Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems

Bebortta, Sujit; Senapati, Dilip; Panigrahi, Chhabi Rani; Pati, Bibudhendu

doi:10.1109/jiot.2021.3123554

Cited by 41 publications

(9 citation statements)

References 43 publications

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“…MEC has also been extensively studied, often by taking into account congestion occurring at the buffers of the users or at the buffers of the MEC server [16]- [26]. However, the MEC state-of-the-art cannot be generalized for GF transmissions, since continuous-time queueing models are adopted.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…However, the MEC state-of-the-art cannot be generalized for GF transmissions, since continuous-time queueing models are adopted. For instance, in [16], the M/M/c/c queuing system was exploited and a holistic QoS-aware framework for Industrial IoT systems was designed, whereas in [17] a heuristic scheduling model was designed to maximize the offloading energy and execution efficiency of an Erlang queueing MEC system. Similarly, in [27] three Erlang based queueing models were applied, one at the mobile users, one at the edge server and one at the cloud server.…”

Section: A Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin-Aided Orchestration of Mobile Edge Computing With Grant-Free Access

Mitsiou

Papanikolaou

Diamantoulakis

et al. 2023

IEEE Open J. Commun. Soc.

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Digital twin-aided (DT) edge computing is investigated, where users utilize grant-free random access with adaptive rate to offload their tasks to the edge server. A novel, with lower implementation complexity, probabilistic partial offloading scheme is introduced, while each device is assumed to have an infinite buffer to store its tasks. The aim of the proposed work is to minimize the average delay of the partial offloading. To that end, the average delay of waiting in the queue, the delay of offloading, and the local computation delay are extracted by using queuing theory tools. Then, the non-convex problem of minimizing the average delay of all clients is formulated, while taking into account DT imperfections. Successive convex approximation (SCA), alternating optimization (AO), and various algebraic manipulations are utilized to transform the problem into an equivalent convex problem with tractable solution. Finally, simulation results showcase the value of the proposed analysis and offer important insights for the proposed DT-aided edge network. Specifically, the proposed partial offloading scheme is shown to be more delay efficient compared to both local computing and full offloading, particularly, for greater task generation rates at the users. Also, the impact of the DT imperfections at the average delay is shown to be more notable as the number of users, or the tasks' size, increases.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Digital Twin-Aided Orchestration of Mobile Edge Computing With Grant-Free Access

Mitsiou

Papanikolaou

Diamantoulakis

et al. 2023

IEEE Open J. Commun. Soc.

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show abstract

“…In particular, they realize an IoT-enabled localization and path planning framework and verify the expected gains of computation offloading by utilizing a real edge computing setting. Bebortta et al [7] develop a computation offloading mechanism for time-critical IIoT tasks. Their framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service, due to the finite number of busy MEC server, and act accordingly.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

Computational Offloading for the Industrial Internet of Things: A Performance Analysis

Bouhoula

Avgeris

Leivadeas

et al. 2022

2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)

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The challenge of minimizing mission response times and the energy consumption of computationally-constrained IoT devices, is becoming increasingly important in Industry 4.0 environments, where mission-critical tasks require real-time and latency-sensitive processing. In this context, the computational offloading paradigm becomes an attractive alternative for utilising remote, computationally capable resources placed at the network Edge, to execute demanding applications. In this paper, an analysis of the main options for task execution (ondevice, Edge and Cloud) is performed, under different sensing and actuation scenarios. These results outline the need of an intelligent decision making mechanism to dynamically alternate between the available execution environments, as a response to the varying networking conditions.

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“…The arising of mobile edge computing (MEC) brings an opportunity to alleviate the aforementioned challenges. It submerges computing resources and services to the network edge, and MITs can offload computing tasks to the ground base station through the edge server, thereby effectively reducing communication latency and energy consumption [1][2][3][4]. However, ground base stations are generally far from the activity area of MITs, and a large number of computing tasks can also cause network traffic congestion, increasing latency and reducing user experience.…”

Section: Introductionmentioning

confidence: 99%

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

Tan,

Sun,

Cao

et al. 2023

IET Communications

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This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances.

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Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems

Cited by 41 publications

References 43 publications

Digital Twin-Aided Orchestration of Mobile Edge Computing With Grant-Free Access

Digital Twin-Aided Orchestration of Mobile Edge Computing With Grant-Free Access

Computational Offloading for the Industrial Internet of Things: A Performance Analysis

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

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