Coflow-Like Online Data Acquisition from Low-Earth-Orbit Datacenters
| dc.contributor.author | Huang, Huawei | |
| dc.contributor.author | Guo, Song | |
| dc.contributor.author | Liang, Weifa | |
| dc.contributor.author | Wang, Kun | |
| dc.contributor.author | Okabe, Yasuo | |
| dc.date.accessioned | 2023-08-09T02:28:51Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2022-07-24T08:18:04Z | |
| dc.description.abstract | Satellite-based communication technology has gained much attention in the past few years, where satellites play mainly the supplementary roles as relay devices to terrestrial communication networks. Unlike previous work, we treat the low-earth-orbit (LEO) satellites as secure data storage mediums. We focus on data acquisition from a LEO satellite based data storage system (also referred to as the LEO based datacenters), which has been considered as a promising and secure paradigm on data storage. Under the LEO based datacenter architecture, one fundamental challenge is to deal with energy-efficient downloading from space to ground while maintaining the system stability. In this paper, we aim to maximize the amount of data admitted while minimizing the energy consumption, when downloading files from LEO based datacenters to meet user demands. To this end, we first formulate a novel optimization problem and develop an online scheduling framework. We then devise a novel coflow-like 'Join the first KK-shortest Queues (JKQ)' based job-dispatch strategy, which can significantly lower backlogs of queues residing in LEO satellites, thereby improving the system stability. We also analyze the optimality of the proposed approach and system stability. We finally evaluate the performance of the proposed algorithm through conducting emulator based simulations, based on real-world LEO constellation and user demand traces. The simulation results show that the proposed algorithm can dramatically lower the queue backlogs and achieve high energy efficiency. | en_AU |
| dc.description.sponsorship | This work is partially supported by NSFC under research grant no. 61872310, 61872195, 61572262, the Kyoto University Research Fund for Young Scientists (Start-up) FY-2018, and the Start-up Research Fund (67000-18841220) from Sun Yat-Sen University, China. | en_AU |
| dc.format.mimetype | application/pdf | en_AU |
| dc.identifier.issn | 1536-1233 | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/295470 | |
| dc.language.iso | en_AU | en_AU |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | en_AU |
| dc.rights | © 2020 IEEE | en_AU |
| dc.source | IEEE Transactions on Mobile Computing | en_AU |
| dc.subject | LEO-based datacenter | en_AU |
| dc.subject | online job-scheduling | en_AU |
| dc.subject | coflow | en_AU |
| dc.subject | drift-plus-penalty | en_AU |
| dc.subject | energy efficiency | en_AU |
| dc.subject | queue stability | en_AU |
| dc.title | Coflow-Like Online Data Acquisition from Low-Earth-Orbit Datacenters | en_AU |
| dc.type | Journal article | en_AU |
| local.bibliographicCitation.issue | 12 | en_AU |
| local.bibliographicCitation.lastpage | 2760 | en_AU |
| local.bibliographicCitation.startpage | 2743 | en_AU |
| local.contributor.affiliation | Huang, Huawei, Sun Yat-Sen University | en_AU |
| local.contributor.affiliation | Guo, Song, Hong Kong Polytechnic University | en_AU |
| local.contributor.affiliation | Liang, Weifa, College of Engineering and Computer Science, ANU | en_AU |
| local.contributor.affiliation | Wang, Kun, University of California at Los Angeles | en_AU |
| local.contributor.affiliation | Okabe, Yasuo, Kyoto University | en_AU |
| local.contributor.authoruid | Liang, Weifa, u9404892 | en_AU |
| local.description.embargo | 2099-12-31 | |
| local.description.notes | Imported from ARIES | en_AU |
| local.identifier.absfor | 460601 - Cloud computing | en_AU |
| local.identifier.absfor | 460609 - Networking and communications | en_AU |
| local.identifier.ariespublication | a383154xPUB15955 | en_AU |
| local.identifier.citationvolume | 19 | en_AU |
| local.identifier.doi | 10.1109/TMC.2019.2936202 | en_AU |
| local.identifier.scopusID | 2-s2.0-85095804741 | |
| local.identifier.thomsonID | WOS:000587912300002 | |
| local.publisher.url | https://www.ieee.org/ | en_AU |
| local.type.status | Published Version | en_AU |
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