Article
Computer Science, Hardware & Architecture
Nisha Chaurasia, Mohit Kumar, Rashmi Chaudhry, Om Prakash Verma
Summary: The objective of cloud computing is to provide seamless services using virtualization technology over the Internet to serve the Quality of Service (QoS)-driven end users requirements. However, due to improper resource utilization, optimizing server energy consumption becomes a significant challenge. By optimizing server usage, reducing power consumption, and having fewer active physical servers to provide the required services, the aim of low energy consumption profile without compromising QoS can be achieved.
JOURNAL OF SUPERCOMPUTING
(2021)
Review
Computer Science, Hardware & Architecture
Raseena M. Haris, Khaled M. Khan, Armstrong Nhlabatsi
Summary: As enterprises outsource their computing needs to cloud computing, the availability of cloud services becomes increasingly important. One solution to service interruption is real-time Live Machine Migration (LVM), which aims to minimize downtime during the migration of virtual machines. This paper reviews the challenges of LVM, particularly focusing on memory content migration, and discusses proposed solutions to overcome these challenges and optimize LVM.
Article
Physics, Multidisciplinary
Ling Yuan, Zhenjiang Wang, Ping Sun, Yinzhen Wei
Summary: With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. We proposed a VMC algorithm based on load forecast to improve efficiency. The algorithm includes migration VM selection strategy called LIP and VM migration point selection strategy called SIR, which effectively improve the accuracy of VM selection and stability of physical machine load.
Article
Engineering, Industrial
Nisha Chaurasia, Shashikala Tapaswi, Joydip Dhar
Summary: With the rise of cloud computing, there is a need to deploy multiple virtual machines (VMs) on multiple hosts. However, the energy consumption of data centers with a large number of servers remains a concern. Server consolidation aims to reduce the number of active servers in a data center by transferring the workload of VMs from one server to another. This paper proposes a Time Sensitive Virtual Machine Migration (TS-VMM) strategy to minimize migrations while optimizing cost and maximizing server utilization.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE
(2023)
Article
Computer Science, Hardware & Architecture
Seyed Milad Farzaneh, Omid Fatemi
Summary: Researching the best approach for virtual machine placement in cloud infrastructure is crucial for optimization, and utilizing arbitrary processing elements in algorithm design can improve overall performance.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Ghetas
Summary: The growing demand for cloud computing adoption poses challenges for researchers to make cloud computing more efficient and affordable. Server consolidation is a strategy to improve data center energy efficiency and resource utilization, and saving energy consumption can significantly reduce overall cloud management costs.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Satyajit Padhy, Jerry Chou
Summary: This study aims to minimize the number of migrations in batch processing systems through a novel consolidation aware scheduling algorithm. The experimental results demonstrate that the approach significantly reduces the number of migrations, improving energy efficiency.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2021)
Article
Computer Science, Information Systems
Eduard Zharikov, Sergii Telenyk
Summary: This research investigates the problem of power-aware VM consolidation under dynamic workloads, uncertainty, and a changing number of VMs, proposing a dynamic VM management method based on beam search and determining optimal algorithm parameters using a new power-aware integral estimation method. The proposed method minimizes SLA violations to improve SLA quality metrics and reduce VM migrations, while slightly increasing power consumption. Experiment results show efficient use of cloud resources in terms of SLA violation and VM migrations.
Article
Computer Science, Artificial Intelligence
Binbin Zhang, Xiao Wang, Hao Wang
Summary: The study focuses on the issue of virtual machine live migration in self-driving systems, presenting a cluster-based genetic algorithm to address the problem. By clustering the population and reducing crossover operations, the algorithm efficiently outputs an approximation result for the bin packing problem. Experimental results demonstrate that the proposed approach outperforms traditional genetic algorithms in terms of both accuracy and efficiency.
Review
Computer Science, Interdisciplinary Applications
Bhagyalakshmi Magotra, Deepti Malhotra, Amit Kr Dogra
Summary: Cloud Computing, as a computing paradigm where services are provided through the internet, has greatly impacted the IT industry. However, the over-provisioning of resources in cloud data centers and the resulting environmental issues caused by high energy consumption highlight the importance of research on resource utilization and energy control.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Satyajit Padhy, Ming-Han Tsai, Shalini Sharma, Jerry Chou
Summary: This article discusses the widely adapted server virtualization and consolidation techniques in modern large-scale computing systems and the negative impacts they can have. The authors propose a solution for parallel computing jobs in batch processing systems, which proactively avoids VM migrations and minimizes communication overhead through the co-design of job scheduling and VM consolidation strategies.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Na Li, XiaoLing Liu, Yu Wang, Musa Mojarad
Summary: Cloud computing provides global IT services, but hosting cloud applications consume a significant amount of energy. The increasing number of providers has led to higher greenhouse gas emissions and operational costs. Therefore, optimizing the cloud infrastructure configuration is crucial to manage energy consumption and costs in data centers. Virtual Machine Placement (VMP) is a key issue in this field, aiming to minimize energy consumption and costs while adhering to the Service Level Agreement (SLA). This study proposes a meta-heuristic algorithm called VMP-ODMA, which dynamically consolidates VMs into a minimum number of active hosts through migration in cloud data centers. Extensive simulations demonstrate that VMP-ODMA efficiently improves system performance, surpassing existing methods by 11% to 27%.
