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Computers 2018, 7(2), 36;

Improving Efficiency of Edge Computing Infrastructures through Orchestration Models

Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, Via Opera Pia 13, 16145 Genova, Italy
S3ITI Lab, National Inter-University Consortium for Telecommunications (CNIT), Via Opera Pia 13, 16145 Genova, Italy
This paper is an extended version of our paper published in Carrega, A.; Portomauro, G.; Repetto, M.; Robino, G. OpenStack extensions for QoS and energy efficiency in edge computing. In Proceedings of the 3rd IEEE International Conference on Fog and Edge Mobile Computing (FMEC 2018), Barcelona, Spain, 23–26 April 2018.
Author to whom correspondence should be addressed.
Received: 22 May 2018 / Revised: 14 June 2018 / Accepted: 14 June 2018 / Published: 20 June 2018
(This article belongs to the Special Issue Mobile Edge Computing)
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Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving. View Full-Text
Keywords: energy efficiency; QoS; edge computing energy efficiency; QoS; edge computing

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Bolla, R.; Carrega, A.; Repetto, M.; Robino, G. Improving Efficiency of Edge Computing Infrastructures through Orchestration Models . Computers 2018, 7, 36.

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