Additionally, the approach pursues strategic decisions that value the potential utility produced by the unavoidable gaps between the ideal and actual resource provisioning over time. As part of experimentation, we built a proof of concept and the results indicate that value-oriented adaptations in elasticity management lead to a better economics performance in terms of lower operating costs and higher quality of service over time. This thesis contributes (i) an economics-driven approach towards elasticity management; (ii) a technical debt-aware model to reason about elasticity adaptations; (iii) a debt-aware learning elasticity management approach; and (iv) a multi-agent elasticity management for multi-tenant applications hosted in the cloud

SISTEMI PER LA MOBILITÀ DEGLI UTENTI E DEGLI APPLICATIVI IN RETI WIRED E WIRELESS

The words mobility and network are found together in many contexts. The issue alone of modeling geographical user mobility in wireless networks has countless applications. Depending on one’s background, the concept is investigated with very different tools and aims. Moreover, the last decade saw also a growing interest in code mobility, i.e. the possibility for soft-ware applications (or parts thereof) to migrate and keeps working in different devices and environ-ments. A notable real-life and successful application is distributed computing, which under certain hypothesis can void the need of expensive supercomputers. The general rationale is splitting a very demanding computing task into a large number of independent sub-problems, each addressable by limited-power machines, weakly connected (typically through the Internet, the quintessence of a wired network). Following this lines of thought, we organized this thesis in two distinct and independent parts: Part I It deals with audio fingerprinting, and a special emphasis is put on the application of broadcast mon-itoring and on the implementation aspects. Although the problem is tackled from many sides, one of the most prominent difficulties is the high computing power required for the task. We thus devised and operated a distributed-computing solution, which is described in detail. Tests were conducted on the computing cluster available at the Department of Engineering of the University of Ferrara. Part II It focuses instead on wireless networks. Even if the approach is quite general, the stress is on WiFi networks. More specifically, we tried to evaluate how mobile-users’ experience can be improved. Two tools are considered. In the first place, we wrote a packet-level simulator and used it to esti-mate the impact of pricing strategies in allocating the bandwidth resource, finding out the need for such solutions. Secondly, we developed a high-level simulator that strongly advises to deepen the topic of user cooperation for the selection of the “best” point of access, when many are available. We also propose one such policy