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

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

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Main topics of DAI : a review

A new branch of artificial intelligence, distributed AI, has developed in the last years. Topic is the cooperation of AI-systems which are distributed among different autonomous agents. The thereby occuring problems extend the traditional AI spectrum and are presented along the major DAI-relevant topics: Knowledge representation, task-decomposition and -allocation, interaction and communication, cooperation, coordination and coherence, organizational models, agent\u27s modelling of other agents and conflict resolution strategies (e.g. negotiation). First we try to describe the role of DAI within AI. Then every subsection will take up one special aspect, illuminate the occurring problems and give links to solutions proposed in literature. Interlaced into this structure are sketchy descriptions of a few very prominent and influential DAI systems. In particular we present the Contract Net Protocol, the Distributed Vehicle Monitoring Testbed, the Air Traffic Control problem and the Blackboard Architecture

Knowledge visualizations: a tool to achieve optimized operational decision making and data integration

The overabundance of data created by modern information systems (IS) has led to a breakdown in cognitive decision-making. Without authoritative source data, commanders’ decision-making processes are hindered as they attempt to paint an accurate shared operational picture (SOP). Further impeding the decision-making process is the lack of proper interface interaction to provide a visualization that aids in the extraction of the most relevant and accurate data. Utilizing the DSS to present visualizations based on OLAP cube integrated data allow decision-makers to rapidly glean information and build their situation awareness (SA). This yields a competitive advantage to the organization while in garrison or in combat. Additionally, OLAP cube data integration enables analysis to be performed on an organization’s data-flows. This analysis is used to identify the critical path of data throughout the organization. Linking a decision-maker to the authoritative data along this critical path eliminates the many decision layers in a hierarchal command structure that can introduce latency or error into the decision-making process. Furthermore, the organization has an integrated SOP from which to rapidly build SA, and make effective and efficient decisions.http://archive.org/details/knowledgevisuali1094545877Outstanding ThesisOutstanding ThesisMajor, United States Marine CorpsCaptain, United States Marine CorpsApproved for public release; distribution is unlimited

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

Trade-off among timeliness, messages and accuracy for large-Ssale information management

