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

Alwabel, Abdulelah; Swain, Chinmaya Kumar

doi:10.1109/access.2024.3350171

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…The authors of [32] presented a novel application placement mechanism for fog computing that focusing on reducing power consumption. The main contribution of this paper is to reduce the number of running fog nodes which leads to a cut in the overall energy consumption of the system.…”

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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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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Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Mohanty,

Sahoo

2024

Concurrency and Computation

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SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

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An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

Mohanty,

Sahoo,

Behera

2024

Decision Analytics Journal

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

Cited by 5 publications

References 42 publications

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

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

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

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