SUMMARYAutomatic construction of workflows on the Grid is a challenging task. The problems that have to be solved are manifold: How can existing services be integrated into a workflow that is able to accomplish a specific task? How can an optimal workflow be constructed with respect to changing resource characteristics during the optimization process? How to cope with dynamically changing or incomplete knowledge of the goal function of the optimization process? and finally: How to react to service failures during workflow execution? In this paper, we propose a method to optimize a workflow based on a heuristic A * approach that allows to react to dynamics in the environment. Changes in the Grid infrastructure and in the users' requirements can be handled during the optimization process as well as during the execution of the workflow. Our algorithm also allows the workflow to recover from failing resources during the execution phase.
For business workflow automation in a service-enriched environment such as a grid or a cloud, services scattered across heterogeneous Virtual Organisations (VOs) can be aggregated in a producer-consumer manner, building hierarchical structures of added value. In order to preserve the supply chain, the Service Level Agreements (SLAs) corresponding to the underlying choreography of services should also be incrementally aggregated. This cross-VO hierarchical SLA aggregation requires validation, for which a distributed trust system becomes a prerequisite. Elaborating our previous work on rule-based SLA validation, we propose a hybrid distributed trust model. This new model is based on Public Key Infrastructure (PKI) and reputation-based trust systems. It helps preventing SLA violations by identifying violation-prone services at service selection stage and actively contributes in breach management at the time of penalty enforcement.
With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.
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