A Fuzzy Decision Tree Based Tasks Orchestration Algorithm for Edge Computing Environments

Mechalikh, Charafeddine; Taktak, Hajer; Moussa, Faouzi

doi:10.1007/978-3-030-44041-1_18

Cited by 12 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study compares various performance parameters given below for under loaded, average loaded and average load condition. In this section a comprehensive comparative study is showcase where the proposed model is compared with existing models like Fuzzy logic [ 25 , 26 ], Genetic algorithm [ 27 ] and Particle swarm optimization [ 28 , 29 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Federated learning inspired Antlion based orchestration for Edge computing environment

H. S.,

Gupta

2024

PLoS ONE

View full text Add to dashboard Cite

Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Federated learning inspired Antlion based orchestration for Edge computing environment

H. S.,

Gupta

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The Deep RL-based Efficient Workload Orchestration (DE-WOrch) model introduces a central orchestrator that knows the current state of all resources in the environment and is accessible to all edge devices in an orchestration area. To improve the performance of our previously proposed model (AdWOrch) [45], we replace the FDT [17] algorithm with DQN. This study embeds the resource selection strategy of the previous work in the reward function.…”

Section: Deep Rl-based Efficient Workload Orchestration Modelmentioning

confidence: 99%

“…To minimize the wasted resource, we defined a wasted resource penalty P w as shown in Equation (17). The wasted resource penalty is defined when a successful task has not been offloaded to the optimum destination.…”

Section: Resource Waste Penaltymentioning

confidence: 99%

“…However, relying on historical data in such an environment is impractical, as edge nodes' mobility impacts their availability and accessibility. Hence, Reinforcement Learning (RL) [15], [16], [17], [18], and Deep Reinforcement Learning (DRL) [19], [20], [21], [22] approaches are proposed to address the high level of dynamicity of the environment. RL agents can learn from their interactions with the dynamic environment, adapt to the environment changes, and achieve the objectives defined by reward functions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing

Safavifar,

Gyamfi,

Mangina

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Workload orchestration at the edge of the network has become increasingly challenging with the ever-increasing penetration of resource demanding mobile, and heterogeneous devices offering low latency services. Literature has addressed this challenge assuming the availability of multi-access Mobile Edge Computing (MEC) servers and placing the computing tasks related to such services on the MEC servers. However, to develop a more sustainable and energy-efficient computing paradigm, for applications operating in stochastic environments with unpredictable workloads, it is essential to minimize the MEC servers' usage, and utilize the available resource-constrained edge devices, to keep the resourceful servers idle for handling any unpredictable larger workload. In this paper, we proposed DEWOrch, a deep reinforcement Learning algorithm for efficient workload orchestration. DEWOrch's aim is to increase the utilization of resource-constrained edge devices and minimize resource waste for more sustainable and energy efficient computing solution. This model is evaluated in an Extreme Edge Computing environment, where no MEC servers is available and only edge devices with constrained capacity are used to perform tasks. The results show that DEWOrch outperforms the state-of-the-art methods by around 50% decrease in resource waste while improved task success rate, and decreased energy consumption per task in most scenarios.

show abstract

“…The Adaptive Workload Orchestration (AdWOrch) model is developed based on the Fuzzy Decision Tree (FDT) algorithm proposed in (Mechalikh et al, 2020a).The base task orchestration algorithm is complemented by a new resource selection strategy and reward function.…”

Section: Adworchmentioning

confidence: 99%

A Robust Adaptive Workload Orchestration in Pure Edge Computing

Safavifar

Mechalikh

Golpayegani

2023

Proceedings of the 15th International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

Pure Edge computing (PEC) aims to bring cloud applications and services to the edge of the network to support the growing user demand for time-sensitive applications and data-driven computing. However, mobility and limited computational capacity of edge devices pose challenges in supporting some urgent and computationally intensive tasks with strict response time demands. If the execution results of these tasks exceed the deadline, they become worthless and can cause severe safety issues. Therefore, it is essential to ensure that edge nodes complete as many latency-sensitive tasks as possible. In this paper, we propose a Robust Adaptive Workload Orchestration (R-AdWOrch) model to minimize deadline misses and data loss by using priority definition and a reallocation strategy. The results show that R-AdWOrch can minimize deadline misses of urgent tasks while minimizing the data loss of lower priority tasks under all conditions.

show abstract

A Fuzzy Decision Tree Based Tasks Orchestration Algorithm for Edge Computing Environments

Cited by 12 publications

References 11 publications

Federated learning inspired Antlion based orchestration for Edge computing environment

Federated learning inspired Antlion based orchestration for Edge computing environment

Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing

A Robust Adaptive Workload Orchestration in Pure Edge Computing

Contact Info

Product

Resources

About