A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment

Apat, Hemant Kumar; Bhaisare, Kunal; Sahoo, Bibhudatta; Maiti, Prasenjit

doi:10.1007/978-981-33-6081-5_26

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Edge computing is appealing to several researchers because of its notable features such as scalability, energy efficiency, security, elasticity, and reduced processing. The cloud computing approach partly tackles these difficulties [6][7]. The research contributes to the evolving field of edge computing by addressing the dual challenges of energy efficiency and security.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Hybrid Metaheuristic Algorithm based energy efficient Authentication method for IoT enable edge computing

Mishra,

Kumar,

Awasthi

2024

Preprint

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With the proliferation of Internet of Things (IoT) devices and the increasing demand for low-latency data processing, edge computing has emerged as a critical paradigm. However, challenges related to energy efficiency and security in this dynamic and distributed environment persist. This paper proposes a novel Hybrid Election-based Ladybug Beetle Optimization (ELBO-H) method tailored to address these challenges concurrently. By synergizing the principles of Election-based Optimization Algorithm (EBOA) and the Ladybug Beetle Optimization (LBO), our approach aims to enhance energy efficiency and bolster authentication protocols in IoT-enabled edge computing environments. We begin by elucidating the exigencies of energy-efficient operations and trust-based authentication within the burgeoning landscape of IoT and edge computing. The proposed ELBO-H method involves integrating this algorithm into the edge computing architecture, optimizing energy usage while ensuring robust security measures between IoT devices, edge nodes. To evaluate the proposed method, we conducted simulations in a controlled environment, considering various scenarios and workload conditions. Results indicate a significant improvement in energy efficiency without compromising security. Our proposed ELBO-H method demonstrates an average Attack detection rate of 94.28% compared to the IB-SEC, G-BHO, DEEC-KSA, and CPSO methods, which have average Attack detection rates of 78.23%, 72.45%, 74.89%, and 52.67%, respectively.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Hybrid Metaheuristic Algorithm based energy efficient Authentication method for IoT enable edge computing

Mishra,

Kumar,

Awasthi

2024

Preprint

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“…This time includes processing time, queueing delay, and transmission time and is calculated based on Equation 11. The completion time for schedule is represented by Mt and is obtained based on Equation 12, inspired by [39].…”

Section: 12completion (Makespan) Time Parameter Definitionmentioning

confidence: 99%

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

Anka

2024

Preprint

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Real-time processing demands are also increasing significantly with the proliferation of Internet of Things (IoT)-based smart systems. At the same time, the volume of data generated, and computational workload are also increasing significantly. In this regard, fog-cloud architectures are proposed to alleviate the excessive load on cloud servers. However, since the resources of fog nodes and the virtual machines on them are limited, efficient management mechanisms are required. As the volume and amount of data increases, computational and processing costs increase, and delays are inevitable. However, these requirements need to be resolved to increase QoS and customer satisfaction. One of the most important criteria to achieve this goal is accurate and effective task allocation and scheduling. Since the scheduling is a type of NP-hard problem, the metaheuristic approach is used. In this study, an Enhanced variant of the Sand Cat Swarm Optimization algorithm (ESCSO) is proposed to efficient scheduling according to tasks priorities and a suitable fitness function based on completion (makespan) time, energy consumption and execution cost parameters is defined. In the proposed algorithm, global search ability and population diversity of the SCSO is improved based on the chaotic map. Also, its exploration and exploitation mechanisms are enhanced based on Brownian and Levy motion strategies. Besides, the control mechanism of the phases transition is redefined to increase balancing performance. The proposed method is compared with SCSO, Improved Artificial Hummingbird Algorithm (IAHA), Discrete Moth Flame Optimization (DMFO), Enhanced Particle Swarm Optimization (EPSO), Chaotic-Whale Optimization Algorithm (CWOA), Modified Harris-Hawks Optimization (MHHO), and Hybrid Artificial Ecosystem Optimization and Salp Swarm Algorithm (AEOSSA) algorithms and analyzed on the three parameters in two different scenarios. The obtained results present that the ESCSO algorithm outperform others in all cases.

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“…The proposed solution performed well, but the cost of the system was high, making it unaffordable to a small company. Taneja and Davy 49 proposed a resource‐aware service placement solution, a good approach for QoS optimization, that first considered the highest‐demand application tasks. Thus, the mechanism resulted in a swamped network with idle and waiting states with low‐capacity devices/nodes.…”

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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A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment

Cited by 6 publications

References 14 publications

RETRACTED: Hybrid Metaheuristic Algorithm based energy efficient Authentication method for IoT enable edge computing

RETRACTED: Hybrid Metaheuristic Algorithm based energy efficient Authentication method for IoT enable edge computing

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

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

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