Article
Computer Science, Artificial Intelligence
Huanlai Xing, Jing Zhu, Rong Qu, Penglin Dai, Shouxi Luo, Muhammad Azhar Iqbal
Summary: This paper introduces a virtual machine placement problem and proposes an energy-and traffic-aware ant colony optimization algorithm to address it. By incorporating three novel schemes, the algorithm demonstrates effective adaptation to the VMP problem and outperforms various state-of-the-art heuristics and metaheuristics in solution quality.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Kalka Dubey, S. C. Sharma
Summary: Cloud computing offers useful services but also brings security risks to user information privacy. This study proposes an extended intelligent water drop algorithm and a VM allocation algorithm to optimize task execution in a secure cloud environment. Experimental results show the effectiveness of the proposed algorithm compared to existing approaches.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Abadhan Saumya Sabyasachi, Jogesh K. Muppala
Summary: The growing demand for cloud computing has raised concerns about energy usage and load balancing. A proposed algorithm optimizes virtual machine resources to address these issues effectively.
Article
Computer Science, Hardware & Architecture
Hao Feng, Yuhui Deng, Jie Li
Summary: This study proposes a global-energy-aware virtual machine placement strategy to reduce the total energy consumption of data centers, and designs a two-step SAG algorithm to lower the energy consumption of cloud data centers with multiple deployed VMs. Experimental results show that compared with other algorithms, this strategy can reduce the total energy consumption of cloud data centers by 8%-24.9%.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)
Article
Chemistry, Analytical
Mohamed Ali Rakrouki, Nawaf Alharbe
Summary: This paper proposes a new scheduling strategy that combines PSO and GSA algorithms to reduce resource consumption and improve SLA compliance based on QoS status analysis.
Article
Automation & Control Systems
Xiaolong Xu, Ruichao Mo, Xiaochun Yin, Mohanmmad R. Khosravi, Fahimeh Aghaei, Victor Chang, Guangshun Li
Summary: The article introduces a privacy-aware deployment method (PDM) for hosting ML applications in industrial CPCSs to improve implementation performance and resource utility.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Yue Zhao, Chunjie Zhou, Yu-Chu Tian, Jianhui Yang, Xiaoya Hu
Summary: Cyber attacks pose serious threats to the security of cyber-physical systems. This article proposes a resilient control scheme based on cloud computing environments to address the destructive changes in system structure caused by actuator attacks. The scheme consists of a local resilient controller and a cloud-based resilient controller. Simulation experiments on a permanent synchronous motor control system demonstrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ammar Al-Moalmi, Juan Luo, Ahmad Salah, Kenli Li, Luxiu Yin
Summary: This paper studies the container and VM placement problem in CaaS environments and proposes an algorithm based on the Whale Optimization Algorithm to optimize power consumption and resource utilization. The proposed method outperforms existing methods in experimental evaluations.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Mohsen Kiani, Mohammad Reza Khayyambashi
Summary: This article proposes a virtual machine placement (VMP) algorithm to reduce power consumption in heterogeneous cloud data centers, employing a novel network power estimation model and chemical reaction optimization (CRO) algorithm. The evaluation results indicate that the CRO algorithm with grouping-based encoding scheme outperforms other methods in terms of power consumption, highlighting the significance of network power consumption.
Article
Computer Science, Theory & Methods
Garima Singh, Anil Kumar Singh
Summary: Cloud computing attracts customers looking to reduce overall business costs, where trust and SLAs are crucial. Providers offer infrastructure-as-a-service through virtual machines. Migration of VMs requires minimizing total migration time and downtime to reduce penalties for SLA violations.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Iftikhar Ahmad, Ambreen Shahnaz, Muhammad Asfand-e-Yar, Wajeeha Khalil, Yasmin Bano
Summary: The demand for cloud computing, especially on-demand computing, has been increasing rapidly. However, cloud services consume a large amount of energy and produce greenhouse gases. This research focuses on energy efficient algorithms for virtual machine consolidation, aiming to minimize energy consumption and meet service level agreement requirements. An online algorithm is developed and analyzed using competitive analysis approach. Experimental analysis shows that the proposed algorithm performs significantly better than benchmark algorithms, reducing energy consumption by 25% and migrations by 43%.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Artificial Intelligence
Tahereh Abbasi-khazaei, Mohammad Hossein Rezvani
Summary: This study proposes a virtual machine placement method to jointly minimize energy costs and scheduling, aiming to address the critical concerns of cloud service providers. The performance of the algorithm is compared with baseline methods and the simulation results demonstrate its effectiveness.
