Priority-based task scheduling and resource allocation in edge computing for health monitoring system
New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the res...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823000010 |
| _version_ | 1811160171632656384 |
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| author | Zubair Sharif Low Tang Jung Muhammad Ayaz Mazlaini Yahya Shahneela Pitafi |
| author_facet | Zubair Sharif Low Tang Jung Muhammad Ayaz Mazlaini Yahya Shahneela Pitafi |
| author_sort | Zubair Sharif |
| collection | DOAJ |
| description | New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks’ computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost. |
| first_indexed | 2024-04-10T05:54:11Z |
| format | Article |
| id | doaj.art-98201b35010846efb05568715496ab5a |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-10T05:54:11Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-98201b35010846efb05568715496ab5a2023-03-04T04:22:35ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-02-01352544559Priority-based task scheduling and resource allocation in edge computing for health monitoring systemZubair Sharif0Low Tang Jung1Muhammad Ayaz2Mazlaini Yahya3Shahneela Pitafi4Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Malaysia; Corresponding author.Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, MalaysiaSensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk 71491, Saudi ArabiaHead IoT Automation, Petronas, MalaysiaComputer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, MalaysiaNew and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks’ computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost.http://www.sciencedirect.com/science/article/pii/S1319157823000010Priority-based task schedulingResource allocationMobile edge computingCloud computingSmart hospitalsHealthcare monitoring system |
| spellingShingle | Zubair Sharif Low Tang Jung Muhammad Ayaz Mazlaini Yahya Shahneela Pitafi Priority-based task scheduling and resource allocation in edge computing for health monitoring system Journal of King Saud University: Computer and Information Sciences Priority-based task scheduling Resource allocation Mobile edge computing Cloud computing Smart hospitals Healthcare monitoring system |
| title | Priority-based task scheduling and resource allocation in edge computing for health monitoring system |
| title_full | Priority-based task scheduling and resource allocation in edge computing for health monitoring system |
| title_fullStr | Priority-based task scheduling and resource allocation in edge computing for health monitoring system |
| title_full_unstemmed | Priority-based task scheduling and resource allocation in edge computing for health monitoring system |
| title_short | Priority-based task scheduling and resource allocation in edge computing for health monitoring system |
| title_sort | priority based task scheduling and resource allocation in edge computing for health monitoring system |
| topic | Priority-based task scheduling Resource allocation Mobile edge computing Cloud computing Smart hospitals Healthcare monitoring system |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823000010 |
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