A reconfigurable resource management framework for fog environments

Godinho, Noé; Silva, Henrique; Curado, Marília; Paquete, Luís

doi:10.1016/j.future.2022.03.015

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In [23], the authors proposed a new mechanism that combines a mixed-integer linear programming formulation with a static and dynamic heuristic. This mechanism is designed to optimize the fairness of energy and bandwidth usage in a fog environment.…”

Section: Workmentioning

confidence: 99%

Deadline and Energy-Aware Application Module Placement in Fog-Cloud Systems

Alwabel,

Swain

2024

IEEE Access

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Fog computing has emerged as a promising augmentation of cloud computing, positioned at the network's edge, and it is poised to enhance a wide range of Internet of Things (IoT) driven applications. Although fog computing promises to reduce the response time of applications, its omnipresence is subject to the availability and capabilities of the resources in the fog infrastructure. Hence, there is a need of efficiently harness fog infrastructure to execute different IoT applications while meeting their quality of service (QoS) requirements. However, this objective becomes challenging when the applications are decomposed into multiple modules with diverse latency sensitivities. The scatter placement of application modules over distributed fog nodes further intensifies the problem by increasing the overall energy consumption of the fog environment. Therefore, this study proposes a deadline and energy-aware modular application placement policy for fog computing environments. The proposed policy simultaneously prioritizes the placement of critical applications in the fog infrastructure and consolidates the number of active fog nodes for energy management. The performance of the proposed policy was evaluated using iFogSim and compared with several contemporary solutions. The experimental results demonstrate that the proposed policy outperforms others in increasing the percentage of QoS-satisfied applications and reducing energy usage in fog computing.INDEX TERMS Application module placement, placement policy, latency-aware placement, energy-aware placement, task scheduling, resource management, fog computing, cloud computing

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Section: Workmentioning

confidence: 99%

Deadline and Energy-Aware Application Module Placement in Fog-Cloud Systems

Alwabel,

Swain

2024

IEEE Access

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“…Moreover, PACK ranks fog nodes based on location and reliability as factors for allocating tasks to nodes. A hybrid mechanism that merges a linear programming method with several heuristic mechanisms to optimize energy and bandwidth consumption in a fog environment was introduced by [25].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs. This approach is particularly relevant in scenarios where latency, resource constraints, and cost considerations are crucial factors for the deployment of applications. In this study, we propose a hybrid approach that combines a genetic algorithm (GA) with the Flamingo Search Algorithm (FSA) to place application modules while minimizing cost. We consider four cost-types for application deployment: Computation, communication, energy consumption, and violations. The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system. An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches. The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio (TGR) and total cost.

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“…Resource management in fog computing includes computation, communication, storage, and energy. Researchers have tackled the resource management issue in fog computing mainly by using three concepts: load balancing, as in [12], placement policies, as in [13,14], and a scheduling algorithm, as in [15]. First, in [12], Kadhim and Naser propose a load balancing mechanism that involves utilizing parked vehicles to reduce offloading to clouds with the aim of achieving lower execution time and bandwidth consumption.…”

Section: Resource Managementmentioning

confidence: 99%

“…First, in [12], Kadhim and Naser propose a load balancing mechanism that involves utilizing parked vehicles to reduce offloading to clouds with the aim of achieving lower execution time and bandwidth consumption. Second, focusing on placement policies, the authors in [13] present a placement policy for fog computing with the focus of increasing availability and quality of service. The main idea of their policy is to place a fog application to a fog community and, then, the services of the application to fog nodes within the community.…”

Section: Resource Managementmentioning

confidence: 99%

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

et al. 2022

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The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time.

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A reconfigurable resource management framework for fog environments

Cited by 9 publications

References 21 publications

Deadline and Energy-Aware Application Module Placement in Fog-Cloud Systems

Deadline and Energy-Aware Application Module Placement in Fog-Cloud Systems

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

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