Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications
In this paper, we consider the problem of task offloading in a software-defined access network, where IoT devices are connected to fog computing nodes by multi-hop IoT access-points (APs). The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decis...
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| Veröffentlicht in: | IEEE journal on selected areas in communications 2019-05, Vol.37 (5), p.1159-1166 |
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| container_title | IEEE journal on selected areas in communications |
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| creator | Misra, Sudip Saha, Niloy |
| description | In this paper, we consider the problem of task offloading in a software-defined access network, where IoT devices are connected to fog computing nodes by multi-hop IoT access-points (APs). The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decision on local or remote task computation; 2) optimal fog node selection; and 3) optimal path selection for offloading. Accordingly, we formulate the multi-hop task offloading problem as an integer linear program (ILP). Since the feasible set is non-convex, we propose a greedy-heuristic-based approach to efficiently solve the problem. The greedy solution takes into account delay, energy consumption, multi-hop paths, and dynamic network conditions, such as link utilization and SDN rule-capacity. Experimental results show that the proposed scheme is capable of reducing the average delay and energy consumption by 12% and 21%, respectively, compared with the state of the art. |
| doi_str_mv | 10.1109/JSAC.2019.2906793 |
| format | Article |
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The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decision on local or remote task computation; 2) optimal fog node selection; and 3) optimal path selection for offloading. Accordingly, we formulate the multi-hop task offloading problem as an integer linear program (ILP). Since the feasible set is non-convex, we propose a greedy-heuristic-based approach to efficiently solve the problem. The greedy solution takes into account delay, energy consumption, multi-hop paths, and dynamic network conditions, such as link utilization and SDN rule-capacity. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-aab863587bb68a567792915365792c9b4828538b7c76687024ba343703744d683</citedby><cites>FETCH-LOGICAL-c293t-aab863587bb68a567792915365792c9b4828538b7c76687024ba343703744d683</cites><orcidid>0000-0002-2467-6414 ; 0000-0002-5955-4994</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8672614$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8672614$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Misra, Sudip</creatorcontrib><creatorcontrib>Saha, Niloy</creatorcontrib><title>Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications</title><title>IEEE journal on selected areas in communications</title><addtitle>J-SAC</addtitle><description>In this paper, we consider the problem of task offloading in a software-defined access network, where IoT devices are connected to fog computing nodes by multi-hop IoT access-points (APs). 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Experimental results show that the proposed scheme is capable of reducing the average delay and energy consumption by 12% and 21%, respectively, compared with the state of the art.</description><subject>Cloud computing</subject><subject>Computation offloading</subject><subject>Computer architecture</subject><subject>Delay</subject><subject>Delays</subject><subject>Edge computing</subject><subject>Electronic devices</subject><subject>Energy consumption</subject><subject>fog computing</subject><subject>Heuristic methods</subject><subject>Internet of Things</subject><subject>quality-of-service</subject><subject>Servers</subject><subject>Software</subject><subject>Software-defined networking</subject><subject>Task analysis</subject><subject>task offloading</subject><issn>0733-8716</issn><issn>1558-0008</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOAjEUhhujiYg-gHHTxPVg7xd3BEQxJCzAddMZWlKE6dgOMby9M4G4Omfx_efyAfCI0QhjpF8-V-PJiCCsR0QjITW9AgPMuSoQQuoaDJCktFASi1twl_MOIcyYIgOwmLo2HtMrnJ5qewgVXNv8DZfe76PdhHoLQw1X0be_Nrli6nyo3QbO4hb6mOA8ruG4afahsm2Idb4HN97us3u41CH4mr2tJx_FYvk-n4wXRUU0bQtrSyUoV7IshbJcSKmJxpwK3jWVLrvDFKeqlJUUQklEWGkpoxJRydhGKDoEz-e5TYo_R5dbs-t-qLuVhhDc-6BadxQ-U1WKOSfnTZPCwaaTwcj0kOmlmV6auUjrMk_nTHDO_fNKSCIwo38PPWUI</recordid><startdate>20190501</startdate><enddate>20190501</enddate><creator>Misra, Sudip</creator><creator>Saha, Niloy</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decision on local or remote task computation; 2) optimal fog node selection; and 3) optimal path selection for offloading. Accordingly, we formulate the multi-hop task offloading problem as an integer linear program (ILP). Since the feasible set is non-convex, we propose a greedy-heuristic-based approach to efficiently solve the problem. The greedy solution takes into account delay, energy consumption, multi-hop paths, and dynamic network conditions, such as link utilization and SDN rule-capacity. 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| source | IEEE Electronic Library (IEL) |
| subjects | Cloud computing Computation offloading Computer architecture Delay Delays Edge computing Electronic devices Energy consumption fog computing Heuristic methods Internet of Things quality-of-service Servers Software Software-defined networking Task analysis task offloading |
| title | Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications |
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