Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment
Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/11/2340 |
| _version_ | 1797466390544777216 |
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| author | Sindhu V Prakash M Mohan Kumar P |
| author_facet | Sindhu V Prakash M Mohan Kumar P |
| author_sort | Sindhu V |
| collection | DOAJ |
| description | Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics. |
| first_indexed | 2024-03-09T18:36:16Z |
| format | Article |
| id | doaj.art-81fb4f7196c944759f80d5d96f6b99bd |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T18:36:16Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-81fb4f7196c944759f80d5d96f6b99bd2023-11-24T07:08:45ZengMDPI AGSymmetry2073-89942022-11-011411234010.3390/sym14112340Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog EnvironmentSindhu V0Prakash M1Mohan Kumar P2Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore 641008, IndiaDepartment of Information Technology, Karpagam College of Engineering, Coimbatore 641032, IndiaDepartment of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore 641008, IndiaInadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.https://www.mdpi.com/2073-8994/14/11/2340cloud–fog computingresource allocationtask schedulingcontainer-based task schedulingdirected acyclic graphreinforcement learning |
| spellingShingle | Sindhu V Prakash M Mohan Kumar P Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment Symmetry cloud–fog computing resource allocation task scheduling container-based task scheduling directed acyclic graph reinforcement learning |
| title | Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment |
| title_full | Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment |
| title_fullStr | Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment |
| title_full_unstemmed | Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment |
| title_short | Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment |
| title_sort | energy efficient task scheduling and resource allocation for improving the performance of a cloud fog environment |
| topic | cloud–fog computing resource allocation task scheduling container-based task scheduling directed acyclic graph reinforcement learning |
| url | https://www.mdpi.com/2073-8994/14/11/2340 |
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