Article
Computer Science, Information Systems
Xuewen Xia, Huixian Qiu, Xing Xu, Yinglong Zhang
Summary: In this paper, a multi-objective genetic algorithm (MOGA) is proposed and applied to optimize workflow scheduling problems under the cloud computing environment. An initialization scheduling sequence scheme is introduced to enhance search efficiency, and the longest common subsequence (LCS) is integrated into the genetic algorithm (GA) to achieve a balance between exploration and exploitation. Experimental results demonstrate that the proposed GALCS algorithm outperforms ordinary GA and other state-of-the-art algorithms in finding a better Pareto front.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Prashant Shukla, Sudhakar Pandey
Summary: With the development of current computing technology, workflow applications have become more important in various fields. Researchers are paying more attention to workflow scheduling algorithms (WSA) as a real-time concern. However, it remains challenging to develop a single coherent algorithm that meets multiple criteria. This paper presents an efficient meta-heuristic approach called Multi-objective Artificial Algae (MAA) algorithm for scheduling scientific workflows in a hierarchical fog-cloud environment. The proposed approach shows significant improvements in execution time, energy consumption, and total cost compared to previous methodologies.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Multidisciplinary Sciences
Prashant Shukla, Sudhakar Pandey
Summary: This study introduces the Differential Evolution-Grey Wolf Optimization (DE-GWO) technique to enhance the scheduling of scientific workflows under cloud-fog settings. The DE-GWO algorithm demonstrates superior performance across several scientific workflows and performance criteria.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Ulf Leser, Odej Kao
Summary: This study presents a method for locally predicting the runtimes of scientific workflow tasks before execution on heterogeneous compute clusters. The method utilizes microbenchmarks and Bayesian linear regression to quickly profile the workflow and provide uncertainty estimates, achieving improved prediction performance compared to existing baselines.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Vasilios Kelefouras, Karim Djemame
Summary: Efficient application scheduling is crucial for high performance in heterogeneous computing systems. This paper proposes two task scheduling methods for heterogeneous computing systems, which can improve scheduling time and scheduling length, resulting in significant gains in makespan and scheduling time.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2022)
Article
Computer Science, Theory & Methods
Xiaoyong Tang, Wenbiao Cao, Huiya Tang, Tan Deng, Jing Mei, Yi Liu, Cheng Shi, Meng Xia, Zeng Zeng
Summary: This study focuses on the scheduling problem and minimizing the execution cost of large-scale data processing and computing workflow applications running on heterogeneous clouds. The workflow applications are modeled as I/O Data-aware Directed Acyclic Graph (DDAG), and a heuristic cost-efficient task scheduling strategy is proposed.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Computer Science, Information Systems
K. Kalyana Chakravarthi, P. Neelakantan, L. Shyamala, V. Vaidehi
Summary: This paper discusses the importance of resource provisioning and workflow execution in a multi-cloud environment using a pay-as-you-use framework. It proposes a Normalization based Reliable Budget constraint Workflow Scheduling (NRBWS) algorithm to improve the reliability of workflow execution and reduce the makespan under the budget constraint. Simulation results demonstrate that the proposed algorithm outperforms existing heuristics.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Qihang Qiao, Lan Chen, Hong Cai, He Zhang, Zhenjie Yao
Summary: With the development of the IC industry, electronic design automation (EDA) tools have also advanced and migrated to high-performance computing clusters or the cloud. This paper proposes a novel workflow scheduling algorithm HEWS, which achieves better performance through a hierarchical sorting method. Simulation experiments demonstrate that HEWS outperforms conventional methods, reducing both the waiting time and makespan of the workflow significantly.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Information Systems
Yi Xie, Feng-Xian Gui, Wei-Jun Wang, Chen-Fu Chien
Summary: This study formulates the workflow scheduling problem in Heterogeneous Distributed Computing Environments (HDCEs) as an integer programming mathematical model, and proposes a novel two-stage multi-population genetic algorithm with heuristics for workflow scheduling. Extensive experiments show that the proposed algorithm outperforms conventional approaches in various scenarios.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Xiaodong Zhang, Xiaoping Li, Houan Du, Ruben Ruiz
Summary: This article investigates the problem of data processing in the Internet of Things and proposes a scheduling optimization algorithm framework to minimize the maximum completion time of tasks. Experimental results demonstrate the effectiveness of the proposed algorithm for the considered problem.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Theory & Methods
Peyman Paknejad, Reihaneh Khorsand, Mohammadreza Ramezanpour
Summary: This study proposes an enhanced multi-objective co-evolutionary algorithm to address the complexity of workflow scheduling in the cloud environment. By applying enhanced algorithm and fitness function, the convergence issues in traditional algorithms are successfully overcome, resulting in improved performance metrics compared to existing counterparts.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Wakar Ahmad, Bashir Alam, Aman Atman
Summary: Infrastructure as a service model of cloud computing provides high-performance computing systems for scientific workflow applications, but energy-efficient workflow scheduling under budget constraints is a challenging issue. The proposed algorithm RECFPAB aims to reduce energy consumption by fairly distributing available budget for unscheduled tasks.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Information Systems
S. Immaculate Shyla, T. Beula Bell, C. Jaspin Jeba Sheela
Summary: Cloud computing exemplifies emerging knowledge and the ability to provide reliable cloud services. To support resource sharing among clouds, the multi-cloud concept has been established. This research proposes a multi-objective scheduling approach for logical workflow in a multi-cloud environment, aiming to control workflow, balance cost and timeliness, and meet reliability requirements.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Weihong Chen, Guoqi Xie, Renfa Li, Keqin Li
Summary: This study proposes a cost optimization algorithm that combines upward and downward approaches to minimize the execution cost of applications on cloud computing platforms. Experimental results show that the proposed approach is more effective than existing methods under various conditions.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(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)
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)