Service fabric management in Fog Computing is a challenging task, which has to deal with a complex and resource scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management for Fog Computing applications. First, we formalize the VoI concept and discuss its adoption in Fog Computing environments. Then, we propose a formal model that aims at maximizing the allocation of Fog services from a value-based perspective. To overcome the complexity of this model, we present two possible approaches (simulation-based optimization and a model approximation) and we compare them by adopting Evolutionary Algorithms (EAs) as optimization techniques. Experimental results prove the validity of both models in finding resource allocation solutions capable of minimizing network latency and maximizing the utility for the end-users of Fog Computing services. Finally, we show how the results of the approximated model can be adopted as a first approximated approach for resource management of Fog Computing services.

Value of Information based Optimal Service Fabric Management for Fog Computing

Poltronieri F.
Primo
;
Tortonesi M.
Secondo
;
Morelli A.;Stefanelli C.
Penultimo
;
2020

Abstract

Service fabric management in Fog Computing is a challenging task, which has to deal with a complex and resource scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management for Fog Computing applications. First, we formalize the VoI concept and discuss its adoption in Fog Computing environments. Then, we propose a formal model that aims at maximizing the allocation of Fog services from a value-based perspective. To overcome the complexity of this model, we present two possible approaches (simulation-based optimization and a model approximation) and we compare them by adopting Evolutionary Algorithms (EAs) as optimization techniques. Experimental results prove the validity of both models in finding resource allocation solutions capable of minimizing network latency and maximizing the utility for the end-users of Fog Computing services. Finally, we show how the results of the approximated model can be adopted as a first approximated approach for resource management of Fog Computing services.
978-1-7281-4973-8
Fog Computing; Optimization Models; Service Management
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11392/2420493
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