Pournaras, E and Nikolic, J (2017) Self-Corrective Dynamic Networks via Decentralized Reverse Computations. In: 2017 IEEE International Conference on Autonomic Computing (ICAC). 2017 IEEE International Conference on Autonomic Computing (ICAC), 17-21 Jul 2017, Columbus, OH, USA. IEEE , pp. 11-20. ISBN 978-1-5386-1762-5
Abstract
The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation of aggregation functions, e.g. summation, are linked to the actual nodes connected in the network, for instance, counting the sum using input values from only connected nodes. Reverse computations are required to maintain a high aggregation accuracy when nodes leave or fail. This paper introduces an autonomic agent-based model for highly dynamic self-corrective networks using decentralized reverse computations. The model is generic and equips the nodes with the capability to disseminate connectivity status updates in the network. Highly resilient agents to the dynamic network migrate to remote nodes and orchestrate reverse computations for each node leave or failure. In contrast to related work, no other computational resources or redundancy are introduced. The self-corrective model is experimentally evaluated using real-world data from a smart grid pilot project under highly dynamic network adjustments that correspond to catastrophic events with up to 50% of the nodes leaving the network. The model is highly agile and modular and is applied to the large-scale decentralized aggregation network of DIAS, the Dynamic Intelligent Aggregation Service, without major structural changes in its design and operations. Results confirm the outstanding improvement in the aggregation accuracy when self-corrective actions are employed with a minimal increase in communication overhead.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper published in 2017 IEEE International Conference on Autonomic Computing (ICAC). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | self-correction; adaptation; accuracy; reverse computation; |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2020 12:58 |
Last Modified: | 26 Feb 2020 14:55 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icac.2017.30 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157099 |