Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach
As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is...
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| creator | Li, Xin Zhang, Xinglin Huang, Tiansheng |
| description | As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters V V , we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter V(t) V(t) and \Omega Ω -additive approximation to enhance the robustness of the proposed algorithm within an error \Omega Ω . Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the [O(1/V), O(V)] [O(1/V),O(V)] |
| doi_str_mv | 10.1109/TCC.2023.3312283 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2899208374</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10239513</ieee_id><sourcerecordid>2899208374</sourcerecordid><originalsourceid>FETCH-LOGICAL-c292t-a763db5416ddcb359fd6d36fce5295f38ac374da7d35f9a9326653b1f1d76ca33</originalsourceid><addsrcrecordid>eNpNkD1PwzAQhiMEElXpzsBgiTnF9tVOzFZF5UugIhFmy_FHSUniEKcg_j2u2oFb7obnvVd6kuSS4DkhWNyURTGnmMIcgFCaw0kyoZDRFGOSn8ab8DzNCCfnySyELY6TMyKImCSbJ193IypV-ERr5xqvTN1tkOoMerPDd60tem2Utq2NlPMDevFV3Vi0MhuLCt_2uzHyt2jZoXXX1J1F5Y9Py7q1QavILft-8Ep_XCRnTjXBzo57mrzfrcriIX1e3z8Wy-dUU0HHVGUcTMUWhBujK2DCGW6AO20ZFcxBrjRkC6MyA8wJJYByzqAijpiMawUwTa4Pf2Pt186GUW79buhipaS5EBTnMR8pfKD04EMYrJP9ULdq-JUEy71RGY3KvVF5NBojV4dIba39h1MQjAD8ARAWcTA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2899208374</pqid></control><display><type>article</type><title>Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach</title><source>IEEE Electronic Library (IEL)</source><creator>Li, Xin ; Zhang, Xinglin ; Huang, Tiansheng</creator><creatorcontrib>Li, Xin ; Zhang, Xinglin ; Huang, Tiansheng</creatorcontrib><description><![CDATA[As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters <inline-formula><tex-math notation="LaTeX">V</tex-math> <mml:math><mml:mi>V</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3312283.gif"/> </inline-formula>, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter <inline-formula><tex-math notation="LaTeX">V(t)</tex-math> <mml:math><mml:mrow><mml:mi>V</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-3312283.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq3-3312283.gif"/> </inline-formula>-additive approximation to enhance the robustness of the proposed algorithm within an error <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq4-3312283.gif"/> </inline-formula>. Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the <inline-formula><tex-math notation="LaTeX">[O(1/V), O(V)]</tex-math> <mml:math><mml:mrow><mml:mo>[</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq5-3312283.gif"/> </inline-formula> performance-backlog tradeoff and is more competitive than benchmarks.]]></description><identifier>ISSN: 2168-7161</identifier><identifier>EISSN: 2372-0018</identifier><identifier>DOI: 10.1109/TCC.2023.3312283</identifier><identifier>CODEN: ITCCF6</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Applications programs ; Approximation algorithms ; Cloud computing ; Computation offloading ; Computer networks ; Costs ; Edge computing ; Error analysis ; Heterogeneity ; Heuristic algorithms ; Mobile computing ; Mobile edge computing ; Optimization ; Parameters ; Placement ; Servers ; service placement ; Stochastic processes ; Task analysis ; task offloading ; Time ; two-timescale lyapunov optimization</subject><ispartof>IEEE transactions on cloud computing, 2023-10, Vol.11 (4), p.3656-3671</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c292t-a763db5416ddcb359fd6d36fce5295f38ac374da7d35f9a9326653b1f1d76ca33</citedby><cites>FETCH-LOGICAL-c292t-a763db5416ddcb359fd6d36fce5295f38ac374da7d35f9a9326653b1f1d76ca33</cites><orcidid>0009-0002-1718-8938 ; 0000-0003-2592-6945 ; 0000-0002-4557-1865</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10239513$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10239513$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li, Xin</creatorcontrib><creatorcontrib>Zhang, Xinglin</creatorcontrib><creatorcontrib>Huang, Tiansheng</creatorcontrib><title>Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach</title><title>IEEE transactions on cloud computing</title><addtitle>TCC</addtitle><description><![CDATA[As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters <inline-formula><tex-math notation="LaTeX">V</tex-math> <mml:math><mml:mi>V</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3312283.gif"/> </inline-formula>, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter <inline-formula><tex-math notation="LaTeX">V(t)</tex-math> <mml:math><mml:mrow><mml:mi>V</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-3312283.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq3-3312283.gif"/> </inline-formula>-additive approximation to enhance the robustness of the proposed algorithm within an error <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq4-3312283.gif"/> </inline-formula>. Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the <inline-formula><tex-math notation="LaTeX">[O(1/V), O(V)]</tex-math> <mml:math><mml:mrow><mml:mo>[</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq5-3312283.gif"/> </inline-formula> performance-backlog tradeoff and is more competitive than benchmarks.]]