Critical distributed applications have strict requirements over performance parameters, that may affect life of users. This is a limitation that may prevent the exploitation of cost effective solutions such as Cloud Computing (CC) based architectures: in fact, the quality of the connection with the CC facility and the lack of control on cloud resources may limit the overall performances of an application and may cause outages. A way to overcome the problem, and disclose the advantages of CC to critical applications, is provided by Edge Computing (EC). EC adds local support to CC, allowing a better distribution of application tasks according to their timeliness requirements. In this paper we present an innovative Special Weapons And Tactics (SWAT) support application, designed to empower effective operations in wide scenarios, that leverages EC to join CC elasticity and local immediateness, and we exploit Queuing Networks (QN) and Genetic Algorithms (GA) to design and optimize the system parameters for an effective workload distribution.

Performance optimization of edge computing homeland security support applications

Iacono M.
;
2018

Abstract

Critical distributed applications have strict requirements over performance parameters, that may affect life of users. This is a limitation that may prevent the exploitation of cost effective solutions such as Cloud Computing (CC) based architectures: in fact, the quality of the connection with the CC facility and the lack of control on cloud resources may limit the overall performances of an application and may cause outages. A way to overcome the problem, and disclose the advantages of CC to critical applications, is provided by Edge Computing (EC). EC adds local support to CC, allowing a better distribution of application tasks according to their timeliness requirements. In this paper we present an innovative Special Weapons And Tactics (SWAT) support application, designed to empower effective operations in wide scenarios, that leverages EC to join CC elasticity and local immediateness, and we exploit Queuing Networks (QN) and Genetic Algorithms (GA) to design and optimize the system parameters for an effective workload distribution.
Gribaudo, M.; Iacono, M.; Jakobik, A.; Kolodziej, J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/478850
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