Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

Zavieh, Hadi; Javadpour, Amir; Ja’fari, Forough; Sangaiah, Arun Kumar; Słowik, Adam

doi:10.1007/s40815-023-01605-y

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…The objective of TSCC is to optimize various performance metrics, such as execution time, resource utilization, and cost. Effectively addressing this challenge is crucial for maximizing resource utilization in the cloud while meeting the diverse requirements of users and applications 47–49 Task description: The set T = ( t 1 , t 2 , …, t n ) comprises the tasks to be executed.…”

Section: The Proposed Gija Methodsmentioning

confidence: 99%

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

Abualigah,

Hussein,

Almomani

et al. 2024

Trans Emerging Tel Tech

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Task scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. GIJA integrates the principles of the Geyser‐inspired algorithm with the Jaya algorithm, augmented by a Levy Flight mechanism, to address the complexities of task scheduling optimization. The motivation for this research stems from the increasing demand for efficient resource utilization and task management in cloud computing, driven by the proliferation of Internet of Things (IoT) devices and the growing reliance on cloud‐based services. Traditional task scheduling algorithms often face challenges in handling dynamic workloads, heterogeneous resources, and varying performance objectives, necessitating innovative optimization techniques. GIJA leverages the eruptive dynamics of geysers, inspired by nature's efficiency in channeling resources, to guide task scheduling decisions. By combining this Geyser‐inspired approach with the simplicity and effectiveness of the Jaya algorithm, GIJA offers a robust optimization framework capable of adapting to diverse cloud computing environments. Additionally, the integration of the Levy Flight mechanism introduces stochasticity into the optimization process, enabling the exploration of solution spaces and accelerating convergence. To evaluate the efficacy of GIJA, extensive experiments are conducted using synthetic and real‐world datasets representative of cloud computing workloads. Comparative analyses against existing task scheduling algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, and GIAA, demonstrate the superior performance of GIJA in terms of solution quality, convergence rate, diversity, and robustness. The findings of GIJA provide a promising solution quality for addressing the complexities of task scheduling in cloud environments (95%), with implications for enhancing system performance, scalability, and resource utilization.

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Section: The Proposed Gija Methodsmentioning

confidence: 99%

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

Abualigah,

Hussein,

Almomani

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“… Zavieh et al (2023) introduced a novel methodology termed the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP) to tackle the task scheduling and energy consumption challenges prevalent in cloud computing environments. By integrating the advantages of Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques, this approach adeptly selected physical and virtual machines while operating under fuzzy conditions.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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An Inverse BCC Model for Evaluating and Ordering Decision-Making Units under Fuzziness

Zavieh,

Niksefat,

Nasseri

2024

Fuzzy. Info. Tech.

Self Cite

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One of the most prevalent problems in linear programming as one of the convenient models in the field of operation research environment is Data Envelopment Analysis (DEA), which supports the efficiency of Decision-Making Units (DMUs). Usually, accurate data are common; however, in the real world, we are facing an inaccurate situation. In this paper, a new model for assessing DMUs in a fuzzy environment is presented; we consider the inverse DEA model with the variable return to scale with fuzzy numbers for fluctuating data. A case study is given to illustrate its performance. KEYWORDSfuzzy mathematical programming; fuzzy ordering; data envelopment analysis 1 Introduction D ata Envelopment Analysis (DEA) is a technique for deliberating the performance of Decision-Making Units (DMUs) with numerous inputs and outputs. Each DMU uses several inputs to generate several outputs. Moreover, the performance of DMUs is evaluated based on obtained inputs and outputs. DEA has been widely used in many fields of science. Pascoe et al. [1] used DEA to assess management alternatives in the presence of multiple objectives. Examples of the use of DEA to assess the financial effectiveness of insurance companies are presented in Ref. [2]. Nasseri and Khatir [3] organized a two-stage DEA model by taking into account undesirable output with fuzzy stochastic data. As one step forward, the Inverse of the DEA (IDEA) model has been recently introduced by Wei et al. [4] so that it tries to answer the question: If input (output) in a DMU is agitated, then how output (input) should be changed to keep the relative performance of the DMU? IDEA models have different applications in practical cases [5−7] . Furthermore, the IDEA model with a variable return to scale (Inverse Banker, Charnes, and Cooper model (IBCC)) is suggested by Lertworasirikul et al. [8] Next, Ghiyasi [9] proposed several problematic issues, and he challenged that the suggested Multi-Objective Linear Programming (MOLP) model by Lertworasirikul et al. [8] is not as effective as it was claimed.Fuzzy uncertainty, grey uncertainty, and rough uncertainty are intertwined facets of imprecision in

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

Cited by 3 publications

References 34 publications

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

An Inverse BCC Model for Evaluating and Ordering Decision-Making Units under Fuzziness

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