An Efficient Simulated Annealing-based Task Scheduling Technique for Task Offloading in a Mobile Edge Architecture

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

doi:10.1109/cloudnet55617.2022.9978900

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…For instance, Shang et al Also, several offline task-offloading strategies have been proposed for IoT devices. Previous work suggested using Simulated Annealing [15] and a Genetic algorithm [16] as offline task offloading strategies. Al-Habob et al [17] also proposed a Genetic-based task offloading strategy.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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

Mahjoubi,

Ramaswamy,

Grinnemo

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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“…Hence, the scheduler executes hard real-time tasks such as braking systems on the local processor, while the firm real-time and soft-real-time tasks are executed in the edge and cloud platforms, respectively. In addition, a meta-heuristic task scheduling algorithm is proposed by [44] to schedule the tasks on either local, edge, or cloud processors by considering the computation power, storage capacity, and bandwidth capability. Consequently, Figure 2 illustrates the three-tier network architecture consisting of the things, edge, and cloud layer adapted from [42].…”

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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Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training

Chen,

Jin,

Yao

et al. 2024

Mobile Netw Appl

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An Efficient Simulated Annealing-based Task Scheduling Technique for Task Offloading in a Mobile Edge Architecture

Cited by 4 publications

References 16 publications

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

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

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

Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training

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