The increasing amount of data and the number of nodes in large-scale environments require new techniques for information management. Examples of such environments are the decentralized infrastructures of Computational Grid and Computational Cloud applications. These large-scale applications need different kinds of aggregated information such as resource monitoring, resource discovery or economic information. The challenge of providing timely and accurate information in large scale environments arise from the distribution of the information. Reasons for delays in distributed information system are a long information transmission time due to the distribution, churn and failures. A problem of large applications such as peer-to-peer (P2P) systems is the increasing retrieval time of the information due to the decentralization of the data and the failure proneness. However, many applications need a timely information provision. Another problem is an increasing network consumption when the application scales to millions of users and data. Using approximation techniques allows reducing the retrieval time and the network consumption. However, the usage of approximation techniques decreases the accuracy of the results. Thus, the remaining problem is to offer a trade-off in order to solve the conflicting requirements of fast information retrieval, accurate results and low messaging cost. Our goal is to reach a self-adaptive decision mechanism to offer a trade-off among the retrieval time, the network consumption and the accuracy of the result. Self-adaption enables distributed software to modify its behavior based on changes in the operating environment. In large-scale information systems that use hierarchical data aggregation, we apply self-adaptation to control the approximation used for the information retrieval and reduces the network consumption and the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an accuracy of the results, while considering user’s defined priorities. First, this presented research addresses the problem of a trade-off among a timely information retrieval, accurate results and low messaging cost by proposing a summarization algorithm for resource discovery in P2P-content networks. After identifying how summarization can improve the discovery process, we propose an algorithm which uses a precision-recall metric to compare the accuracy and to offer a user-driven trade-off. Second, we propose an algorithm that applies a self-adaptive decision making on each node. The decision is about the pruning of the query and returning the result instead of continuing the query. The pruning reduces the retrieval time and the network consumption at the cost of a lower accuracy in contrast to continuing the query. The algorithm uses an analytic hierarchy process to assess the user’s priorities and to propose a trade-off in order to satisfy the accuracy requirements with a low message cost and a short delay. A quantitative analysis evaluates our presented algorithms with a simulator, which is fed with real data of a network topology and the nodes’ attributes. The usage of a simulator instead of the prototype allows the evaluation in a large scale of several thousands of nodes. The algorithm for content summarization is evaluated with half a million of resources and with different query types. The selfadaptive algorithm is evaluated with a simulator of several thousands of nodes that are created from real data. A qualitative analysis addresses the integration of the simulator’s components in existing market frameworks for Computational Grid and Cloud applications. The proposed content summarization algorithm reduces the information retrieval time from a logarithmic increase to a constant factor. Furthermore, the message size is reduced significantly by applying the summarization technique. For the user, a precision-recall metric allows defining the relation between the retrieval time and the accuracy. The self-adaptive algorithm reduces the number of messages needed from an exponential increase to a constant factor. At the same time, the retrieval time is reduced to a constant factor under an increasing number of nodes. Finally, the algorithm delivers the data with the required accuracy adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests entorns són les infraestructures descentralitzades de Computacional Grid i Cloud. Les aplicacions a gran escala necessiten diferents classes d’informació agregada com monitorització de recursos i informació econòmica. El desafiament de proporcionar una provisió ràpida i acurada d’informació en ambients de grans escala sorgeix de la distribució de la informació. Una raó és que el sistema d’informació ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients. Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P) és el creixent temps de recuperació de l’informació a causa de la descentralització de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten inexactituds dels resultats si la informació es reparteix a temps. A més i més, el consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís per resoldre els requisits en conflicte d’extracció de la informació ràpida, resultats acurats i cost d’enviament baix. El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu per tal d’oferir el compromís entre temps de recuperació, consum de xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes d’informació de gran escala que utilitzen agregació de dades jeràrquica, l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada definida per l’usari. La recerca que es presenta, introdueix un algoritme de sumarització de continguts per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar com sumarització pot millorar el procés de descoberta, proposem una mètrica que s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després, introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer els requisits de precisió amb un cost de missatge baix i un retard curt. Basat en les prioritats d’usuari, l’algoritme troba automàticament un compromís. L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de sumarització de contingut s’avalua amb mig milió de recursos i amb diferents tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del simulador en estructures de mercat existents per a aplicacions de Computacional Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés d’agregació i la query language) és comprovada per la integració de prototips. L’algoritme de sumarització de contingut proposat redueix el temps d’extracció de l’informació d’un augment logarítmic a un factor constant. A més, també permet que la mida del missatge es redueix significativament. Per a l’usuari, una precision-recall mètric permet definir la relació entre el nivell de precisió i el temps d’extracció de la informació. Alhora, el temps de recuperació es redueix a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació de gran escala en el futur

Efficient Spectrum Management for Mobile Ad Hoc Networks

The successful deployment of advanced wireless network applications for defense, homeland security, and public safety depends on the availability of relatively interference-free spectrum. Setup and maintenance of mobile networks for military and civilian first-response units often requires temporary allocation of spectrum resources for operations of finite, but uncertain, duration. As currently practiced, this is a very labor-intensive process with direct parallels to project management. Given the wide range of real-time local variation in propagation conditions, spatial distribution of nodes, and evolving technical and mission priorities current human-in-the loop conflict resolution approaches seem untenable. If the conventional radio regulatory structure is strictly adhered to, demand for spectrum will soon exceed supply. Software defined radio is one technology with potential to exploit local inefficiencies in spectrum usage, but questions regarding the management of such network have persisted for years. This dissertation examines a real-time spectrum distribution approach that is based on principles of economic utility and equilibrium among multiple competitors for limited goods in a free market. The spectrum distribution problem may be viewed as a special case of multi-objective optimization of a constrained resource. A computer simulation was developed to create hundreds of cases of local spectrum crowding, to which simultaneous perturbation simulated annealing (SPSA) was applied as a nominal optimization algorithm. Two control architectures were modeled for comparison, one requiring a local monitoring infrastructure and coordination ("top down") the other more market based ("bottom up"). The analysis described herein indicates that in both cases "hands-off" local spectrum management by trusted algorithms is not only feasible, but that conditions of entry for new networks may be determined a priori, with a degree of confidence described by relatively simple algebraic formulas