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dc.contributor.authorThai, Minh-Tuanen_US
dc.contributor.authorLin, Ying-Daren_US
dc.contributor.authorLai, Yuan-Chengen_US
dc.contributor.authorChien, Hsu-Tungen_US
dc.date.accessioned2020-05-05T00:02:24Z-
dc.date.available2020-05-05T00:02:24Z-
dc.date.issued2020-03-01en_US
dc.identifier.issn1932-4537en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TNSM.2019.2937342en_US
dc.identifier.urihttp://hdl.handle.net/11536/154208-
dc.description.abstractA collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.en_US
dc.language.isoen_USen_US
dc.subjectCloud computingen_US
dc.subjectOptimizationen_US
dc.subjectComputer architectureen_US
dc.subjectEdge computingen_US
dc.subjectServersen_US
dc.subjectData centersen_US
dc.subjectDelaysen_US
dc.subjectcapacity optimizationen_US
dc.subjectedge computingen_US
dc.subjectfog computingen_US
dc.subjectoptimizationen_US
dc.subjectworkload offloadingen_US
dc.titleWorkload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloadingen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TNSM.2019.2937342en_US
dc.identifier.journalIEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENTen_US
dc.citation.volume17en_US
dc.citation.issue1en_US
dc.citation.spage227en_US
dc.citation.epage238en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電機資訊國際碩士學位學程zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentEECS International Graduate Program-Masteren_US
dc.identifier.wosnumberWOS:000520507200017en_US
dc.citation.woscount0en_US
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