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Item type:Publication, Access status: without files , Enabling fog computing using self-organizing compute nodes(2019-05); ; ; The emergence of fog computing has led to the design of multi-layer fog computing models which are organized hierarchically. These models commonly dictate the hierarchical structure to all the participating compute nodes. However, organizing the compute nodes by adding customized connections that do not abide by the hierarchical approach, may result in improved performance due to the network's properties i.e., latency or bandwidth between the nodes. For this reason, in this paper we propose an alternative to the hierarchical approach, which is the self-organizing compute nodes. These nodes organize themselves into a flat model which leverages on the network's properties to provide improved performance. The results of the evaluation show that this approach reduces bandwidth utilization (~30%) by using optimized messaging instead of direct messaging. Furthermore, we show that following a flat model, enables the design of mechanisms for fault tolerance which has been mostly neglected in existing hierarchical models.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimal placement of stream processing operators in the fogElastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network.In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.Publicationtype: Conference Paper43