Cloud and Edge Computation Offloading for Latency Limited Services
Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers wh...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9399077/ |
| _version_ | 1818327868032155648 |
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| author | Ivana Kovacevic Erkki Harjula Savo Glisic Beatriz Lorenzo Mika Ylianttila |
| author_facet | Ivana Kovacevic Erkki Harjula Savo Glisic Beatriz Lorenzo Mika Ylianttila |
| author_sort | Ivana Kovacevic |
| collection | DOAJ |
| description | Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate. |
| first_indexed | 2024-12-13T12:23:06Z |
| format | Article |
| id | doaj.art-677eebd15ee742999cb8a5d539996f88 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:23:06Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-677eebd15ee742999cb8a5d539996f882022-12-21T23:46:31ZengIEEEIEEE Access2169-35362021-01-019557645577610.1109/ACCESS.2021.30718489399077Cloud and Edge Computation Offloading for Latency Limited ServicesIvana Kovacevic0https://orcid.org/0000-0002-6188-3711Erkki Harjula1https://orcid.org/0000-0001-5331-209XSavo Glisic2Beatriz Lorenzo3Mika Ylianttila4https://orcid.org/0000-0002-8079-5514Centre for Wireless Communications, University of Oulu, Oulu, FinlandCentre for Wireless Communications, University of Oulu, Oulu, FinlandWorcester Polytechnic Institute, Worcester, MA, USADepartment of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USACentre for Wireless Communications, University of Oulu, Oulu, FinlandMulti-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate.https://ieeexplore.ieee.org/document/9399077/Cloud computingmulti access edge computing (MEC)computational offloading (CO)end-to-end latencylimited-latency servicesjoint resource allocation |
| spellingShingle | Ivana Kovacevic Erkki Harjula Savo Glisic Beatriz Lorenzo Mika Ylianttila Cloud and Edge Computation Offloading for Latency Limited Services IEEE Access Cloud computing multi access edge computing (MEC) computational offloading (CO) end-to-end latency limited-latency services joint resource allocation |
| title | Cloud and Edge Computation Offloading for Latency Limited Services |
| title_full | Cloud and Edge Computation Offloading for Latency Limited Services |
| title_fullStr | Cloud and Edge Computation Offloading for Latency Limited Services |
| title_full_unstemmed | Cloud and Edge Computation Offloading for Latency Limited Services |
| title_short | Cloud and Edge Computation Offloading for Latency Limited Services |
| title_sort | cloud and edge computation offloading for latency limited services |
| topic | Cloud computing multi access edge computing (MEC) computational offloading (CO) end-to-end latency limited-latency services joint resource allocation |
| url | https://ieeexplore.ieee.org/document/9399077/ |
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