An analysis of fog computing data placement algorithms

Silva, Daniel Maniglia A. da; Asaamoning, Godwin; Orrillo, Hector; Sofia, Rute C.; Mendes, Paulo

doi:10.1145/3360774.3368201

Cited by 18 publications

(5 citation statements)

References 17 publications

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“…Tier 3 (CLOUD) devices in this level are generally having significantly higher amount of resources. These they make up for example, virtual machines in data centers (Amancio da Silva, et al, 2020).…”

Section: The Fog Computing Architecturementioning

confidence: 99%

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Jakwa

Gital

Boukari

et al. 2023

International Journal of Cloud Applications and Computing

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.

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Section: The Fog Computing Architecturementioning

confidence: 99%

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Jakwa

Gital

Boukari

et al. 2023

International Journal of Cloud Applications and Computing

View full text Add to dashboard Cite

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“…This level serve as data sources, the devices here, distributes and capture data to the next tier level. Tier 2 (FOG) in this level, IoT devices are integrated and labelled as fod nodes [4]. Processing and gathering of information including translating communication protocols and help in data decimination to other part of network is here in the IoT gatways and hubs, this level includes other devices like access points and routers.…”

Section: The Fog Computing Architecturementioning

confidence: 99%

“…Since these devices are in edges of the network, they are normaly positioned in the customers domain, fog nodes are characterised with limited resources.Tier 3 (CLOUD) devices in this level are generally having significantly higher amount of resources. These they make up for example, virtual machines in data centers [4].…”

Section: The Fog Computing Architecturementioning

confidence: 99%

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Jakwa

Gital²,

Souley

et al. 2023

IJASRE

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

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“…Therefore, offloading is not required for this technique. However, many solutions use offloading techniques nonetheless to perform edge placement strategies [64].…”

Section: Principles Of Edge Computingmentioning

confidence: 99%

Cloud-Edge Orchestration for Smart Cities: A Review of Kubernetes-based Orchestration Architectures

Böhm

Wirtz

2022

EAI Endorsed Trans Smart Cities

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Edge computing offers computational resources near data-generating devices to enable low-latency access. Especially for smart city contexts, edge computing becomes inevitable for providing real-time services, like air quality monitoring systems. Kubernetes, a popular container orchestration platform, is often used to efficiently manage containerized applications in smart cities. Although it misses essential requirements of edge computing, like network-related metrics for scheduling decisions, it is still considered. This paper analyzes custom cloud-edge architectures implemented with Kubernetes. Specifically, we analyze how essential requirements of edge orchestration in smart cities are solved. Also, shortcomings are identified in these architectures based on the fundamental requirements of edge orchestration. We conduct a literature review to obtain the general requirements of edge computing and edge orchestration for our analysis. We map these requirements to the capabilities of Kubernetes-based cloud-edge architectures to assess their level of achievement. Issues like using network-related metrics and the missing topology-awareness of networks are partially solved. However, requirements like real-time resource utilization, fault-tolerance, and the placement of container registries are in the early stages. We conclude that Kubernetes is an eligible candidate for cloudedge orchestration. When the formerly mentioned issues are solved, Kubernetes can successfully contribute latency-critical, large-scale, and multi-tenant application deployments for smart cities.

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An analysis of fog computing data placement algorithms

Cited by 18 publications

References 17 publications

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Cloud-Edge Orchestration for Smart Cities: A Review of Kubernetes-based Orchestration Architectures

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