METO: Matching-Theory-Based Efficient Task Offloading in IoT-Fog Interconnection Networks

Swain, Chittaranjan; Sahoo, Manmath Narayan; Satpathy, Anurag; Muhammad, Khan; Bakshi, Sambit; Rodrigues, Joel J. P. C.; Albuquerque, Victor Hugo C. de

doi:10.1109/jiot.2020.3025631

Cited by 64 publications

(32 citation statements)

References 45 publications

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“…Majority of the cloud services are deployed over the Internet and are thus prone to network delay. Swain et al [22] implemented a fog computing architecture to execute real time IoT tasks at the edge nodes. The devices in the IoT network can increase their battery life by distributing the tasks to nearby fog nodes.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja¹,

Alshammari²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

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Section: Literature Surveymentioning

confidence: 99%

Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja¹,

Alshammari²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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“…Task offloading in a densely connected network is proven to be NP-Hard [9]. In view of full offloading scenarios, Adhikari et al [3] proposed a particle swarm optimization (PSO) based offloading strategy to improve the QoS parameters such as cost and resource utilization.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, the optimization solvers are computationally intensive and are not scalable. Matching theory-based solutions overcome these drawbacks and focus on reducing the response time and energy consumption [7]- [9]. Although these approaches resolve the concerns of optimization solutions, they face the following issues, however.…”

Section: Introductionmentioning

confidence: 99%

“…Although these approaches resolve the concerns of optimization solutions, they face the following issues, however. The solution approaches discussed in [7]- [9] are restricted to a single SP environment. Moreover, Gu et al [10] pointed out that the direct interaction between the FNs and IoT devices may raise security concerns such as eavesdropping and data hijacking.…”

Section: Introductionmentioning

confidence: 99%

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SPATO: A Student Project Allocation Based Task Offloading in IoT-Fog Systems

Swain¹,

Sahoo²,

Satpathy³

2021

Preprint

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The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network (WAN) latency which makes execution of delay-critical IoT applications inconceivable. To overcome this, service providers (SPs) often deploy multiple fog nodes (FNs) at the network edge that helps in executing offloaded computations from IoT devices with improved user experience. As the FNs have limited resources, matching IoT services to FNs while ensuring minimum latency and energy from an end-user's perspective and maximizing revenue and tasks meeting deadlines from a SP's standpoint is challenging. Therefore in this paper, we propose a student project allocation (SPA) based efficient task offloading strategy called SPATO that takes into account key parameters from different stakeholders. Thorough simulation analysis shows that SPATO is able to reduce the offloading energy and latency respectively by 29% and 40% and improves the revenue by 25% with 99.3% tasks executing within their deadline.

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“…In reference [19], Huang et al introduced a task-container matching market to provide on-demand offloading services based on system service capability and resource availability. In reference [20], a matching theory-based task for offloading strategy was proposed, aiming at reducing the total IoT network energy. In reference [21], a solution to minimize the network delay from a contract-matching integration perspective was provided.…”

Section: Introductionmentioning

confidence: 99%

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

et al. 2022

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With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16.

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METO: Matching-Theory-Based Efficient Task Offloading in IoT-Fog Interconnection Networks

Cited by 64 publications

References 45 publications

Efficient Resource Allocation in Fog Computing Using QTCS Model

Efficient Resource Allocation in Fog Computing Using QTCS Model

SPATO: A Student Project Allocation Based Task Offloading in IoT-Fog Systems

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

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