Article
Computer Science, Hardware & Architecture
Posham Bhargava Reddy, Chapram Sudhakar
Summary: The edge-fog-cloud computing system consists of three layers: edge or IoT layer, fog layer, and cloud layer. Tasks with different characteristics can be scheduled to run at the appropriate layer based on their computation or communication demands. A scheduling algorithm based on the osmotic approach is proposed to minimize execution time, meet task deadlines, and maximize resource utilization at the fog layer. The algorithm outperforms traditional random and round-robin task offloading algorithms in terms of performance.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Telecommunications
Qian Ren, Kui Liu, Lianming Zhang
Summary: With the development of wireless communication technology and IoT, fog computing architecture has become a research hotspot. To address task offloading in fog computing, researchers propose a globally optimal multi-objective optimization algorithm and establish a performance model using network calculus theory.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Artificial Intelligence
Mirsaeid Hosseini Shirvani, Reza Noorian Talouki
Summary: This paper proposes a hybrid algorithm for scheduling scientific workflows on hybrid cloud architecture, addressing the optimization of both makespan and monetary cost. By utilizing simulated annealing and task duplication algorithms, the proposed approach significantly improves efficiency and performance compared to existing methods.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Reza NoorianTalouki, Mirsaeid Hosseini Shirvani, Homayun Motameni
Summary: This article discusses the task scheduling problem in cloud computing and the limitations of existing heuristic algorithms. It proposes a new scheduling algorithm based on task priority strategy and task duplication methods to address the task scheduling problem in heterogeneous cloud computing systems. Experimental results demonstrate the significant advantages of this algorithm in terms of scalability and efficiency.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Eyhab Al-Masri, Alireza Souri, Habiba Mohamed, Wenjun Yang, James Olmsted, Olivera Kotevska
Summary: This article proposes a cooperative energy-aware resource allocation and scheduling strategy based on the TOPSIS multi-criteria decision-making method. The method achieves load balancing by allocating and scheduling virtual machine resources and considers energy efficiency as an optimization objective. Experimental results show that the proposed approach outperforms existing algorithms in terms of energy savings and execution time.
INTERNET OF THINGS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mustafa Ibrahim Khaleel
Summary: This paper proposes a method for modeling scientific workflow scheduling as a multi-objective optimization problem to balance energy consumption and scheduling reliability. By placing tasks with low computational requirements on fog resources and using a reliability-aware stepwise performance-to-power ratio procedure to reduce energy consumption. Simulation results show that this method has higher reliability under minimized energy constraints.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Automation & Control Systems
Naela Rizvi, Dharavath Ramesh, P. C. Srinivasa Rao, Koushik Mondal
Summary: This study proposes an intelligent fuzzy scheduler that utilizes the salp swarm algorithm to learn and optimize fuzzy task-resource allocation rules. It addresses complex and uncertain computation offloading problems in fog computing. Experimental results demonstrate that the proposed approach outperforms other classical algorithms in workflow scheduling problems.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ali Kamran, Umar Farooq, Ihsan Rabbi, Kashif Zia, Muhammad Assam, Hadeel Alsolai, Fahd N. Al-Wesabi
Summary: Scheduling plays a vital role in cloud computing, but current algorithms are not efficient in processing different sizes and types of workflows. This paper proposes an improved unified mechanism that shows promising results across multiple workflows. Future work will focus on studying the impact of compute time on optimization parameters.
Article
Chemistry, Multidisciplinary
Ahmad Naseem Alvi, Muhammad Awais Javed, Mozaherul Hoque Abul Hasanat, Muhammad Badruddin Khan, Abdul Khader Jilani Saudagar, Mohammed Alkhathami, Umar Farooq
Summary: Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system, with C-V2X infrastructure comprising multiple RSUs for applications like emergency response, traffic management, and infotainment. Vehicles' limited processing capabilities necessitate the use of fog computing servers to handle offloaded tasks near RSUs. By designing a utility function and proposing a knapsack-based task scheduling algorithm, the proposed scheme optimizes fog nodes to scrutinize and process high-priority tasks efficiently.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Alaa Albtoush, Farizah Yunus, Khaled Almi'ani, Noor Maizura Mohamad Noor
Summary: This paper proposes level- and hierarchy-based scheduling approaches for scientific workflow scheduling in the cloud. The level-based approach assigns tasks to virtual machines based on a utility function, while the hierarchy-based approach reduces data dependency between task groups and employs a fair-share strategy for virtual machine allocation. The results show that both approaches improve execution time and cost by an average of 27% compared to benchmarked algorithms.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
V. Vijayalakshmi, M. Saravanan
Summary: This paper presents a novel multi-objective energy efficient task scheduling model for fog-cloud Industrial Internet of Things (IIoT) systems. The model utilizes reinforcement learning and task classification to improve energy efficiency. Experimental results demonstrate its superiority over existing task scheduling models.
