Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Baburao, D.; Pavankumar, T.; Prabhu, C.S.R.

doi:10.1007/s13204-021-01970-w

Cited by 40 publications

(10 citation statements)

References 22 publications

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“…Furthermore, each particle has a velocity that influences its path. Each particle successfully explores the issue space by continually pursuing the most promising particles at any given time [32].…”

Section: Performance Evaluation and Resultsmentioning

confidence: 99%

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Alsamarai,

Uçan

2024

Applied Sciences

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Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.

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Section: Performance Evaluation and Resultsmentioning

confidence: 99%

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Alsamarai,

Uçan

2024

Applied Sciences

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“…Hybrid methods apply various methods, such as approximate, exact, and fundamental, for accomplishing centralized allocation in fog networks [59], [60]. Studies with hybrid methods are reviewed in this section.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…In [59], to efficiently handle load balancing, a particle swarm optimization-based Enhanced Dynamic Resource Allocation Method (EDRAM) has been proposed, which in turn reduces task waiting time, latency and network bandwidth consumption and improves the Quality of Experience (QoE). The Enhanced Dynamic Resource Allocation Method (EDRAM), in turn, helps allocate the required resource by removing the long-time inactive, unreferenced and sleepy services from the random-access memory.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

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Despite the importance of resource allocation issues, there is no systematic, comprehensive and detailed survey on resource allocation approaches in the fog-computing context. In this article, we provide a Systematic Literature Review (SLR) on the resource allocation approaches in fog environments in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and provide open issues. The presented taxonomy is categorized into three main fields: centralized allocation, decentralized allocation, and integrated allocation (published between 2017 and March 2022). According to what is known in fog computing, load balancing and service placement are among the most important basic parameters that ensure service quality. These fields are classified into four methods, approximate, exact, fundamental, and hybrid. In addition, this article investigates resource allocation metrics with all advantages and limitations related to chosen resource allocation mechanisms in networks.

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“…Resource allocation dynamically is given importance for containers as explored in [11], [16], [17]. Usage of micro services based applications with Docker support is also evident in the literature [7], [12], [13], [23].…”

Section: Related Workmentioning

confidence: 99%

Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments

Gogineni,

2024

Preprint

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Containerization became indispensable in distributed environments for packaging software and dependencies in a lightweight executable container. In the era of big data and availability of cloud infrastructure, it is more so as distributed applications are more resource and data-intensive. Such High Performance Computing (HPC) applications are deployed in containerized services. However, data-intensive nature of those applications lead to poor performance unless scheduler considers it. In this paper, not only load balancing of containers but also performance of underlying applications is considered. Towards this end, a scheduling algorithm with unified optimization considering load balancing and also application performance is proposed. This algorithm, named Contention-aware Greedy Heuristic Scheduling and Load Balancing for Containers (CGHSLBC), helps in improving performance of containerization in distributed environments. Problems associated with containerization in terms of balancing load and also application performance are NP-hard. CGHSLBC has heuristics to deal with such issues. Empirical study has revealed that CGHSLBC better application performance besides balancing load of containerized services in cloud infrastructure. We also proposed a learning based methodology to schedule and balance load for containers. It is based on Deep Reinforcement Learning (DRL) where state change is continuously monitored while making well informed scheduling decisions. An algorithm named Reinforcement Learning based Dynamic Scheduling (RLbDS) is proposed and empirical study has revealed that it shows better performance over state of the art methods.

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Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Cited by 40 publications

References 22 publications

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Resource Allocation in Fog Computing: A Systematic Review

Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments

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