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
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
Computer Science, Interdisciplinary Applications
Naela Rizvi, Ramesh Dharavath, Damodar Reddy Edla
Summary: In this paper, a Hybrid Spider Monkey Optimization (HSMO) algorithm is proposed for QoS constrained workflow scheduling in the cloud. The algorithm optimizes makespan and cost while meeting budget and deadline constraints, outperforming existing ABC, Bi-Criteria PSO, and BDSD algorithms.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
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, Hardware & Architecture
Yuanqing Xia, Yufeng Zhan, Li Dai, Yuehong Chen
Summary: This paper presents a dynamic multi-workflow scheduling model in a cloud environment and proposes a new scheduling algorithm named MT. The MT algorithm considers the heterogeneity of resources and uses the TOPSIS method to rank and select resources for tasks. Simulation experiments demonstrate that the proposed algorithm effectively reduces the maximum completion time and cost of multiple workflows.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Automation & Control Systems
Yi Xie, Yuhan Sheng, Moqi Qiu, Fengxian Gui
Summary: With the increasing data and computing requirements, the transition of scientific and business applications to cloud platforms has led to the importance of cloud workflow scheduling. Existing heuristic and metaheuristic algorithms have limitations in solving this NP-hard problem. To address this, a novel adaptive decoding biased random key genetic algorithm is proposed, which improves the search efficiency and accuracy for cloud workflow scheduling.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Ali Belgacem, Kadda Beghdad-Bey
Summary: Modern businesses are transitioning to cloud computing platforms for workflow applications, facing challenges in scheduling due to computational intensity, task dependencies, and resource heterogeneity. This paper addresses the trade-off between makespan and cost, proposing a HEFT-ACO approach to minimize them. Experimental results demonstrate the algorithm's superiority over basic ACO, PEFT-ACO, and FR-MOS.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Celestin Tshimanga Kamanga, Emmanuel Bugingo, Simon Ntumba Badibanga, Eugene Mbuyi Mukendi
Summary: Cloud computing is recognized as the best way to execute and manage high-performance applications, but selecting the right configuration for optimal cost and makespan remains complex. To address this, researchers proposed a three-variant algorithm to help users schedule their workflow applications on clouds and reduce makespan and costs.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Zengpeng Li, Huiqun Yu, Guisheng Fan
Summary: This paper proposes two algorithms, CSDW and N-WOA, to execute workflow applications in clouds with the minimum cost and meeting the deadline constraints. The experimental results demonstrate the advantages of these two algorithms in meeting the deadlines and reducing the execution costs.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Software Engineering
Neeraj Arora, Rohitash K. Banyal
Summary: This article introduces a hybrid algorithm PSO-GWO for solving the NP-hard problem of workflow scheduling in cloud computing environment, which outperforms the standard PSO and GWO algorithms in terms of performance.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Minhaj Ahmad Khan
Summary: This paper proposes a power-aware cloudlet scheduling algorithm that aims at reducing request processing time through mapping cloudlets to virtual machines while minimizing energy consumption and cost. Experimental results show a significant overall performance improvement over other well-known cloudlet scheduling algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Morteza Mollajafari, Mohammad H. Shojaeefard
Summary: This paper proposes a multi-objective workflow scheduling algorithm named TC3PoP customized for cloud environments, addressing the challenges of simplifying assumptions, limited search space coverage, and high time complexity in existing methods. Experimental results show that TC3PoP solutions outperform NSGA-II in terms of effectiveness and diversity, with a significantly faster speed.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Telecommunications
Prathibha Soma, B. Latha, V. Vijaykumar
Summary: This article proposes a workflow scheduling algorithm based on a new multi-objective optimization model, considering both energy efficiency and makespan. Compared to existing systems, this algorithm shows better performance in reducing energy consumption and improving makespan.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
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, Hardware & Architecture
Zhao-hong Jia, Lei Pan, Xiao Liu, Xue-jun Li
Summary: This paper presents a workflow scheduling algorithm based on stable matching game theory to minimize workflow completion time and ensure fairness among tasks. By using local optimization methods and a novel evaluation metric, the algorithm's performance is improved and outperforms other algorithms in comprehensive experiments.
JOURNAL OF SUPERCOMPUTING
(2021)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)