Article
Computer Science, Hardware & Architecture
Yung-Ting Chuang, Chiu-Shun Hsiang
Summary: The paper proposed a robust system that considers energy consumption, execution time, load balancing, and popularity in offloading decisions, aiming to maximize the execution efficiency of mobile devices and minimize their overall energy consumption.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Zulfiqar Ahmad, Ali Imran Jehangiri, Nader Mohamed, Mohamed Othman, Arif Iqbal Umar
Summary: Cloud computing is a virtualized, scalable, ubiquitous, and distributed computing paradigm. We propose a fault-tolerant and data-oriented scientific workflow management and scheduling system in cloud computing to efficiently execute scientific workflow tasks.
Article
Computer Science, Information Systems
Hoda Taheri, Saeid Abrishami, Mahmoud Naghibzadeh
Summary: This study introduces a cloud broker for executing Deadline-constrained Periodic scientific Workflows (BDPW), which uses both reserved and on-demand resources to minimize cost. The broker utilizes container technology to decrease VM provisioning delay and adopts a hybrid scheduling method for static planning and dynamic scheduling, adapting to uncertainties.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Information Systems
Habiba Mohamed, Eyhab Al-Masri, Olivera Kotevska, Alireza Souri
Summary: This paper proposes OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous edge devices. It optimizes resource allocation by capturing offloadable task requirements, reducing costs and energy consumption, and increasing the likelihood of successful task offloading.
Article
Computer Science, Theory & Methods
Vincenzo De Maio, Radu Prodan, Shajulin Benedict, Gabor Kecskemeti
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2016)
Article
Computer Science, Information Systems
Ennio Torre, Juan J. Durillo, Vincenzo de Maio, Prateek Agrawal, Shajulin Benedict, Nishant Saurabh, Radu Prodan
INFORMATION AND SOFTWARE TECHNOLOGY
(2020)
Article
Computer Science, Information Systems
Ivan Lujic, Vincenzo De Maio, Ivona Brandic
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2020)
Article
Computer Science, Information Systems
Ivan Lujic, Vincenzo De Maio, Srikumar Venugopal, Ivona Brandic
Summary: In this study, we propose a framework called SEA-LEAP that enables on-the-fly deployment of on-demand analytics in edge computing environments. By utilizing a new mechanism for tracking data movements and a generic control mechanism, SEA-LEAP minimizes overall latency by performing adaptive data movements.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Josip Zilic, Vincenzo De Maio, Atakan Aral, Ivona Brandic
Summary: This paper proposes a fault-tolerant offloading method based on predictions and support vector regression, which is applied to the Markov Decision Process for offloading decisions.
PROCEEDINGS OF THE 5TH INTERNATIONAL WORKSHOP ON EDGE SYSTEMS, ANALYTICS AND NETWORKING (EDGESYS'22)
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Tu Mai Anh Do, Ewa Deelman
Summary: With the end of Moore's Law and the limitations of Von Neumann's architecture, scientists are seeking alternatives to meet the increasing computing demands of scientific applications, and quantum computing appears to be the most promising. However, the current limitations of quantum devices require them to interoperate with classical systems, forming hybrid quantum systems. Variational Quantum Algorithms have emerged as the most promising approach to achieve quantum advantage, but their execution time and accuracy are influenced by various hyperparameters. Therefore, providing methods for developers to select the right set of parameters is crucial.
2022 IEEE 18TH INTERNATIONAL CONFERENCE ON E-SCIENCE (ESCIENCE 2022)
(2022)
Article
Computer Science, Hardware & Architecture
Atakan Aral, Vincenzo De Maio, Ivona Brandic
Summary: Wireless sensor networks are important for monitoring applications, but limitations in energy, processing power, and network bandwidth hinder real-time requirements in IoT applications. Deploying edge nodes in urban areas requires consideration of reliability and environmental sustainability. This paper proposes the ARES algorithm, which uses multi-objective optimization and a dynamic Bayesian network model to achieve sustainable and reliable edge node deployment in urban areas.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Alfredo Cuzzocrea, Vincenzo De Maio, Edoardo Fadda
2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020)
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Vincenzo De Maio, Ivona Brandic
PROCEEDINGS OF THE 2019 ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING (ICPE '19)
(2019)
Proceedings Paper
Computer Science, Hardware & Architecture
Vincenzo De Maio, Ivona Brandic
2018 18TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID)
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Ivan Lujic, Vincenzo De Maio, Ivona Brandic
2018 IEEE 2ND INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING (ICFEC)
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Ivan Lujic, Vincenzo De Maio, Ivona Brandic
2017 IEEE 1ST INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING (ICFEC)
(2017)
Proceedings Paper
Computer Science, Information Systems
Vincenzo De Maio, Gabor Kecskemeti, Radu Prodan
2016 IEEE/ACM 9TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC)
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Vincenzo De Maio, Gabor Kecskemeti, Radu Prodan
2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID)
(2016)
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)