JOURNAL OF GRID COMPUTING
(2023)
Article
Computer Science, Information Systems
Badieh Nikzad, Behnam Barzegar, Homayun Motameni
Summary: This paper proposes a novel approach to improve physical machine efficiency in data centers. By employing heuristics and meta-heuristic algorithms along with eight performance criteria, the approach optimizes power usage in small to medium scale data centers. The results demonstrate significant improvement in energy consumption, number of SLA violations, and number of VMs migrations compared to previous algorithms.
Article
Computer Science, Information Systems
Zhoujun Ma, Di Ma, Mengjie Lv, Yutong Liu
Summary: The energy used by cloud data centers (CDCs) is increasing as cloud services expand, posing environmental burdens and higher expenses for cloud providers. To reduce energy consumption, virtualization migration and consolidation techniques are widely used in CDCs. This study proposes algorithms for determining migration timing, selecting VMs to migrate, and choosing migration destination hosts. Experimental results show that the proposed algorithms outperform state-of-the-art methods, reducing energy consumption, service level agreement violation, and the number of VM migrations.
Article
Computer Science, Information Systems
Shirzad Shahryari, Farzad Tashtarian, Seyed-Amin Hosseini-Seno
Summary: Edge Cloud Computing (ECC) is a new approach to provide cloud computing services to mobile users, aiming to reduce latency and increase bandwidth. This study proposes a Cost-aware Virtual Machine (VM) placement and migration (CoPaM) framework that selects optimal Cloudlets using path prediction methods to optimize service efficiency and costs.
COMPUTER COMMUNICATIONS
(2022)
Article
Biochemical Research Methods
Chengqian Lu, Lishen Zhang, Min Zeng, Wei Lan, Guihua Duan, Jianxin Wang
Summary: Emerging evidence suggests that circRNAs, with their covalently closed loop structures, can serve as promising biomarkers for diagnosis in pathogenic processes. Computational approaches provide a cost-effective way to identify circRNA-disease associations by aggregating multi-source pathogenesis data and inferring potential associations at the system level. The proposed CDHGNN model, based on edge-weighted graph attention and heterogeneous graph neural networks, outperforms state-of-the-art algorithms in predicting circRNA-disease associations and can identify specific molecular associations and investigate biomolecular regulatory relationships in pathogenesis.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Zhuofan Liao, Yinbao Ma, Jiawei Huang, Jianxin Wang
Summary: This paper proposes an energy-aware 3D-deployment method for Unmanned Aerial Vehicles (UAVs) called 3D-UAV, aiming to ensure a high uplink rate and minimize the number of UAVs in the Internet of Vehicles (IoV) with Highway Interchange. By dividing vehicles into clusters and optimizing the flight altitude of UAVs, this method can cover all vehicles in the bridge scenario and outperform other methods in terms of uplink rate and energy.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Biochemical Research Methods
Qichang Zhao, Guihua Duan, Mengyun Yang, Zhongjian Cheng, Yaohang Li, Jianxin Wang
Summary: The identification of drug-target relations (DTRs) is crucial in drug development. Traditional methods treating DTRs as drug-target interactions (DTIs) suffer from the lack of reliable negative samples and fail to consider many important aspects of DTRs. With the availability of drug-protein binding affinity data, predicting DTRs as a regression problem of drug-target affinities (DTAs) using deep learning architectures has become feasible. In this study, a deep learning-based model named AttentionDTA is proposed, which utilizes attention mechanism to predict DTAs. The model demonstrates superior performance compared to state-of-the-art methods and successfully extracts protein and drug features to better predict drug-target affinities.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Qichang Zhao, Guihua Duan, Haochen Zhao, Kai Zheng, Yaohang Li, Jianxin Wang
Summary: Drug discovery and drug repurposing benefit from the application of deep learning in predicting drug-target interactions (DTIs). A novel model called GIFDTI is proposed to address the challenges of representing local chemical environments, encoding long-distance relationships, and modeling intermolecular interactions. Evaluation results demonstrate that GIFDTI outperforms state-of-the-art methods in DTI prediction. Case studies also validate the accuracy and cost-effectiveness of the model. The code for GIFDTI is available at https://github.com/zhaoqichang/GIFDTI.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xiaoqing Peng, Wenjin Zhang, Wanxin Cui, Binrong Ding, Qingtong Lyu, Jianxin Wang
Summary: Alzheimer's disease (AD) is a common neurodegenerative disease, and DNA methylation is closely related to its pathological mechanism. A database named ADmeth has been designed to collect AD-related differential methylation data, containing 16,709 items identified from various brain regions and cell types in the blood, including 209 genes, 2,229 regions, and 14,271 CpG sites. The ADmeth database provides user-friendly functions for searching, submitting, and downloading data, aiming to facilitate research on the pathological mechanism of AD and non-invasive diagnosis using cell-free DNA.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Jiawei Huang, Wenlu Zhang, Yijun Li, Lin Li, Zhaoyi Li, Jin Ye, Jianxin Wang
