An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment

Ghanavati, Sara; Abawajy, Jemal; Izadi, Davood

doi:10.1109/tsc.2020.3028575

Cited by 64 publications

(21 citation statements)

References 31 publications

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“…However, when calculated, the energy required to transmit task

to the cloud is calculated as follows:

where

is a constant related to the wireless interface [ 32 ]. The energy consumption

for processing task

on fog node

is expressed by multiplying the execution time by a constant coefficient as follows:

…”

Section: Methodsmentioning

confidence: 99%

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Alatoun

Matrouk

Mohammed

et al. 2022

Sensors

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In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

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“…However, when calculated, the energy required to transmit task

to the cloud is calculated as follows:

where

is a constant related to the wireless interface [ 32 ]. The energy consumption

for processing task

on fog node

is expressed by multiplying the execution time by a constant coefficient as follows:

…”

Section: Methodsmentioning

confidence: 99%

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Alatoun

Matrouk

Mohammed

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Although these algorithms usually perform better than heuristics, similarly they cannot guarantee to ind the optimal solution. Several works such as [30,37,82,120] used meta-heuristics.…”

Section: Placement Techniquementioning

confidence: 99%

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya

2022

ACM Comput. Surv.

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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.

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“…Although these algorithms usually perform better than heuristics, similarly they cannot guarantee to find the optimal solution. Several works such as [33,40,90,132] used meta-heuristics.…”

Section: Placement Techniquementioning

confidence: 99%

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment

Cited by 64 publications

References 31 publications

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

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