Meta-heuristic Based Hybrid Service Placement Strategies for Two-Level Fog Computing Architecture

Natesha, B V; Guddeti, Ram Mohana Reddy

doi:10.1007/s10922-022-09660-w

Cited by 22 publications

(12 citation statements)

References 47 publications

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“…We use some existing benchmark works including Hybrid‐EGAPSO, 20 SPP‐PSO, 18 and GWO‐SPP 30 for comparison work with LPB‐SPP. All these works are similar to the proposed approach and use meta‐heuristic techniques to handle SPP.…”

Section: Resultsmentioning

confidence: 99%

“…In Reference 17, the current trends presented in the fields of service placement and computation offloading on fog environments are surveyed and compared. Today, there are extensive works to address SPP, which try to solve the problem by considering different aspects 18–20 . One of the most important aspects of SPP is the type of model, placement strategy, objectives, mobility and placement policy 18 .…”

Section: Related Workmentioning

confidence: 99%

“…In Reference 20, a meta‐heuristic approach for deploying hybrid IoT services on two‐level fog environment is presented. This approach combines microbial genetic algorithm and particle swarm optimization (MGAPSO), and elitism‐based genetic algorithm and particle swarm optimization (EGAPSO) to minimize energy consumption and service cost in fog computing architecture.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned, the priority of IoT services to execute is determined by the analyze component. In extensive studies, deadline and sending time are used for prioritization 20,30 . Without loss of generality, the priority of all services belonging to an IoT application is the same.…”

Section: System Modelmentioning

confidence: 99%

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LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Zhou

et al. 2023

Trans Emerging Tel Tech

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Nowadays, fog computing has joined cloud computing as an emerging computing paradigm to provide resources at the network edge. Fog servers process data associated with Internet of Things (IoT) devices independently of cloud computing, thus saving bandwidth, resource reservations, and storage for real‐time applications with lower latency. Besides, cloud computing supports the integration of edge and cloud resources and facilitates the placement of IoT applications at the network edge. Recent researchers focus on how to deploy IoT services as components of IoT applications on fog computing units, where the loss of resources, energy, and bandwidth are minimized. This problem, known as the IoT service placement problem (SPP), is NP‐hard, and meta‐heuristic models are popular to address it. Each IoT service has its own requirements in terms of latency sensitivity, processor, memory, and storage. Meanwhile, fog computing units are heterogeneous and have limited resource capacities. Therefore, SPP should be addressed by considering the features of fog environment, tolerable delay, and network bandwidth. We formulate SPP as a multi‐objective optimization problem with the perspective of throughput, service cost, resource utilization, energy consumption, and service latency. To solve this problem, the learner performance‐based behavior (LPB) algorithm is presented as a meta‐heuristic model that originates from the MAPE‐K autonomous planning model. The proposed approach, LPB‐SPP, considers resource consumption distribution and service deployment prioritization, and also uses the concepts of elitism and balanced resource consumption to improve the placement process. The validation of LPB‐SPP has been done using different performance metrics and the results have been compared against state‐of‐the‐art algorithms. Simulations show that LPB‐SPP performs better in most comparisons.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

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LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Zhou

et al. 2023

Trans Emerging Tel Tech

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“…This algorithm includes a continuous PSO-inspired modification for updating particle positions, which aims to enhance its effectiveness in optimizing service placement. The study [37] focuses on developing two hybrid algorithms, namely, MGAPSO and EGAPSO, which use meta-heuristics to optimize service placement in a fog computing environment. Specifically, these algorithms are developed by combining GA with Particle Swarm Optimization (PSO) and EGA with PSO, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

Dogani

Yazdanpanah

Zare

et al. 2023

Preprint

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Using cloud computing for Internet of Things (IoT) applications necessitates the transmission of all data to the centralized structure of the cloud, thereby leading to an increase in network traffic and service time. Consequently, cloud computing proves impractical for latency-sensitive IoT applications. Fog computing, acting as an intermediate layer between the cloud and IoT, ensures low latency for such applications. The service placement problem, an NP-hard problem that determines which node should host each service, represents one of the major challenges in the fog computing paradigm. While lightweight containers have emerged as a highly efficient virtualization approach, prior research predominantly employed traditional VM-based architecture for service placement in fog computing. Therefore, this study introduces a multi-objective optimization approach for dynamic service placement in container-based fog computing, accounting for cost, latency, and energy consumption. Specifically, we propose a two-tier framework for resource management in fog computing based on Kubernetes. The multi-objective non-dominated sorting genetic algorithm II (NSGA-II) algorithm balances conflicting performance objectives, such as latency, cost, and energy consumption. Empirical results demonstrate that the proposed method outperforms existing state-of-the-art methods.

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Service Placement Problem for IoT Applications on Fog Computing

Ben Rjeb,

Zorgati,

Ben Djemaa

et al. 2024

Intelligent Systems Design and Applications

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Meta-heuristic Based Hybrid Service Placement Strategies for Two-Level Fog Computing Architecture

Cited by 22 publications

References 47 publications

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

Service Placement Problem for IoT Applications on Fog Computing

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