Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing

Chakraborty, Sheuli; Mazumdar, Kaushik

doi:10.1016/j.jksuci.2022.02.014

Cited by 30 publications

(10 citation statements)

References 32 publications

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“…To guarantee the completion of task without interruption in the limited time, the computing task is required to be completed in advance of the vehicle leaving the MEC cell scope [24]. Therefore, with offloaded local server, this satisfies the criteria below: (10) where z i represents the distance from the vehicle of the MEC server within its coverage area.…”

Section: Computational Modelmentioning

confidence: 99%

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

Wang

2023

Sustainability

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“Smart transportation” promotes urban sustainable development. The Internet of Vehicles (IoV) refers to a network with huge interaction, which comprises location, speed, route information, and other information about vehicles. To address the problems that the existing task scheduling models and strategies are mostly single and the reasonable allocation of tasks is not considered in these strategies, leading to the low completion rate of unloading, a task offloading with improved genetic algorithm (GA) is proposed. At first, with division in communication and calculation models, a system utility function maximization model is objectively conducted. The problem is solved by improved GA to obtain the scheme of optimal task offloading. As GA, in the traditional sense, inclines to a local optimum, the model herein introduces a Halton sequence for uniform initial population distribution. Additionally, the authors also adapt improved GA for the problem model and global optimal solution guarantee, thus improving the rate of task completion. Finally, the proposed method is proven through empirical study in view of scenario building. The experimental demonstration of the proposed strategy based on the built scenario shows that the task calculation completion rate is not less than 75%, and when the vehicle terminal is 70, the high-priority task completion rate also reaches 90%, which can realize reasonable allocation of computing resources and ensure the successful unloading of tasks.

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Section: Computational Modelmentioning

confidence: 99%

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

Wang

2023

Sustainability

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“…In [33], the improved maximum minimum scheduling algorithm (IMMSA) that improves request completion time by using machine learning training as well as requesting size clustering and clustering the productivity percentage of virtual machines has been proposed. In [34], a genetic algorithm (GA-)-based optimization technique in the sensor mobile edge computing environment to discern the optimal solution has been proposed. In [35], an improved elitism genetic algorithm (IEGA) for overcoming the task scheduling problem for FC to enhance the quality 2…”

Section: Related Workmentioning

confidence: 99%

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Zhang

Shu

Navaei

2022

Wireless Communications and Mobile Computing

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Due to the proliferation of requests in heterogeneous resources in edge computing, the existence of a large number of tasks and workloads in virtual machines in the edge computing environment is inevitable. Thus, load balancing strives to facilitate an even distribution of workload across available resources. Its purpose is to provide continuous service and to ensure fair load distribution among resources. Load balancing, with the aim of minimizing response time for tasks and improving resource efficiency, tries to do the proper mapping of tasks among virtual machines at a lower cost. Flow scheduling, on the other hand, assigns a task (group of tasks) to computational resources by prioritizing tasks, so that the relationship between them is maintained. Therefore, in this research, a hierarchical control framework for load balancing and assignment of tasks in edge computing services is presented in order to create load balancing. In the proposed method, in the first level, the genetic algorithm receives a set of tasks in a workflow. Genetic algorithm prioritizes and assigns tasks to resources according to time constraints, resource processing power, resource availability, and task cost. For this purpose, the integer linear programming optimization in the evaluation function of the genetic algorithm will be utilized. In the second level, considering the past load distribution in edge resources, we estimate the probability of load distribution among sources according to hidden Markov model (HMM). Finally, in order to optimally map tasks to the virtual machines in each host, we will use game theory with service quality factors as an evaluation function. Previous methods have provided a hierarchical control framework that aims to achieve conflicting goals within a data center, but does not use linear programming. Considering the use of service quality criteria as evaluation function parameters in heuristic and optimization methods in this research, it is expected that the results of this research will improve compared to previous methods.

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“…In dynamic environments such as 5G networks with UAVs, knowing whether to undertake this computational offloading is a mission of high importance and complexity that requires intelligent decision-making [46]. Decision on the task offloading mechanism is a choice on whether to offload a task or not, taking into account parameters such as MD battery power, memory space, UAV speed and trajectory, and wireless transmission latency, as well as ensuring that the required QoS is verified when MD roams to a closer cell [47].…”

Section: Factors Affecting Intelligent Offloading Decisionsmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing

Cited by 30 publications

References 32 publications

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

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