Joint Task Offloading and Resource Allocation for IoT Edge Computing With Sequential Task Dependency

An, Xuming; Fan, Rongfei; Hu, Han; Zhang, Ning; Atapattu, Saman; Tsiftsis, Theodoros A.

doi:10.1109/jiot.2022.3150976

Cited by 37 publications

(19 citation statements)

References 29 publications

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“…An optimization problem was formulated for the first scenario, and for the second scenario, the Lagrange duality method was implemented. In order to handle video feeds and other intelligent applications, An et al 147 developed an IoT system that used the MEC approach. The task offloading approach, communication, and computation resources were optimized for slow and fast fading channels to reduce ECp and task processing delays.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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“…A framework for joint task offloading, communication, and computing resource allocation for sequential tasks is proposed in Ref. [ 103 ]. Slow-fading and fast-fading channels are considered.…”

Section: Iot Task Offloading Mechanism In Mecmentioning

confidence: 99%

“…In Ref. [ 103 ], the researcher uses the experience replay technique to train DNN, randomly selecting a batch of training samples from memory. In addition, this method takes into account the application scenario of wireless power transmission to power IoT nodes and considers the joint optimization between optimizing wireless power supply and wireless communication.…”

Section: Iot Task Offloading Mechanism In Mecmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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“…The existing approaches for solving task offloading in SEC can be categorized into two categories: centralized and distributed. Centralized algorithms include heuristics [26], meta-heuristics [15,27,28], game algorithms [29], optimization methods [30], and reinforcement learning [3,31], all of which have demonstrated their effectiveness in various application scenarios. In contrast, distributed approaches, such as dynamic group learning distributed particle swarm optimization [32] and the multi-agent actor-critic reinforcement learning algorithm [33], cannot guarantee optimal results.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

Zhang,

Feng,

Yang

et al. 2023

Aerospace

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Senabling a satellite network with edge computing capabilities, SEC provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network.

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Joint Task Offloading and Resource Allocation for IoT Edge Computing With Sequential Task Dependency

Cited by 37 publications

References 29 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

A comprehensive review on internet of things task offloading in multi-access edge computing

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

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