Article
Computer Science, Information Systems
Jun Feng, Laurence T. Yang, Xin Nie, Nicholaus J. Gati
Summary: This article proposes a novel edge-cloud-aided differentially private tucker decomposition scheme to protect private data of data owners in CPSS. The scheme achieves efficient tensor factorization while preserving privacy through perturbation and local resolution.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Mustafa Gamsiz, Ali Haydar Ozer
Summary: The paper proposes an energy-aware combinatorial auction-based model for resource allocation in cloud environments, providing a solution that minimizes energy costs. Experimental results demonstrate the performance advantages of the model and proposed heuristic methods.
Article
Computer Science, Information Systems
Jinjiang Wang, Junyang Yu, Yixin Song, Xin He, Yalin Song
Summary: This paper introduces a strategy called LBVMP, which utilizes virtual machine consolidation technology to reduce energy consumption, decrease migration count, and optimize the quality of service in cloud data centers. By allocating and deploying resources efficiently, the strategy ensures proper utilization among virtual machines, thereby improving performance.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Keke Huang, Shijun Tao, Dehao Wu, Chunhua Yang, Weihua Gui, Shiyan Hu
Summary: Process monitoring is crucial for the reliable operation of industrial systems in the context of industrial Internet of Things (IIoT). However, due to the harsh environment and unreliable sensors and actuators, it is challenging to collect enough tagged and highly reliable data, leading to degraded performance and lack of trust in the monitoring results. To address this issue, a self-weighted dictionary learning process monitoring method is proposed, which incorporates label propagation classification, reweighting of classification loss and label-consistency constraints, and an iterative optimization algorithm for learning the classifier and dictionary.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Shuiguang Deng, Yishan Chen, Gong Chen, Shouling Ji, Jianwei Yin, Albert Y. Zomaya
Summary: This paper presents a proactive application deployment system consisting of three modules (incentive, profit, and latency) that optimizes application deployment based on a fully distributed edge network architecture. The SELL algorithm in the incentive module allows edge servers to compete for deployment rights in a two-stage Stackelberg game and receive payment for their efforts. The other two modules recursively adjust service prices and deployment intentions based on their own profits. Simulations demonstrate that the SELL algorithm can help application providers find suitable edge servers for deployment while maximizing profits for both parties with low latency.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Hailiang Zhao, Shuiguang Deng, Feiyi Chen, Jianwei Yin, Schahram Dustdar, Albert Y. Zomaya
Summary: This article discusses the scheduling of multi-server jobs online and proposes the Esdp algorithm to deal with the unknown actual processing speeds. By learning the distribution of processing speed fluctuations, the Esdp algorithm maximizes the cumulative overall utility and has polynomial complexity and logarithmic regret.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mohit Kumar, Avadh Kishor, Jitendra Kumar Samariya, Albert Y. Zomaya
Summary: The Internet of Things (IoT) has transformed the industry by providing various facilities and advancements. To meet the requirements of the industrial IoT system, an autonomic workload prediction and resource allocation framework is introduced. This framework efficiently allocates resources among fog nodes (FNs) based on workload prediction using a deep autoencoder (DAE) model and optimal FN selection using the crow search algorithm (CSA). The proposed scheme outperforms existing optimization models in terms of cost, delay, and workload execution.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Mingyue Zhang, Junlong Zhou, Peijin Cong, Gongxuan Zhang, Cheng Zhuo, Shiyan Hu
Summary: This paper presents a lightweight incentive authentication scheme (LIAS) for forensic services in Internet of Vehicles (IoV). LIAS is developed on a three-tier architecture and uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to achieve secure and efficient anonymous authentication.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Haixin Wang, Zihao Yang, Zhe Chen, Jun Liang, Gen Li, Junyou Yang, Shiyan Hu
Summary: In this paper, a multiple adaptive model predictive controller (MAMPC) is proposed to alleviate the mechanical fatigue of the main shaft in all speed sections. The effectiveness of the method is verified through extensive simulations, showing a reduction in minimum frequency deviation and an extension in the number of fatigue cycles of the main shaft.
