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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
Performance Optimisation Of Edge Computing Homeland Security Support
Applications |
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Authors: |
Marco
Gribaudo, Mauro Iacono, Agnieszka Jakobik, Joanna Kolodziej |
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Published in:
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(2018). ECMS 2018
Proceedings Edited by: Lars Nolle, Alexandra
Burger, Christoph Tholen,
Jens Werner, Jens Wellhausen European Council for
Modeling and Simulation. doi:
10.7148/2018-0005 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) 32nd European Conference on Modelling and Simulation, Wilhelmshaven, Germany, May 22nd
– May 265h, 2018 |
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Citation
format: |
Marco
Gribaudo, Mauro Iacono, Agnieszka Jakobik, Joanna Kolodziej (2018). Performance Optimisation Of Edge Computing Homeland Security Support
Applications, ECMS 2018 Proceedings Edited
by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner,
Jens Wellhausen European Council for Modeling and
Simulation. doi:
10.7148/2018-0440 |
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DOI: |
https://doi.org/10.7148/2018-0440 |
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Abstract: |
Critical distributed applications have strict
require-ments 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 qual-ity of the connection with the CC facility and the
lack of control on cloud resources may limit the overall per-formances of an application and may cause outages. A way
to overcome the problem, and disclose the ad-vantages of CC to critical
applications, is provided by Edge Computing (EC). EC adds local support to
CC, allowing a better distribution of application tasks ac-cording to their
timeliness requirements. In this paper we present an innovative Special
Weapons And Tac-tics (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 Algo-rithms (GA) to design and optimize the system param-eters for an effective workload
distribution. |
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Full
text: |
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