Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Hossain, Delowar; Sultana, Tangina; Hossain, Alamgir; Hossain, Imtiaz; Huynh, Luan N. T.; Park, Junyoung; Huh, Eui‐Nam

doi:10.3390/s21041484

Cited by 24 publications

(13 citation statements)

References 46 publications

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“…Fuzzy decision-based task offloading management was proposed in [46]. An orchestrator layer is responsible for executing the fuzzy logic rules to determine the target offloading node, which can be a local edge node, neighboring edge node, or cloud node.…”

Section: Related Workmentioning

confidence: 99%

“…A second offloading was introduced as a dynamic solution that allowed fog nodes to exit the overloading state by redirecting a portion of the workload to other fog nodes. To propose an efficient offloading service, four main questions should be answered: How is the offloading node chosen [34][35][36][37][38][39][40][41][42][43][44][45][46][47], how is the number of offloading nodes decided [37], what tasks are eligible for offloading [35] and how is the number of offloaded tasks determined [38]. The proposed dynamic offloading service attempts to address these questions.…”

Section: Introductionmentioning

confidence: 99%

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Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility of offloading a task, for the second time, to a fog or a cloud node depends to a great extent on task importance, latency constraints, and required resources. This paper presents a dynamic service that determines which tasks can endure a second offloading. The task type, latency constraints, and amount of required resources are used to select the offloading destination node. This study proposes three heuristic offloading algorithms. Each algorithm targets a specific task type. An overloaded fog node can only issue one offloading request to execute one of these algorithms according to the task offloading priority. Offloading requests are sent to a Software Defined Networking (SDN) controller. The fog node and controller determine the number of offloaded tasks. Simulation results show that the average time required to select offloading nodes was improved by 33% when compared to the dynamic fog-to-fog offloading algorithm. The distribution of workload converges to a uniform distribution when offloading latency-sensitive nonurgent tasks. The lowest offloading priority is assigned to latency-sensitive tasks with hard deadlines. At least 70% of these tasks are offloaded to fog nodes that are one to three hops away from the overloaded node.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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“…Normally, in inference steps, the activation, aggregation, and accumulation methods are applied. In the aggregation step (a.k.a., the combination step), we apply minimum and maximum functions for AND and OR operators [20,59]. The activation step determines how the evaluated result of the IF part is utilized to the THEN part.…”

Section: Fuzzy Inferencementioning

confidence: 99%

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment

Khanh

Hai

Hossain

et al. 2022

Sensors

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In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

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“…The structure is shown in Figure 1 . For the information construction of an industry, information networks are the foundation, information resources are the core, the use of information and the use of information technology is the goal, and information talents, information technology industry, and information policies, regulations, and standards are its guarantee [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Luo

2022

Computational Intelligence and Neuroscience

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This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

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Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Cited by 24 publications

References 46 publications

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

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