744 research outputs found

    A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

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    Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

    Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

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    The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

    The impact of microservices: an empirical analysis of the emerging software architecture

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    Dissertação de mestrado em Informatics EngineeringThe applications’ development paradigm has faced changes in recent years, with modern development being characterized by the need to continuously deliver new software iterations. With great affinity with those principles, microservices is a software architecture which features characteristics that potentially promote multiple quality attributes often required by modern, large-scale applications. Its recent growth in popularity and acceptance in the industry made this architectural style often described as a form of modernizing applications that allegedly solves all the traditional monolithic applications’ inconveniences. However, there are multiple worth mentioning costs associated with its adoption, which seem to be very vaguely described in existing empirical research, being often summarized as "the complexity of a distributed system". The adoption of microservices provides the agility to achieve its promised benefits, but to actually reach them, several key implementation principles have to be honored. Given that it is still a fairly recent approach to developing applications, the lack of established principles and knowledge from development teams results in the misjudgment of both costs and values of this architectural style. The outcome is often implementations that conflict with its promised benefits. In order to implement a microservices-based architecture that achieves its alleged benefits, there are multiple patterns and methodologies involved that add a considerable amount of complexity. To evaluate its impact in a concrete and empirical way, one same e-commerce platform was developed from scratch following a monolithic architectural style and two architectural patterns based on microservices, featuring distinct inter-service communication and data management mechanisms. The effort involved in dealing with eventual consistency, maintaining a communication infrastructure, and managing data in a distributed way portrayed significant overheads not existent in the development of traditional applications. Nonetheless, migrating from a monolithic architecture to a microservicesbased is currently accepted as the modern way of developing software and this ideology is not often contested, nor the involved technical challenges are appropriately emphasized. Sometimes considered over-engineering, other times necessary, this dissertation contributes with empirical data from insights that showcase the impact of the migration to microservices in several topics. From the trade-offs associated with the use of specific patterns, the development of the functionalities in a distributed way, and the processes to assure a variety of quality attributes, to performance benchmarks experiments and the use of observability techniques, the entire development process is described and constitutes the object of study of this dissertation.O paradigma de desenvolvimento de aplicações tem visto alterações nos últimos anos, sendo o desenvolvimento moderno caracterizado pela necessidade de entrega contínua de novas iterações de software. Com grande afinidade com esses princípios, microsserviços são uma arquitetura de software que conta com características que potencialmente promovem múltiplos atributos de qualidade frequentemente requisitados por aplicações modernas de grandes dimensões. O seu recente crescimento em popularidade e aceitação na industria fez com que este estilo arquitetural se comumente descrito como uma forma de modernizar aplicações que alegadamente resolve todos os inconvenientes apresentados por aplicações monolíticas tradicionais. Contudo, existem vários custos associados à sua adoção, aparentemente descritos de forma muito vaga, frequentemente sumarizados como a "complexidade de um sistema distribuído". A adoção de microsserviços fornece a agilidade para atingir os seus benefícios prometidos, mas para os alcançar, vários princípios de implementação devem ser honrados. Dado que ainda se trata de uma forma recente de desenvolver aplicações, a falta de princípios estabelecidos e conhecimento por parte das equipas de desenvolvimento resulta em julgamentos errados dos custos e valores deste estilo arquitetural. O resultado geralmente são implementações que entram em conflito com os seus benefícios prometidos. De modo a implementar uma arquitetura baseada em microsserviços com os benefícios prometidos existem múltiplos padrões que adicionam considerável complexidade. De modo a avaliar o impacto dos microsserviços de forma concreta e empírica, foi desenvolvida uma mesma plataforma e-commerce de raiz segundo uma arquitetura monolítica e duas arquitetura baseadas em microsserviços, contando com diferentes mecanismos de comunicação entre os serviços. O esforço envolvido em lidar com consistência eventual, manter a infraestrutura de comunicação e gerir os dados de uma forma distribuída representaram desafios não existentes no desenvolvimento de aplicações tradicionais. Apesar disso, a ideologia de migração de uma arquitetura monolítica para uma baseada em microsserviços é atualmente aceite como a forma moderna de desenvolver aplicações, não sendo frequentemente contestada nem os seus desafios técnicos são apropriadamente enfatizados. Por vezes considerado overengineering, outras vezes necessário, a presente dissertação visa contribuir com dados práticos relativamente ao impacto da migração para arquiteturas baseadas em microsserviços em diversos tópicos. Desde os trade-offs envolvidos no uso de padrões específicos, o desenvolvimento das funcionalidades de uma forma distribuída e nos processos para assegurar uma variedade de atributos de qualidade, até análise de benchmarks de performance e uso de técnicas de observabilidade, todo o desenvolvimento é descrito e constitui o objeto de estudo da dissertação

    Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

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    With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

    Towards Autonomous Computer Networks in Support of Critical Systems

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models

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    Complexity science aims to better understand the processes of both natural and man-made systems which are composed of many interacting entities at different scales. A disaggregated approach is proposed for simulating electricity systems, by using agent-based models coupled to continuous ones. The approach can help in acquiring a better understanding of the operation of the system itself, e.g. on emergent phenomena or scale effects; as well as in the improvement and design of future smart grids

    Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

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    Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device's capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we pro- pose a work ow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of work ow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from di_erent fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner

    Towards Autonomic Network Management: an Analysis of Current and Future Research Directions

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    A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

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    Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio
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