Summary: Identifying heavy flows is crucial for network management, but it is challenging to detect heavy flow quickly and accurately in highly dynamic traffic and rapidly growing network capacity. Existing schemes trade-off efficiency, accuracy, and speed, requiring large memory for acceptable performance. To address this, ChainSketch is proposed, with advantages in memory efficiency, accuracy, and fast detection. ChainSketch utilizes selective replacement, hash chain, and compact structure, significantly improving F1-score compared to existing solutions, especially in small memory conditions.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Hardware & Architecture
Jingling Liu, Jiawei Huang, Weihe Li, Jianxin Wang, Tian He
Summary: Datacenter networks often face path asymmetry, leading to problems like packet reordering and under-utilization of multiple paths. In this paper, we propose a load balancing mechanism called AG that adaptively adjusts switching granularity based on the degree of topology asymmetry. We also design a switch-based scheme to measure the difference of one-way delay, allowing accurate detection of topology asymmetry. Experimental results show that AG outperforms existing load balancing schemes in terms of flow completion time.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Theory & Methods
Jingui Huang, Jie Chen, Yunlong Liu, Guang Xiao, Jianxin Wang
Summary: In this paper, we study the fixed-order book drawing problem and develop algorithms for it from the perspective of parameterized complexity. By limiting the number of crossings per edge and other parameters of the input graph, we obtain specific results.
INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
(2023)
Article
Biology
Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jianxin Wang
Summary: Adverse drug reactions have a direct impact on human health. Computational methods, such as the deep learning framework GCAP, offer promising alternatives for predicting the seriousness of clinical outcomes resulting from adverse reactions to drugs. GCAP can effectively predict whether adverse reactions cause serious clinical outcomes and infer the corresponding classes of seriousness.
COMMUNICATIONS BIOLOGY
(2023)
Article
Biochemical Research Methods
Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang
Summary: In this paper, a deep learning framework called MSDRP is proposed for drug response prediction. MSDRP captures interactions between drugs and cell lines using an interaction module, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms. Experimental results demonstrate the excellent performance of our model in all performance measures for all experiments.
Article
Biochemical Research Methods
Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang
Summary: In this research, a novel method called DFHiC is proposed to generate high-resolution Hi-C matrix from low-resolution Hi-C matrix using the dilated convolutional neural network framework. DFHiC can reliably and accurately improve the resolution of Hi-C matrix, and the super-resolution Hi-C data enhanced by DFHiC is more similar to real high-resolution Hi-C data in terms of both chromatin significant interactions and identifying topologically associating domains.
Article
Biochemical Research Methods
Neng Huang, Minghua Xu, Fan Nie, Peng Ni, Chuan-Le Xiao, Feng Luo, Jianxin Wang
Summary: We developed a deep learning-based method called NanoSNP for identifying SNP sites in low-coverage Nanopore sequencing data. NanoSNP uses a multi-step, multi-scale, and haplotype-aware pipeline to detect SNP sites and predict genotypes. Comparison with other methods showed that NanoSNP outperformed Clair, Pepper-DeepVariant, and NanoCaller in identifying SNPs, especially in difficult-to-map regions and the major histocompatibility complex regions of the human genome. NanoSNP performed comparably to Clair3 when coverage exceeded 16x.
Article
Computer Science, Information Systems
Weihe Li, Jiawei Huang, Wenjun Lyu, Baoshen Guo, Wanchun Jiang, Jianxin Wang
Summary: Current ABR algorithms do not pay enough attention to audio bitrate selection, assuming it has minimal impact on video selection. However, with the advancement of audio technologies, audio bitrate can significantly affect video selection and viewing experience. To address this issue, we propose a deep reinforcement learning-based ABR algorithm that considers both audio and video quality, achieving significant improvements in overall viewing quality.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Shigeng Zhang, Zijing Ma, Kaixuan Lu, Xuan Liu, Jia Liu, Song Guo, Albert Y. Zomaya, Jian Zhang, Jianxin Wang
Summary: This paper presents HearMe, an accurate and real-time lip-reading system built on commercial RFID devices. HearMe can help people with speech disorders communicate and interact with the world effectively. By utilizing effective data collection, signal pattern extraction, and feature extraction techniques, HearMe achieves high accuracy in mouth motion recognition and word-level recognition. The use of transfer learning enhances the model's robustness in different environments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Shigeng Zhang, Zijing Ma, Chengwei Yang, Xiaoyan Kui, Xuan Liu, Weiping Wang, Jianxin Wang, Song Guo
Summary: This article introduces a real-time and accurate gesture recognition system called ReActor based on RFID. ReActor combines time-domain statistical features and frequency-domain features to represent the signal profile corresponding to different gestures accurately, and uses signal preprocessing and classifier training to maintain high accuracy in different environments.
IEEE TRANSACTIONS ON MOBILE 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)
