Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transpor
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Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems Fatos Xhafa1
· Alhassan Aly1 · Angel A. Juan2
Received: 24 October 2020 / Accepted: 6 November 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements.
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Fatos Xhafa [email protected] Alhassan Aly [email protected] Angel A. Juan [email protected]
1
Universitat Politècnica de Catalunya, Barcelona, Spain
2
Universitat Oberta de Catalunya, Barcelona, Spain
123
F. Xhafa et al.
Keywords Fog computing · Optimization · Allocation · Clustering · Semantic computing · Smart logistics · Intelligent transportation systems Mathematics Subject Classification 68Uxx · 68Txx · 90Cxx
1 Introduction A number of computing paradigms and technologies have emerged after IoT and Cloud computing. Indeed, the view of IoT Cloud, in which IoT devices are directly connected to Cloud platforms and Data centers has shifted to a continuum view, where thereby various computing layers sit in between of IoT and Cloud. These layers referred to as Edge and Fog computing aim to retain the processing of data close to the source where the data is generated. Besides significantly alleviating the computing burden to the Cloud, such layers enable faster round-trip time processing and better support to end users. Most importantly, by processing data close to data sources and to end-users, real tim
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