| 000 | 03258nlm1a2200505 4500 | ||
|---|---|---|---|
| 001 | 665196 | ||
| 005 | 20231030041955.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\36395 | ||
| 035 | _aRU\TPU\network\33956 | ||
| 090 | _a665196 | ||
| 100 | _a20210830a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aNL | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aAdvanced optimization technique for scheduling IoT tasks in cloud-fog computing environments _fA. M. Mokhamed Elsaed, L. Abualigah, I. Attiya |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 330 | _aCloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput. | ||
| 461 | _tFuture Generation Computer Systems | ||
| 463 |
_tVol. 124 _v[P. 142-154] _d2021 |
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| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aInternet of things (IoT) | |
| 610 | 1 | _acloud computing | |
| 610 | 1 | _afog computing | |
| 610 | 1 | _atask scheduling | |
| 610 | 1 | _amakespan | |
| 610 | 1 | _aartificial ecosystem-based optimization | |
| 610 | 1 | _asalp | |
| 610 | 1 | _aswarm | |
| 610 | 1 | _aalgorithm | |
| 610 | 1 | _aинтернет вещей | |
| 610 | 1 | _aоблачные вычисления | |
| 610 | 1 | _aоптимизация | |
| 610 | 1 | _aэкосистемы | |
| 610 | 1 | _aалгоритмы | |
| 700 | 1 |
_aMokhamed Elsaed _bA. M. _cSpecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1987- _gAkhmed Mokhamed _2stltpush _3(RuTPU)RU\TPU\pers\46943 |
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| 701 | 1 |
_aAbualigah _bL. _gLaith |
|
| 701 | 1 |
_aAttiya _bI. _gIbrahim |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20210830 _gRCR |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1016/j.future.2021.05.026 |
| 942 | _cCF | ||