Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Subramoney, Dineshan; Nyirenda, Clement N.

doi:10.1109/access.2022.3220239

Cited by 28 publications

(7 citation statements)

References 41 publications

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“…Moreover, its fitness function is based on single objective. In [34], a Multi-Swarm (MSPSO) algorithm was proposed to solve the workflow scheduling problem within a three-layer fog architecture by optimizing energy, cost, delay and load balancing parameters. This study did not consider the issue of QoS and users' satisfaction.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…As described in Section 2.1, the VMD decomposition needs to first determine the modal number k, penalty factor α, and mode convergence absolute tolerance tol. We introduce PSO [32][33][34] algorithm to help VMD quickly find the best decomposition parameters and improve the decomposition efficiency. PSO considers each individual of the birds' group as a particle without the physiological characteristics in the searching space.…”

Section: Pso Algorithmmentioning

confidence: 99%

Network traffic prediction model based on improved VMD and PSO‐ELM

Shi¹,

Zhou²,

Feng³

et al. 2023

Int J Communication

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SummaryThe rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO‐VMD combined with multi‐scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO‐ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO‐ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high‐precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD‐PSO‐ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

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“…By dynamically distributing requests based on server performance, the Honey Bee Load Balancing Algorithm helps to ensure that resources are e ciently utilized and prevents overloading any one server. The enhancement of cloud-based services' overall performance and reliability can be achieved through this approach [23].…”

Section: Honey Beementioning

confidence: 99%

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Rajpoot,

Singh,

Pant

2023

Preprint

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Smart healthcare systems are novel innovations that can improve healthcare services by giving individuals details about their medical conditions in real time. However, processing and analyzing huge amounts of information stored in these systems can frequently result in latency and interruptions, negatively impacting the system's efficiency. To solve this issue, this research presents an innovative healthcare framework incorporating cloud and fog computing techniques to reduce latency and enhance whole system performance. The framework has three different tiers: the user tier, the fog tier, and the cloud tier. Health-related information is gathered from users in the user tier using cloud pulses. An optimized Ant Colony Optimization load balancing algorithm subsequently monitors this data. The load balancer allocates user requests to fog servers in the fog tier, considering factors including response time and cost. The fog tier is comprised of fog servers that are located in closer proximity to end-users and are tasked with the real-time processing and analysis of data. The cloud tier accommodates vast quantities of healthcare data and facilitates sophisticated analytics and processing functionalities. The performance of the proposed framework was assessed by implementing it on the Cloud Analyst tool and utilizing metrics such as response time, cost, and system throughput. The experiment findings indicate that the proposed framework performs better in mitigating delay in healthcare compared to conventional healthcare systems. The load balancer that was optimized using Ant Colony Optimization algorithm efficiently allocated the workload among the fog servers, resulting in enhanced system response time and decreased latency. The concept of fog computing proved to be highly productive in mitigating latency by enabling local data processing and analysis near the end-user, thereby reducing reliance on the cloud tier. The empirical findings indicate that the framework put forward can transform the provision of healthcare services, rendering them more streamlined, economical, and focused on the needs of patients.

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Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Cited by 28 publications

References 41 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

Network traffic prediction model based on improved VMD and PSO‐ELM

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

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