></description><subject>Algorithms</subject><subject>Applications programs</subject><subject>Approximation algorithms</subject><subject>Cloud computing</subject><subject>Computation offloading</subject><subject>Computer networks</subject><subject>Costs</subject><subject>Edge computing</subject><subject>Error analysis</subject><subject>Heterogeneity</subject><subject>Heuristic algorithms</subject><subject>Mobile computing</subject><subject>Mobile edge computing</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Placement</subject><subject>Servers</subject><subject>service placement</subject><subject>Stochastic processes</subject><subject>Task analysis</subject><subject>task offloading</subject><subject>Time</subject><subject>two-timescale lyapunov optimization</subject><issn>2168-7161</issn><issn>2372-0018</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkD1PwzAQhiMEElXpzsBgiTnF9tVOzFZF5UugIhFmy_FHSUniEKcg_j2u2oFb7obnvVd6kuSS4DkhWNyURTGnmMIcgFCaw0kyoZDRFGOSn8ab8DzNCCfnySyELY6TMyKImCSbJ193IypV-ERr5xqvTN1tkOoMerPDd60tem2Utq2NlPMDevFV3Vi0MhuLCt_2uzHyt2jZoXXX1J1F5Y9Py7q1QavILft-8Ep_XCRnTjXBzo57mrzfrcriIX1e3z8Wy-dUU0HHVGUcTMUWhBujK2DCGW6AO20ZFcxBrjRkC6MyA8wJJYByzqAijpiMawUwTa4Pf2Pt186GUW79buhipaS5EBTnMR8pfKD04EMYrJP9ULdq-JUEy71RGY3KvVF5NBojV4dIba39h1MQjAD8ARAWcTA</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Li, Xin</creator><creator>Zhang, Xinglin</creator><creator>Huang, Tiansheng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0009-0002-1718-8938</orcidid><orcidid>https://orcid.org/0000-0003-2592-6945</orcidid><orcidid>https://orcid.org/0000-0002-4557-1865</orcidid></search><sort><creationdate>20231001</creationdate><title>Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach</title><author>Li, Xin ; Zhang, Xinglin ; Huang, Tiansheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-a763db5416ddcb359fd6d36fce5295f38ac374da7d35f9a9326653b1f1d76ca33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Applications programs</topic><topic>Approximation algorithms</topic><topic>Cloud computing</topic><topic>Computation offloading</topic><topic>Computer networks</topic><topic>Costs</topic><topic>Edge computing</topic><topic>Error analysis</topic><topic>Heterogeneity</topic><topic>Heuristic algorithms</topic><topic>Mobile computing</topic><topic>Mobile edge computing</topic><topic>Optimization</topic><topic>Parameters</topic><topic>Placement</topic><topic>Servers</topic><topic>service placement</topic><topic>Stochastic processes</topic><topic>Task analysis</topic><topic>task offloading</topic><topic>Time</topic><topic>two-timescale lyapunov optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xin</creatorcontrib><creatorcontrib>Zhang, Xinglin</creatorcontrib><creatorcontrib>Huang, Tiansheng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on cloud computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Xin</au><au>Zhang, Xinglin</au><au>Huang, Tiansheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach</atitle><jtitle>IEEE transactions on cloud computing</jtitle><stitle>TCC</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>11</volume><issue>4</issue><spage>3656</spage><epage>3671</epage><pages>3656-3671</pages><issn>2168-7161</issn><eissn>2372-0018</eissn><coden>ITCCF6</coden><abstract><![CDATA[As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters <inline-formula><tex-math notation="LaTeX">V</tex-math> <mml:math><mml:mi>V</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3312283.gif"/> </inline-formula>, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter <inline-formula><tex-math notation="LaTeX">V(t)</tex-math> <mml:math><mml:mrow><mml:mi>V</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-3312283.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq3-3312283.gif"/> </inline-formula>-additive approximation to enhance the robustness of the proposed algorithm within an error <inline-formula><tex-math notation="LaTeX">\Omega</tex-math> <mml:math><mml:mi>Ω</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq4-3312283.gif"/> </inline-formula>. Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the <inline-formula><tex-math notation="LaTeX">[O(1/V), O(V)]</tex-math> <mml:math><mml:mrow><mml:mo>[</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>V</mml:mi><mml:mo>)</mml:mo><mml:mo>]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq5-3312283.gif"/> </inline-formula> performance-backlog tradeoff and is more competitive than benchmarks.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TCC.2023.3312283</doi><tpages>16</tpages><orcidid>https://orcid.org/0009-0002-1718-8938</orcidid><orcidid>https://orcid.org/0000-0003-2592-6945</orcidid><orcidid>https://orcid.org/0000-0002-4557-1865</orcidid></addata></record> |
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| subjects | Algorithms Applications programs Approximation algorithms Cloud computing Computation offloading Computer networks Costs Edge computing Error analysis Heterogeneity Heuristic algorithms Mobile computing Mobile edge computing Optimization Parameters Placement Servers service placement Stochastic processes Task analysis task offloading Time two-timescale lyapunov optimization |
| title | Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach |
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