An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

Huang, Yinfeng; Zhang, Shizheng; Wang, Bo

doi:10.3390/electronics12092064

Cited by 8 publications

(2 citation statements)

References 41 publications

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“… Huang, Zhang & Wang (2023) focused on addressing the task scheduling problem with deadline and security constraints in hybrid cloud environments. The approach involved formulating the problem into mixed-integer non-linear programming and proposing a novel algorithm named SPGA, which integrated swarm intelligence into the genetic algorithm framework.…”

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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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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“…However, static algorithms are insufficient due to the dynamic nature of workload changes over time in cloud computing environments. Accordingly, it is crucial to implement dynamic schemes for distributing the workload among VMs [13,14]. Task scheduling is an NP-hard problem in the CC due to its dynamic and heterogeneous nature.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid MSA-CSA Algorithm for Cloud Computing Task Scheduling Problems

Alsubai,

Garg,

Alqahtani

2023

Symmetry

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Recently, the dynamic distribution of resources and task scheduling has played a critical role in cloud computing to achieve maximum storage and performance. The allocation of computational tasks in the cloud is a complicated process that can be affected by some factors, such as available network bandwidth, makespan, and cost considerations. However, these allocations are always non-symmetric. Therefore, it is crucial to optimize available bandwidth for efficient cloud computing task scheduling. In this research, a novel swarm-based task scheduling with a security approach is proposed to optimize the distribution of tasks using available resources and encode cloud information during task scheduling. It can combine the Moth Swarm Algorithm (MSA) with the Chameleon Swarm Algorithm (CSA) for the task scheduling process and utilizes the Polymorphic Advanced Encryption Standard (P-AES) for information security of cloud scheduled tasks. The approach offers a new perspective for utilizing swarm intelligence algorithms to optimize cloud task scheduling. The integration of MSA and CSA with P-AES enables the approach to provide efficient and secure task scheduling by exploiting the strengths of used algorithms. The study evaluates the performance of the proposed approach in terms of the degree of imbalance, makespan, resource utilization, cost, average waiting time, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using a wide range of tasks from 1000 to 5000. The results show that the approach provides an innovative solution to the challenges of task scheduling in cloud environments and improves the performance of cloud services in terms of effectiveness and security measures.

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Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

Dwivedi,

Prasad

2024

Adv Control Appl

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The Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L2‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

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An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

Cited by 8 publications

References 41 publications

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

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

A Novel Hybrid MSA-CSA Algorithm for Cloud Computing Task Scheduling Problems

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

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