An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment

Yakubu, Ismail Zahraddeen; Murali, M.

doi:10.1007/s12652-023-04544-6

Cited by 17 publications

(8 citation statements)

References 30 publications

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“…In the study, it is aimed to make the results reliable by making comparisons with well-known algorithms in the literature regarding the problem addressed. They are MHHO [38], IAHA [47], DMFO [48], EPSO [8], AEOSSA [23], Chaotic-WOA [49], and SCSO [15] algorithms, which published after 2021. These algorithms are comprehensive studies on the problem recently described in the fog-cloud environment.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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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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Section: Simulation Results and Analysismentioning

confidence: 99%

“…Since the devices in this layer are limited in number and have limited resources, the task distributions should be allocated according to priority. Equation 6 is used to calculate this priority as in [38]. In this regard, the priority of each task is evaluated.…”

Section: Priority 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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“…Let's consider that makespan refers to the total time needed to finish a set of tasks on 𝑅 𝑗 . The makespan can be calculated using the following [24]:…”

Section: B Problem Formulationmentioning

confidence: 99%

“…𝐹 𝑏 = {𝑃 Fb , 𝐹 Fb }, ∀ 1 < 𝑃 Fb < 𝑚 (24) where Gb 1 : a global best particle. {position, fitness}, 𝑃 Fb : Farthest-best position, and 𝐹 Fb : Farthest-best fitness.…”

Section: B Farthest-best Particle Mechanism (Fbpm)mentioning

confidence: 99%

Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments

Al-muqarm

2024

IJEETC

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The data generated by the IoT needs a powerful platform such as cloud computing for data processing. However, the cloud faces challenges when dealing with various types of resources, high delay, and cost, this represents a substantial challenge in scheduling tasks. Therefore, the need appeared to introduce the concept of fog. To address these limitations, optimization algorithms such as PSO were used. In traditional PSO, all particles in the swarm are influenced by a single global best particle (Gbest), if it becomes stuck in a local optimum, all the particles will move closer to it, thus, the PSO may easily get trapped in premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified PSO called PSO Optimized Leader (PSO-OL). These modifications on four stages: Firstly, a method to ensure swarm diversity in the initialization phase is introduced. Secondly, to reduce the chance of the population getting trapped in a local optimum, the farthest-best particle is introduced. Third, in addition to the primary Gbest, a second Gbest represents a different good particle presented to explore multiple promising regions. Finally proposed a new crossover operator to get an optimized leader. The PSO-OL approach was evaluated and the results show the effectiveness of the enhanced leader by 40% with farthest-best, 45% with second-Gbest when compared to standard PSO, and when compared to scheduling algorithms where outperforms the other algorithms by minimizing makespan by 34%, cost by 14%, and increasing throughput by 75%, in comparison to existing load balancing and scheduling methods: RR, BLA, MPSO, ETS, and TCaS.

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“…Cloud computing is built upon service-oriented architecture, which enables it to offer various services such as Database-as-a-Service (DbaaS), Identity-as-a-Service (IDaaS), and the broader concept of Anything-as-a-Service (XaaS). This architecture has revolutionized resource management in industry and academia, providing an efficient and dynamic approach [8]. The cloud system's dynamic nature is a crucial characteristic, accommodating numerous users, devices, networks, organizations, and resources that frequently connect and disconnect from the system.…”

Section: Introductionmentioning

confidence: 99%

MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing

ZHOU,

2023

IJACSA

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Cloud computing has emerged as a transformative technology, offering remote access to various computing resources. However, efficiently managing these resources while curbing escalating energy consumption remains a critical challenge. In response, this paper presents the Micro-Genetic Algorithm with Cuckoo Search (MG-CS), a novel approach for enhancing cloud computing efficiency. MG-CS optimizes load balancing and power reduction and significantly contributes to reducing operational costs, ensuring compliance with service level agreements, and enhancing overall service quality. Our experiments showcase MG-CS's versatility in achieving a wellbalanced distribution of workloads, resource optimization, and substantial energy savings. This multifaceted approach redefines cloud resource management, offering an environmentally sustainable and cost-effective solution. By introducing MG-CS, this research addresses the pressing challenges in cloud computing, aligning it with environmental responsibility and economic efficiency.

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An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment

Cited by 17 publications

References 30 publications

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

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

Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments

MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing

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