MiGrror: Mitigating Downtime in Mobile Edge Computing, An Extension to Live Migration
Procedia Computer Science, ISSN: 1877-0509, Vol: 203, Page: 41-50
2022
- 9Citations
- 20Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1877050922006135; http://dx.doi.org/10.1016/j.procs.2022.07.008; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85141730596&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1877050922006135; https://dx.doi.org/10.1016/j.procs.2022.07.008
Elsevier BV
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