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

Virtualization And Self-organization For Utility Computing

We present an alternative paradigm for utility computing when the delivery of service is subject to binding contracts; the solution we propose is based on resource virtualization and a selfmanagement scheme. A virtual cloud aggregates set virtual machines to work in concert for the tasks specified by the service agreement. A first step for the establishment of a virtual cloud is to create a scale-free overlay network through a biased random walk; scale-free networks enjoy a set of remarkable properties such as: robustness against random failures, favorable scaling, and resilience to congestion, small diameter, and average path length. Constrains such as limits on the cost of per unit of service, total cost, or the requirement to use only “green computing cycles are then considered when a node of this overlay network decides whether to join the virtual cloud or not. A VIRTUAL CLOUD consists of a subset of the nodes assigned to the tasks specified by a Service Level Agreement, SLA, as well as a virtual interconnection network, or overlay network, for the virtual cloud. SLAs could serve as a congestion control mechanism for an organization providing utility computing; this mechanism allows the system to reject new contracts when there is the danger of overloading the system and failing to fulfill existing contractual obligations. The objective of this thesis is to show that biased random walks in power law networks are capable of responding to dynamic changes of the workload in utility computing

Robust and efficient membership management in large-scale dynamic networks

Epidemic protocols are a bio-inspired communication and computation paradigm for large-scale networked systems based on randomised communication. These protocols rely on a membership service to build decentralised and random overlay topologies. In large-scale, dynamic network environments, node churn and failures may have a detrimental effect on the structure of the overlay topologies with negative impact on the efficiency and the accuracy of applications. Most importantly, there exists the risk of a permanent loss of global connectivity that would prevent the correct convergence of applications. This work investigates to what extent a dynamic network environment may negatively affect the performance of Epidemic membership protocols. A novel Enhanced Expander Membership Protocol (EMP+) based on the expansion properties of graphs is presented. The proposed protocol is evaluated against other membership protocols and the comparative analysis shows that EMP+ can support faster application convergence and is the first membership protocol to provide robustness against global network connectivity problems

Spectra: Robust Estimation of Distribution Functions in Networks

Distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system. Simple aggregates such as minima/maxima, counts, sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates of the values on the network. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties, namely: robust when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property, not requiring algorithm restarts, and is highly resilient to node churn. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.Comment: Full version of the paper published at 12th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Stockholm (Sweden), June 201

Application of overlay techniques to network monitoring

Measurement and monitoring are important for correct and efficient operation of a network, since these activities provide reliable information and accurate analysis for characterizing and troubleshooting a network’s performance. The focus of network measurement is to measure the volume and types of traffic on a particular network and to record the raw measurement results. The focus of network monitoring is to initiate measurement tasks, collect raw measurement results, and report aggregated outcomes. Network systems are continuously evolving: besides incremental change to accommodate new devices, more drastic changes occur to accommodate new applications, such as overlay-based content delivery networks. As a consequence, a network can experience significant increases in size and significant levels of long-range, coordinated, distributed activity; furthermore, heterogeneous network technologies, services and applications coexist and interact. Reliance upon traditional, point-to-point, ad hoc measurements to manage such networks is becoming increasingly tenuous. In particular, correlated, simultaneous 1-way measurements are needed, as is the ability to access measurement information stored throughout the network of interest. To address these new challenges, this dissertation proposes OverMon, a new paradigm for edge-to-edge network monitoring systems through the application of overlay techniques. Of particular interest, the problem of significant network overheads caused by normal overlay network techniques has been addressed by constructing overlay networks with topology awareness - the network topology information is derived from interior gateway protocol (IGP) traffic, i.e. OSPF traffic, thus eliminating all overlay maintenance network overhead. Through a prototype that uses overlays to initiate measurement tasks and to retrieve measurement results, systematic evaluation has been conducted to demonstrate the feasibility and functionality of OverMon. The measurement results show that OverMon achieves good performance in scalability, flexibility and extensibility, which are important in addressing the new challenges arising from network system evolution. This work, therefore, contributes an innovative approach of applying overly techniques to solve realistic network monitoring problems, and provides valuable first hand experience in building and evaluating such a distributed system

Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays

In this paper, we discuss on the use of self-organizing protocols to improve the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar approaches are studied, which are based on local knowledge of the nodes' 2nd neighborhood. The first scheme is a simple protocol requiring interactions among nodes and their direct neighbors. The second scheme adds a check on the Edge Clustering Coefficient (ECC), a local measure that allows determining edges connecting different clusters in the network. The performed simulation assessment evaluates these protocols over uniform networks, clustered networks and scale-free networks. Different failure modes are considered. Results demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking and Applications. The final publication is available at Springer via http://dx.doi.org/10.1007/s12083-015-0384-

LHView: Location Aware Hybrid Partial View

The rise of the Cloud creates enormous business opportunities for companies to provide global services, which requires applications supporting the operation of those services to scale while minimizing maintenance costs, either due to unnecessary allocation of resources or due to excessive human supervision and administration. Solutions designed to support such systems have tackled fundamental challenges from individual component failure to transient network partitions. A fundamental aspect that all scalable large systems have to deal with is the membership of the system, i.e, tracking the active components that compose the system. Most systems rely on membership management protocols that operate at the application level, many times exposing the interface of a logical overlay network, that should guarantee high scalability, efficiency, and robustness. Although these protocols are capable of repairing the overlay in face of large numbers of individual components faults, when scaling to global settings (i.e, geo-distributed scenarios), this robustness is a double edged-sword because it is extremely complex for a node in a system to distinguish between a set of simultaneously node failures and a (transient) network partition. Thus the occurrence of a network partition creates isolated sub-sets of nodes incapable of reconnecting even after the recovery from the partition. This work address this challenges by proposing a novel datacenter-aware membership protocol to tolerate network partitions by applying existing overlay management techniques and classification techniques that may allow the system to efficiently cope with such events without compromising the remaining properties of the overlay network. Furthermore, we strive to achieve these goals with a solution that requires minimal human intervention