EHGA: A Genetic Algorithm Based Approach for Scheduling Tasks on Distributed Edge-Cloud Infrastructures

Mahjoubi, Ayeh; Grinnemo, Karl-Johan; Taheri, Javid

doi:10.1109/nof55974.2022.9942552

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Therefore, a realistic network computing paradigm comprises cloud computing and edge computing as collaborative computation platforms. Consequently, many studies [23,41,42] consider three-tier (three-layered) network architecture composed of the cloud, the edge, and the Things layers to take advantage of the cloud and edge resources. For instance, Fizza et al [43] proposed two scheduling algorithms for autonomous vehicles that execute tasks based on latency tolerance.…”

Section: Network Architecturementioning

confidence: 99%

A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective

2023

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The edge computing paradigm enables mobile devices with limited memory and processing power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications on the network by bringing the computational power and storage capacity closer to end users. Edge computing comprises heterogeneous computing platforms with resource constraints that are geographically distributed all over the network. As users are mobile and applications change over time, identifying an optimal task scheduling method is a complex multi-objective optimization problem that is NP-hard, meaning the exhaustive search with a time complexity that grows exponentially can solve the problem. Therefore, various approaches are utilized to discover a good solution for scheduling the tasks within a reasonable time complexity, while achieving the most optimal solution takes exponential time. This study reviews task scheduling algorithms based on centralized and distributed methods in a three-layer computing architecture to identify their strengths and limitations in scheduling tasks to edge service nodes.

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Section: Network Architecturementioning

confidence: 99%

A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective

2023

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An Online Simulated Annealing-Based Task Offloading Strategy for a Mobile Edge Architecture

Mahjoubi,

Ramaswamy,

Grinnemo

2024

IEEE Access

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This paper presents a new task-scheduling strategy, SATS, for Mobile Edge Computing (MEC). SATS uses a simple simulated annealing-based method for scheduling tasks and shows that it can be a promising solution for online task scheduling in a MEC architecture. The paper evaluates three types of predictors: neutral, conservative, and optimistic, and concludes that using a conservative predictor that overestimates the number of service requests leads to the best performance in terms of higher acceptance rates and shorter processing times. With its Simulated Annealing-based method, SATS offers an acceptance ratio that is only 5% lower than what it could have been if it had known the frequency of service request arrivals beforehand. The simplicity and efficiency of the SATS strategy are highlighted as it deviates less than 20% from this acceptance ratio in all conducted experiments.INDEX TERMS online task scheduling, simulated annealing, mobile edge computing, task offloading I. INTRODUCTION

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Multi-Criteria Optimization of Application Offloading in the Edge-to-Cloud Continuum

Miloradović,

Papadopoulos

2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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EHGA: A Genetic Algorithm Based Approach for Scheduling Tasks on Distributed Edge-Cloud Infrastructures

Cited by 4 publications

References 9 publications

A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective

A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective

An Online Simulated Annealing-Based Task Offloading Strategy for a Mobile Edge Architecture

Multi-Criteria Optimization of Application Offloading in the Edge-to-Cloud Continuum

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