Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics
Abstract To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-12-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00965-1 |
| _version_ | 1797973460917420032 |
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| author | B. Vijayaram V. Vasudevan |
| author_facet | B. Vijayaram V. Vasudevan |
| author_sort | B. Vijayaram |
| collection | DOAJ |
| description | Abstract To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms. |
| first_indexed | 2024-04-11T04:04:38Z |
| format | Article |
| id | doaj.art-ea3da2c5f75048fc8b7f85a3d95c69e4 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-11T04:04:38Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-ea3da2c5f75048fc8b7f85a3d95c69e42023-01-01T12:30:12ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802022-12-012022112310.1186/s13634-022-00965-1Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristicsB. Vijayaram0V. Vasudevan1Kalasalingam Academy of Research and Education, Kalasalingam UniversityKalasalingam Academy of Research and Education, Kalasalingam UniversityAbstract To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms.https://doi.org/10.1186/s13634-022-00965-1Mobile edge computingHyper-heuristicsMeta-heuristicsTask offloadingOptimization |
| spellingShingle | B. Vijayaram V. Vasudevan Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics EURASIP Journal on Advances in Signal Processing Mobile edge computing Hyper-heuristics Meta-heuristics Task offloading Optimization |
| title | Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics |
| title_full | Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics |
| title_fullStr | Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics |
| title_full_unstemmed | Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics |
| title_short | Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics |
| title_sort | wireless edge device intelligent task offloading in mobile edge computing using hyper heuristics |
| topic | Mobile edge computing Hyper-heuristics Meta-heuristics Task offloading Optimization |
| url | https://doi.org/10.1186/s13634-022-00965-1 |
| work_keys_str_mv | AT bvijayaram wirelessedgedeviceintelligenttaskoffloadinginmobileedgecomputingusinghyperheuristics AT vvasudevan wirelessedgedeviceintelligenttaskoffloadinginmobileedgecomputingusinghyperheuristics |