Multi-access Edge Computing: A Survey

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Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.26(2018) (online)DOI http://dx.doi.org/10.2197/ipsjjip.26.87------------------------------

Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.26(2018) (online)DOI http://dx.doi.org/10.2197/ipsjjip.26.87------------------------------

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詳細情報

  • CRID
    1050282812886283136
  • NII論文ID
    170000149258
  • NII書誌ID
    AN00116647
  • ISSN
    18827764
  • Web Site
    http://id.nii.ac.jp/1001/00185748/
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • IRDB
    • CiNii Articles

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