An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum
With the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication betw...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10411893/ |
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| author | Abhijeet Mahapatra Santosh K. Majhi Kaushik Mishra Rosy Pradhan D. Chandrasekhar Rao Sandeep K. Panda |
| author_facet | Abhijeet Mahapatra Santosh K. Majhi Kaushik Mishra Rosy Pradhan D. Chandrasekhar Rao Sandeep K. Panda |
| author_sort | Abhijeet Mahapatra |
| collection | DOAJ |
| description | With the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication between the IoT devices and the Cloud datacenter. This latency can be reduced by introducing a Fog layer in between the Cloud and the IoT layer and therefore, it is paramount to offload those tremendous data to leverage the overloaded storage and computation to the Cloud-based systems and Fog-assisted nodes. Moreover, these heavy computations consume significant energy from the distributed Fog servers as well as Cloud datacenters. Therefore, this work addresses the task migration problem in a Fog-Cloud system and load balancing to reduce the latency rate, energy utilized and service time while increasing the resource utilization for latency-sensitive systems. This paper uses a Fuzzy logic algorithm for determining the target layers for offloading considering the resource heterogeneity and the system requirements (i.e., network bandwidth, task size, resource utilization and latency sensitivity). A Binary Linear-Weight JAYA (BLWJAYA) task scheduling algorithm has been proposed to map the incoming IoT requests to computation-rich Fog nodes/virtual machines (VMs). Numerous experimental simulations have been carried out to appraise the efficacy of the suggested method and it is evident that the suggested method outperforms other baselines with an approximate improvement of 26.2%, 12%, 7%, 8.63% and 6% for Resource utilization, Service rate, Latency rate, Energy consumption and Load balancing rate. The presented approach is generic and scalable concerning addressing the unpredictability of data and the associated latency due to the task offloading criteria within the Fog layer. |
| first_indexed | 2024-03-08T08:39:25Z |
| format | Article |
| id | doaj.art-8e364df2f58c4cc8b1840dd6b6e763ce |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T08:39:25Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8e364df2f58c4cc8b1840dd6b6e763ce2024-02-02T00:04:32ZengIEEEIEEE Access2169-35362024-01-0112143341434910.1109/ACCESS.2024.335712210411893An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud ContinuumAbhijeet Mahapatra0https://orcid.org/0000-0003-1315-5039Santosh K. Majhi1https://orcid.org/0000-0002-8887-6933Kaushik Mishra2https://orcid.org/0000-0001-9499-0727Rosy Pradhan3D. Chandrasekhar Rao4https://orcid.org/0000-0001-7414-3360Sandeep K. Panda5https://orcid.org/0000-0002-8887-6933Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, IndiaDepartment of Computer Science and Information Technology, Guru Ghasidas Viswavidyalaya, Bilaspur, Chhattisgarh, IndiaDepartment of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, IndiaDepartment of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, IndiaDepartment of Information Technology, Veer Surendra Sai University of Technology, Burla, Odisha, IndiaDepartment of Data Science and Artificial Intelligence, Faculty of Science & Technology, ICFAI Foundation for Higher Education, Hyderabad, Telangana, IndiaWith the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication between the IoT devices and the Cloud datacenter. This latency can be reduced by introducing a Fog layer in between the Cloud and the IoT layer and therefore, it is paramount to offload those tremendous data to leverage the overloaded storage and computation to the Cloud-based systems and Fog-assisted nodes. Moreover, these heavy computations consume significant energy from the distributed Fog servers as well as Cloud datacenters. Therefore, this work addresses the task migration problem in a Fog-Cloud system and load balancing to reduce the latency rate, energy utilized and service time while increasing the resource utilization for latency-sensitive systems. This paper uses a Fuzzy logic algorithm for determining the target layers for offloading considering the resource heterogeneity and the system requirements (i.e., network bandwidth, task size, resource utilization and latency sensitivity). A Binary Linear-Weight JAYA (BLWJAYA) task scheduling algorithm has been proposed to map the incoming IoT requests to computation-rich Fog nodes/virtual machines (VMs). Numerous experimental simulations have been carried out to appraise the efficacy of the suggested method and it is evident that the suggested method outperforms other baselines with an approximate improvement of 26.2%, 12%, 7%, 8.63% and 6% for Resource utilization, Service rate, Latency rate, Energy consumption and Load balancing rate. The presented approach is generic and scalable concerning addressing the unpredictability of data and the associated latency due to the task offloading criteria within the Fog layer.https://ieeexplore.ieee.org/document/10411893/Energy consumptionfog-cloud computingIoTlatency sensitivityload balancingresource utilization |
| spellingShingle | Abhijeet Mahapatra Santosh K. Majhi Kaushik Mishra Rosy Pradhan D. Chandrasekhar Rao Sandeep K. Panda An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum IEEE Access Energy consumption fog-cloud computing IoT latency sensitivity load balancing resource utilization |
| title | An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum |
| title_full | An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum |
| title_fullStr | An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum |
| title_full_unstemmed | An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum |
| title_short | An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum |
| title_sort | energy aware task offloading and load balancing for latency sensitive iot applications in the fog cloud continuum |
| topic | Energy consumption fog-cloud computing IoT latency sensitivity load balancing resource utilization |
| url | https://ieeexplore.ieee.org/document/10411893/ |
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