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2023)
Article
Computer Science, Information Systems
Firas Al-Doghman, Nour Moustafa, Ibrahim Khalil, Nasrin Sohrabi, Zahir Tari, Albert Y. Zomaya
Summary: The paradigm of edge computing has created new possibilities for the Internet of Things (IoT), expanding cloud services to the network edge. This allows for the design of distributed architectures and the enhancement of decision-making applications. Edge computing faces challenges related to security and privacy, but advancements in artificial intelligence and machine learning provide opportunities for precise models and intelligent applications at the network edge. This study presents a comprehensive survey on securing edge computing-based AI microservices, highlighting key requirements and proposing a secure edge computing framework.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Automation & Control Systems
Keke Huang, Zui Tao, Yishun Liu, Bei Sun, Chunhua Yang, Weihua Gui, Shiyan Hu
Summary: This article proposes a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updates the model by learning the data of new modes in order to adapt to newly emerged modes. A similarity metric is also proposed to ensure the representation ability of the method for historical data. Numerical simulation experiments, CSTH process experiments, and industrial roasting process experiments demonstrate that the proposed JMSDL method accurately matches new modes while maintaining performance on historical modes. Furthermore, the proposed method outperforms state-of-the-art methods in terms of fault detection and false alarm rate.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Yishun Liu, Chunhua Yang, Keke Huang, Weihua Gui, Shiyan Hu
Summary: In this article, a systematic intelligent technique is proposed for optimizing the procurement supply chain (PSC) in the context of smart manufacturing. The technique involves predicting market prices using variational mode decomposition and long short-term memory network, building a multiperiod dynamic purchasing model considering production plans and market fluctuations, evaluating suppliers automatically using a stacked autoencoder under bootstrap aggregation, and establishing a multiobjective order allocation model considering procurement costs and supplier scores and solving it using particle swarm optimization. Extensive experiments in a zinc smelter company demonstrate the effectiveness of the proposed technique in reducing labor costs, improving PSC efficiency, and reducing procurement costs.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Energy & Fuels
Chunqiu Xia, Wei Li, Xiaomin Chang, Ting Yang, Albert Y. Zomaya
Summary: The increasing use of distributed energy resources has changed the management of the electricity system. Microgrids have been established by homes and businesses with local electricity generators, which have increased renewable energy use but also introduced challenges in managing the microgrid system due to the uncertainty of renewable energy generation, load demands, and dynamic electricity prices. To address this, a rank-based multiple-choice secretary algorithm (RMSA) was proposed for microgrid management to reduce operating costs by making real-time decisions under uncertainties. Extensive experiments were conducted to prove the efficacy of the solution in complex real-world scenarios.
FRONTIERS IN ENERGY
(2023)
Article
Computer Science, Hardware & Architecture
Yong Xie, Gang Zeng, Ryo Kurachi, Fu Xiao, Hiroaki Takada, Shiyan Hu
Summary: This article develops the first security-aware system model for the processing of CAN FD messages and presents a new WCRT analysis to estimate the interference induced by security-critical messages. We show that the number of impacted messages increases with the increasing number of security critical messages, and the percentage of WCRT increase varies from 12.43% to 14.57% and 7.0% to 10.89% for the two typical CAN FD systems, respectively; the percentage of WCRT decrease varies from 3.29% to 6.04% and 4.13% to 7.93%, respectively.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Automation & Control Systems
Jun Wang, Chang Tang, Xinwang Liu, Wei Zhang, Wanqing Li, Xinzhong Zhu, Lizhe Wang, Albert Y. Zomaya
Summary: This research proposes a region-aware hierarchical latent feature representation learning-guided clustering method for hyperspectral band selection. By utilizing superpixel segmentation algorithm and clustering technique, this method takes full consideration of spatial information and importance of different regions in HSIs, achieving superior performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Liying Li, Yinghui Wang, Haizhou Wang, Shiyan Hu, Tongquan Wei
Summary: This article presents an efficient architecture for imputing missing values in distributed IoT data. Experimental results demonstrate that the proposed method significantly reduces imputation errors compared to other methods, while maintaining low transmission costs.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Construction & Building Technology
P. Ruiz, J. M. Aragon-Jurado, M. Seredynski, J. F. Cabrera, D. Pena, J. C. de la Torre, A. Y. Zomaya, B. Dorronsoro
Summary: Public transport is vital for sustainable cities, and plug-in electric hybrid buses can further reduce greenhouse gas emissions. By optimizing the electric drive strategy, these buses can achieve high electric range and lower emissions. This study focuses not only on improving bus performance, but also on the environmental benefits and livability of cities.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Computer Science, Information Systems
Wei-Kang Chung, Yun Li, Chih-Heng Ke, Sun-Yuan Hsieh, Albert Y. Zomaya, Rajkumar Buyya
Summary: BCube, a well-known network structure for data center networks (DCNs), provides multiple low-diameter paths and good fault-tolerance. This paper proposes two centralized dynamic parallel flow scheduling algorithms, CDPFS and CDPFSMP, to decrease collisions and improve bandwidth utilization in BCube topology. The simulation results demonstrate that our algorithms leverage the advantages of BCube structure and achieve high-performance solutions for load balancing problems, improving throughput by 44.1% in random bijective traffic pattern and 36.2% in data shuffle compared with the BSR algorithm.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(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)