Service Placement for Latency Reduction in the Fog Using Application Profiles

Velásquez, Karima; Abreu, David Pérez; Curado, Marília; Monteiro, Edmundo

doi:10.1109/access.2021.3085370

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…The level of service latency is an important indicator of the performance of edge systems. Velasquez et al [19] initially framed the challenge of edge service deployment as a linear programming problem, focusing on the optimization of latency. Subsequently, they introduced a PageRank-based heuristic algorithm, which assigns grades based on service request volume, and deployment decisions are made based on these grades.…”

Section: Reducing Average Latencymentioning

confidence: 99%

A Container Optimal Matching Deployment Algorithm based on CN-Graph for Mobile Edge Computing

Rao,

Chen,

et al. 2024

Preprint

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The deployment of increasingly diverse services on edge devices is becoming increasingly prevalent. Efficiently deploying functionally heterogeneous services to resource heterogeneous edge nodes while achieving superior user experience is a challenge that every edge system must address. In this paper, we propose a Container-Node Graph (CN-Graph) based container optimal matching deployment algorithm, Edge Kuhn-Munkres Algorithm (EKM) Based on Container-Node Graph, designed for heterogeneous environment to optimize systemperformance. Initially, containers are categorized by functional labels, followed by construction of a CN-Graph model based on the relationship between containers and nodes. Finally, the container deployment problem is transformed into a weighted bipartite graph optimal matching problem. In comparison with the mainstream container deployment algorithms, Swarm, Kubernetes, and the recently emerged ECSched-dp algorithm, the EKM algorithm demonstrates the ability to effectively enhance the average runtime performance of containers to 3.74 times, 4.10 times, and 2.39 times, respectively.

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Section: Reducing Average Latencymentioning

confidence: 99%

A Container Optimal Matching Deployment Algorithm based on CN-Graph for Mobile Edge Computing

Rao,

Chen,

et al. 2024

Preprint

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“…[10-14] [22, 24]. In community-based approaches, nodes are clustered based on connection density and organized as communities [21,24,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The betweenness centrality measure is used to create a set of wellconnected devices to improve service availability. Velasquez et al [21] create groups of nodes based on the possibility of sharing gateway load between the nodes. To do this, they rank the nodes to form communities that contain nodes with the highest transition probability.…”

Section: Community Based Application Service Deploymentmentioning

confidence: 99%

An Efficient Application Deployment in Fog

Azimzadeh,

Rezaee,

Jafarali Jassbi

et al. 2023

itrc

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Fog computing emerged to meet to the needs of modern IoT applications, such as low latency, high security, etc. To this end, it brings the network resources closer to the end user. The properties of fog computing, such as heterogeneity, distribution, and resource limitations, have challenged application deployment in this environment. Smart service placement means deploying services of the IoT applications on fog nodes in a way that their service quality requirements are met and fog resources are used effectively. This paper proposes an efficient application deployment method in fog computing using communities. In contrast to previous research, the proposed method uses more factors than topological features to distribute network capacity more evenly between communities. This results in efficient use of network resources and better fulfillment of application requirements. In addition, according to our argument, using multiple criteria to prioritize applications will lead to better deployment and more effective use of resources. For this purpose, we use the number of application requests besides the deadline factor for application prioritization. Extensive simulation results showed that the proposed method significantly outperforms the state-of-the-art methods in terms of meeting deadlines, decreasing delays, increasing resource utilization, and availability by about 17, 33, 7, and 11 percent, respectively.

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“…Khosroabadi and Fotouhi-Ghazvini 5 proposed "a clustering of fog devices and requirement-sensitive services first" (SCATTER) algorithm to allocate the fog-edge border computing resources to delay-sensitive applications but ignores the other tasks in demand, which could be a concern in many real-life applications. 39 introduced popularity ranked placement (PRP) based on graph partitions and optimization using genetic algorithms, and showed that PRP yielded improvements compared to the generic genetic algorithm or first fit (FF) algorithm but could still be improved. Morkevicius and Venčkauskas 40 presented two stages of multiobjective optimization that included multiple metrics that we use in this study to evaluate the proposed method: security performance, compute usage performance, and storage utilization.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

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A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

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Service Placement for Latency Reduction in the Fog Using Application Profiles

Cited by 10 publications

References 39 publications

A Container Optimal Matching Deployment Algorithm based on CN-Graph for Mobile Edge Computing

A Container Optimal Matching Deployment Algorithm based on CN-Graph for Mobile Edge Computing

An Efficient Application Deployment in Fog

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

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