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

Wei, Yong; Yang, Huifeng; Wang, Junqing; Chen, Xi; Li, Jianqi; Zhang, Sunxuan; Huang, Biyao

doi:10.3390/electronics11060839

Cited by 6 publications

(5 citation statements)

References 36 publications

(39 reference statements)

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“…In [40], the authors proposed an offloading algorithm for Energy IoT (EIoT) devices in smart grids. The algorithm aims to achieve energy efficiency without violating the delay constraints.…”

Section: Related Workmentioning

confidence: 99%

“…A second offloading was introduced as a dynamic solution that allowed fog nodes to exit the overloading state by redirecting a portion of the workload to other fog nodes. To propose an efficient offloading service, four main questions should be answered: How is the offloading node chosen [34][35][36][37][38][39][40][41][42][43][44][45][46][47], how is the number of offloading nodes decided [37], what tasks are eligible for offloading [35] and how is the number of offloaded tasks determined [38]. The proposed dynamic offloading service attempts to address these questions.…”

Section: Introductionmentioning

confidence: 99%

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Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility of offloading a task, for the second time, to a fog or a cloud node depends to a great extent on task importance, latency constraints, and required resources. This paper presents a dynamic service that determines which tasks can endure a second offloading. The task type, latency constraints, and amount of required resources are used to select the offloading destination node. This study proposes three heuristic offloading algorithms. Each algorithm targets a specific task type. An overloaded fog node can only issue one offloading request to execute one of these algorithms according to the task offloading priority. Offloading requests are sent to a Software Defined Networking (SDN) controller. The fog node and controller determine the number of offloaded tasks. Simulation results show that the average time required to select offloading nodes was improved by 33% when compared to the dynamic fog-to-fog offloading algorithm. The distribution of workload converges to a uniform distribution when offloading latency-sensitive nonurgent tasks. The lowest offloading priority is assigned to latency-sensitive tasks with hard deadlines. At least 70% of these tasks are offloaded to fog nodes that are one to three hops away from the overloaded node.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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“…In [18], edge resources were allocated to minimize average latency while numerous IoT systems powered multiple smart city facilities and met the edge server capacity limits. Another article [19] proposed an algorithm for offloading tasks with low complexity and balancing energy efficiency based on two-sided correspondence in an IoT system; the suggested method is innovative in dynamically balancing energy efficiency and delay and stably offloading tasks with low complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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“…Small-Timescale Second-Stage Channel Selection: In each slot, m i , ∀m i ∈ Θ makes action decision based on (18) and selects the corresponding channel. Then, m i calculates the r i,n ðtÞ and r i,n ðtÞ based on ( 14) and (15). Finally, update r K ðt + 1Þ and ε t+1 based on ( 16) and (17).…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Matching theory provides an effective approach to solve the two-side matching problem by defining the preferences of matching subjects to address access conflicts among devices, which has been widely used in solving task offloading problems [14,15]. In [16], Shi et al proposed a two-side matching-based server selection algorithm to maximize the efficiency of device-to-device content sharing.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

Huang

et al. 2022

Wireless Communications and Mobile Computing

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The increase in the number of services in the power distribution grid leads to a massive increase in task data. Power distribution internet of things (PDIoT) is the specific application of internet of things (IoT) in the power distribution grid. By deploying a large number of PDIoT devices, the voltage, active power, reactive power, and harmonic parameters are collected to support distribution grid services such as fault identification and status detection. Therefore, PDIoT utilizes massive devices to collect and offload tasks to the edge server through 5G network for real-time data processing. However, how to dynamically select edge servers and channels to meet the energy-efficient and low-latency task offloading requirements of PDIoT devices still faces several technical challenges such as task offloading decisions coupling among devices, unobtainable global state information, as well as interrelation of various quality of service (QoS) metrics such as energy efficiency and delay. To this end, we firstly construct a joint optimization problem to maximize the weighted difference between energy efficiency and delay of devices in PDIoT. Then, the joint optimization problem is decomposed into a large-timescale server selection problem and a small-timescale channel selection problem. Next, we propose an ML-based two-stage task offloading algorithm, where the large-timescale problem is solved by two-side matching in the first stage, and the small-timescale problem is solved by adaptive ε -greedy learning in the second stage. Finally, simulation results show that compared with the task offloading delay-first matching algorithm and the matching theory-based task offloading strategy, the proposed algorithm performs superior in terms of energy efficiency and delay.

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

Cited by 6 publications

References 36 publications

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

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