Simulating Mobile Edge Computing Environment with Real Data Sets

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http://eprints.org/relation/isVersionOf https://researchdata.gla.ac.uk/id/document/3171 http://eprints.org/relation/isVolatileVersionOf https://researchdata.gla.ac.uk/id/document/3171 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.gla.ac.uk/id/document/3171 archive 6975 disk0/00/00/08/96 2019-10-09 15:24:23 2025-05-01 11:31:21 2019-10-09 15:24:23 data_collection show Alghamdi Ibrahim Ahmed I 42853 glaresearchdata:2019-10-09-896 Simulating Mobile Edge Computing Environment with Real Data Sets 30200000 This data set is a combination of two data sets available on line. The first data set is real data set of taxi cabs’ movements in Rome [1]. For each row in this data set, we have the cab-id, date/time and GPS coordinates of the current location. The focus was on 50 cars for one minute of movements. The goal of using this data set is to simulate the movements of the mobile nodes. To simulate the delay of the MEC servers, we used real data set for servers utilisation in a data centre taken from Alibaba Cluster Trace Program [2]. The data set, after the combining the two data sets contains the following fields: V1 (car id) , V2(time of movements), V3 (position), latitude, longitude, machine name, server Id, time of taken the measurement (utilization), cpu utilization, memory utilization and the sum of cpu and memory utilization. [1] Bracciale, L.; Bonola, M.; Loreti, P.; Bianchi, G.; Amici, R.; Rabuffi, A. CRAWDAD dataset roma/taxi (v.3932014-07-17). Downloaded from https://crawdad.org/roma/taxi/20140717, 2014. doi:10.15783/C7QC7M. [2] Alibaba Cluster Trace Program cluster-trace-v2018. Downloaded from https://github.com/alibaba/clusterdata/blob/master/cluster-trace-v2018/trace_2018.md 2019-10-09 published University of Glasgow 10.5525/gla.researchdata.896 Code Spreadsheet Text Ibrahim Ahmed I Alghamdi TRUE English 2029-10-09 R FALSE FALSE Delay-tolerant sequential decision making for task offloading in mobile edge computing environments https://eprints.gla.ac.uk/id/eprint/199204