JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks

Cai, Peng; Yang, Fuqian; Wang, Jianjia; Wu, Xing; Yang, Yang; Luo, Xiliang

doi:10.1109/jiot.2020.2964951

Cited by 32 publications

(16 citation statements)

References 29 publications

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“…DRAFT May 16, 2022 There are multiple differences between [20] and our work. In terms of energy consumption (i) our work minimizes energy consumed by all computing nodes while [20] minimizes energy spent only by the task node, energy consumption of helper nodes is examined but only as a constraint. There are significant differences in network and traffic models (iii).…”

Section: Related Workmentioning

confidence: 93%

“…To do so, it needs to transfer both the task and energy required for computations to the helper node. The authors of [20] optimize cost defined as a weighted sum of delay and energy consumed by the task node for computations, energy transmission, and task transmission. The constraints include a maximum task execution delay which cannot be exceeded and offloading enough energy for computations in helper nodes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Kopras¹,

Bossy²,

Idzikowski³

et al. 2022

Preprint

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How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission-and processingrelated energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed. They allow for successful distribution of network requests between FNs and the cloud in various scenarios significantly reducing the average energy cost and decreasing the number of computational requests with unmet delay requirements.

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Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Kopras¹,

Bossy²,

Idzikowski³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Cai et al [ 23 ] studied energy and tasks in fog-enabled IoT networks as the joint offloading. The offloading strategy was applied to minimize the task execution delay, reduce energy consumption for a special task, and analyze a beneficial condition for the joint offloading of tasks and energy.…”

Section: Classification Of the Fog Offloading Approachesmentioning

confidence: 99%

Towards effective offloading mechanisms in fog computing

Sofla

Kashani

Mahdipour

et al. 2021

Multimed Tools Appl

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Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

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“…They improved application response time and the cost of service execution. An algorithm is investigated in Reference [28] based on simultaneous wireless information and power transfer technology. They have used an offloading policy based on the Lyapunov optimization for minimization of delay.…”

Section: Related Workmentioning

confidence: 99%

SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks

Khosroabadi

Fotouhi-Ghazvini

Fotouhi

2021

JSAN

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Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Service (QoS) metrics. Our experimental results have demonstrated that SCATTER approach has better performance compared with the edge-ward and cloud-only, 42.1% and 60.2% less application response times, 22% and 27.8% less network usage, 45% and 65.7% less average application loop delays, and 2.33% and 3.2% less energy consumption.

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JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks

Cited by 32 publications

References 29 publications

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

Towards effective offloading mechanisms in fog computing

SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks

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