Article
Computer Science, Information Systems
Samar Hussni Anbarkhan, Mohamed Ali Rakrouki
Summary: This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm to address the issue of high time and cost in scheduling workflow tasks in a cloud computing environment. The algorithm combines intensive tasks to reduce particle dimensions and ensure initial particle quality. It optimizes particle initialization and integrates a self-adaptive function to determine the best direction of the particles. Experimental results show that the proposed enhanced PSO algorithm achieves faster convergence speed and better performance in task execution.
Article
Computer Science, Information Systems
Kaili Shao, Hui Fu, Bo Wang
Summary: Task scheduling in cloud services is a critical issue that needs improvement for better performance. We propose a hybrid heuristic algorithm called PGSAO, which is a combination of genetic algorithm (GA) and particle swarm optimization (PSO), to solve the task-scheduling problem in heterogeneous cloud computing. Extensive simulated experiments demonstrate that on average, PGSAO outperforms other meta-heuristic and hybrid heuristic algorithms by achieving 23.0-33.2% more accepted tasks and 27.9-43.7% higher resource utilization.
Article
Computer Science, Artificial Intelligence
Xiaoyong Tang, Cheng Shi, Tan Deng, Zhiqiang Wu, Li Yang
Summary: The study introduces a random matrix particle swarm optimization scheduling algorithm for cloud service scheduling, as well as two parallel algorithms to reduce its time complexity. Experimental results demonstrate that the GPU-accelerated algorithm performs better compared to the others.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Muhammad Usman Sana, Zhanli Li, Fawad Javaid, Muhammad Wahab Hanif, Imran Ashraf
Summary: This study proposes a novel encoding technique using blockchain and Improved Particle Swarm Optimization (IPSO) to improve the makespan value and scheduling time. The experimental results indicate that the proposed algorithm is practical and secure in handling flexible job scheduling and outperforms the state-of-the-art task scheduling algorithms.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Yun Wang, Xingquan Zuo
Summary: This paper proposes a cloud workflow scheduling approach that combines particle swarm optimization and idle time slot-aware rules to minimize the execution cost of a workflow application. The approach utilizes a new particle encoding and decoding procedure to handle tasks' priorities and outperforms comparative algorithms in terms of both execution cost and meeting deadlines according to experiments.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Information Systems
Seema A. Alsaidy, Amenah D. Abbood, Mouayad A. Sahib
Summary: Task scheduling is a significant issue in cloud computing, and this paper proposes an improved initialization method for particle swarm optimization (PSO) using heuristic algorithms. By initializing PSO with longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms, the performance can be significantly enhanced, compared to traditional PSO and other methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Bo Wang, Junqiang Cheng, Jie Cao, Changhai Wang, Wanwei Huang
Summary: This paper focuses on the task scheduling problem in Device-Edge-Cloud Cooperative Computing (DE3C) and proposes an integer particle swarm optimization method (IPSO) to efficiently solve the sub-problems of offloading decision, task assignment, and task ordering. Experimental results show significant improvements in SLA satisfaction and resource efficiency compared to classical and state-of-the-art methods.
PEERJ COMPUTER SCIENCE
(2022)
Review
Computer Science, Artificial Intelligence
Yousef Qawqzeh, Mafawez T. Alharbi, Ayman Jaradat, Khalid Nazim Abdul Sattar
Summary: This review focuses on recent publications of swarm intelligence algorithms in scheduling and optimization problems, highlighting the increasing number of algorithms for cloud computing optimization. Many emerging algorithms have been developed based on amendments to the original SI algorithms, especially the PSO algorithm. The main goal of this work is to inspire researchers to innovate new SI-based solutions for handling complex and multiobjective computational problems.
PEERJ COMPUTER SCIENCE
(2021)
Article
Automation & Control Systems
Ibrahim Attiya, Mohamed Abd Elaziz, Laith Abualigah, Tu N. Nguyen, Ahmed A. Abd El-Latif
Summary: This article proposes a new task scheduling method, called MRFOSSA, for optimizing the scheduling of IoT application tasks in cloud computing. This method uses a hybrid swarm intelligence approach, utilizing a modified Manta ray foraging optimization algorithm and the salp swarm algorithm, to improve local search ability and outperform other metaheuristic techniques.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Software Engineering
Narayana Potu, Chandrashekar Jatoth, Premchand Parvataneni
Summary: In this study, an extended particle swarm optimization (EPSO) algorithm was developed to optimize task scheduling in cloud-fog environments, aiming to improve resource efficiency and minimize task completion time. Experimental results showed that the performance of the proposed method is comparable to traditional techniques.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Information Systems
An Song, Wei-Neng Chen, Xiaonan Luo, Zhi-Hui Zhan, Jun Zhang
Summary: This article proposes a novel workflow model with composite tasks, which can manage complex workflows and address data transmission between sub-tasks. To solve this problem, a nested particle swarm optimization and a fast version of nested particle swarm optimization are devised.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Automation & Control Systems
Bo Wang, Zhifeng Zhang, Ying Song, Ming Chen, Yangyang Chu
Summary: This paper presents the first attempt to apply Quantum Particle Swarm Optimization (QPSO), a type of Swarm Intelligence and Evolutionary Algorithm (SI&EAs), to the task scheduling problem in Device-Edge-Cloud Cooperative Computing (DE3C). The authors propose a QPSO-based method to solve the problem efficiently and conduct simulated experiments to evaluate the performance. The findings demonstrate that QPSO outperforms other heuristics and SI&EAs in terms of user satisfaction and resource efficiency, while existing improvement methods have limited impact on QPSO for solving large-scale problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Mohd Sha Alam Khan, R. Santhosh
Summary: Cloud computing offers a variety of services and has powerful processing capacity, but struggles with resource allocation. A task scheduling method based on a hybrid optimization algorithm is proposed in the study to reduce waiting time effectively.
Article
Green & Sustainable Science & Technology
Mohammed Alghamdi
Summary: As more people utilize the cloud, more employment opportunities become available. However, solving the task scheduling problem in cloud computing becomes a difficult NP-hard optimization issue due to various constraints. An intelligent resource allocation system can cut down costs and waste. Research shows that applying Artificial Neural Networks (ANN) in cloud computing can lead to better outcomes.
Article
Computer Science, Information Systems
Tieliang Gao, Qigui Tang, Jiao Li, Yi Zhang, Yiqiu Li, Jingya Zhang
Summary: Edge-cloud computing is an efficient approach to address high latency in mobile cloud computing. This paper proposes a hybrid heuristic method based on Particle Swarm Optimization to solve the task offloading and service caching problem. Experimental results show that the proposed method outperforms other methods in terms of user satisfaction, resource efficiency, and processing efficiency.