EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications Article Swipe
Ranumayee Sing
,
Sourav Kumar Bhoi
,
Niranjan Panigrahi
,
Kshira Sagar Sahoo
,
Muhammad Bilal
,
Sayed Chhattan Shah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142215096
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142215096
The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.
Related Topics
Concepts
Computer science
Cloud computing
Job shop scheduling
Energy consumption
Distributed computing
Scheduling (production processes)
Internet of Things
Server
Edge computing
Efficient energy use
Real-time computing
Computer network
Embedded system
Mathematical optimization
Operating system
Biology
Ecology
Engineering
Electrical engineering
Mathematics
Routing (electronic design automation)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su142215096
- https://www.mdpi.com/2071-1050/14/22/15096/pdf?version=1668505316
- OA Status
- gold
- Cited By
- 21
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309153920
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309153920Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su142215096Digital Object Identifier
- Title
-
EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT ApplicationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-15Full publication date if available
- Authors
-
Ranumayee Sing, Sourav Kumar Bhoi, Niranjan Panigrahi, Kshira Sagar Sahoo, Muhammad Bilal, Sayed Chhattan ShahList of authors in order
- Landing page
-
https://doi.org/10.3390/su142215096Publisher landing page
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https://www.mdpi.com/2071-1050/14/22/15096/pdf?version=1668505316Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://www.mdpi.com/2071-1050/14/22/15096/pdf?version=1668505316Direct OA link when available
- Concepts
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Computer science, Cloud computing, Job shop scheduling, Energy consumption, Distributed computing, Scheduling (production processes), Internet of Things, Server, Edge computing, Efficient energy use, Real-time computing, Computer network, Embedded system, Mathematical optimization, Operating system, Biology, Ecology, Engineering, Electrical engineering, Mathematics, Routing (electronic design automation)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 6, 2023: 7Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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