Article
Computer Science, Information Systems
Stavros Souravlas, Sofia D. Anastasiadou, Nicoleta Tantalaki, Stefanos Katsavounis
Summary: This paper discusses load balancing techniques in cloud computing, and proposes a fair task load balancing strategy to improve system performance. By formulating the problem as an irreducible finite state Markov process, the expected utilizations for virtual machines are derived and used in the task allocation approach. The experimental results show that the proposed scheme outperforms other algorithms in terms of makespan, average response time, and resource utilization, while providing lower degree of imbalance.
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, Artificial Intelligence
Yogesh Gupta
Summary: Cloud storage, a type of distributed storage based on cloud computing technology, emerged to efficiently manage the rapidly expanding data in cyberspace. It acts as a repository for data storage, management, and user accessibility, aiming to balance server load, reduce response time, and leverage overall system performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Petra Loncar, Paula Loncar
Summary: This research explores the strategy of optimizing task scheduling in cloud environments and achieves promising results using the metaheuristic Evolution Strategies algorithm.
Article
Computer Science, Information Systems
Andrea Detti, Ludovico Funari, Luca Petrucci, Michele Dorazio, Arianna Mencattini, Eugenio Martinelli
Summary: The Common Workflow Language (CWL) is a description language for data science workflows. In this paper, the authors propose CWL-PLAS, an extension that allows tasks to use a cloud platform for parallel computing, reducing workflow execution time. They implemented a workflow manager and evaluated its performance in machine learning workflows.
Article
Computer Science, Information Systems
Sangkwon Lee, Syed Asif Raza Shah, Woojin Seok, Jeonghoon Moon, Kihyeon Kim, Syed Hasnain Raza Shah
Summary: Deep learning is a growing technique used to solve complex AI problems. Large-scale deep learning faces challenges due to the expansion of datasets and complexity of models. The network is a major performance barrier in distributed deep learning in a distributed HPC environment.
Article
Computer Science, Theory & Methods
Rui Han, Shilin Li, Xiangwei Wang, Chi Harold Liu, Gaofeng Xin, Lydia Y. Chen
Summary: Research shows that with the exponential growth of data generated by edge computing, the decentralized and Gossip-based training of deep learning models is gaining momentum. The EdgeGossip framework is designed to reduce the performance variation among heterogeneous edge platforms during training and achieve best possible model accuracy quickly. Implementing EdgeGossip based on popular Gossip algorithms has demonstrated an average reduction of model training time by 2.70 times with only 0.78% accuracy loss.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Dapeng Lan, Amir Taherkordi, Frank Eliassen, Lei Liu, Stephane Delbruel, Schahram Dustdar, Yang Yang
Summary: This article introduces a system framework, EDGE VISION, for computer vision applications on heterogeneous edge computing platforms. It proposes two scheduling algorithms, minimum latency task scheduling and minimum cost task scheduling, to minimize processing latency and system cost.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yujin Cai, Wenwu Yu, Xiaokai Nie, Qiang Cheng, Tiejun Cui
Summary: This paper investigates the resource allocation problem in the RIS-aided heterogeneous network and proposes two algorithms to solve it. The numerical results demonstrate that the centralized algorithm achieves higher total throughput, while the distributed algorithm significantly reduces the resource allocation time.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Computer Science, Information Systems
Chinmaya Kumar Dehury, Prasan Kumar Sahoo
Summary: This article introduces a failure-aware semi-centralized VNE algorithm to reduce the impact of resource failures on cloud computing users, and demonstrates the superiority of this algorithm through simulation experiments.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Telecommunications
Pengfei Wang, Boya Di, Lingyang Song, Nicholas R. Jennings
Summary: This paper focuses on distributed heterogeneous multi-layer mobile edge computing networks where resource-poor edge devices upload computing tasks for processing to MEC servers and a cloud center. To reduce energy consumption, each device and server independently perform computation offloading and resource allocation. However, due to limited information available, offloading strategies may lead to network congestion. To address this, a smart pricing mechanism is developed to coordinate computation offloading, resulting in reduced energy consumption. Simulation results show significant energy consumption reduction and decreased congestion probability compared to existing techniques.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Economics
Felipe Arteaga, Adam J. Kapor, Ciiristopiier A. Neilson, Seth D. Zimmerman
Summary: This article reveals that beliefs about admissions chances have an impact on choice outcomes, even when a strategy-proof assignment mechanism is in place. Smart matching platforms that offer live feedback can enhance the effectiveness of applicants' school searches. To alleviate the burden of school choice, it is crucial to not only ensure strategy-proofness within the centralized system but also provide support for the strategic decisions that fall outside of it.
QUARTERLY JOURNAL OF ECONOMICS
(2022)
Article
Computer Science, Information Systems
Sunanda Bose, Nandini Mukherjee
Summary: This article proposes novel scheduling algorithms for heterogeneous resources with varying capabilities, contexts of usage, and non-uniform performance over time in a sensor-cloud infrastructure. Simulation results demonstrate that the algorithms perform efficiently.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Software Engineering
Anshul Jindal, Michael Gerndt, Mohak Chadha, Vladimir Podolskiy, Pengfei Chen
Summary: Serverless computing has grown rapidly since the launch of Amazon's Lambda platform, with Function-as-a-Service (FaaS) enabling applications to be decomposed into standalone functions for execution on a FaaS platform. This study introduces the concept of a Function Delivery Network (FDN) to support heterogeneous clusters and functions, showcasing opportunities for increased service level objective (SLO) fulfillment and energy efficiency. Evaluating over five distributed target platforms, scheduling functions on an edge target platform reduced overall energy consumption by 17x without violating SLO requirements.
SOFTWARE-PRACTICE & EXPERIENCE
(2021)
Review
Computer Science, Software Engineering
Mohammadreza Razian, Mohammad Fathian, Rami Bahsoon, Adel N. Toosi, Rajkumar Buyya
Summary: This paper presents a systematic literature review on service composition under uncertainty. It identifies and classifies existing studies, discusses trends and future research directions in this field.
JOURNAL OF SYSTEMS AND SOFTWARE
(2022)
Article
Computer Science, Theory & Methods
Weiguang Liu, Jinhua Cui, Tiantian Li, Junwei Liu, Laurence T. Yang
Summary: This paper presents MLCache, a space-efficient shared cache management scheme for NVMe SSDs. By learning the impact of reuse distance on cache allocation and building a workload-generic neural network model, MLCache achieves efficient space allocation decisions. Additionally, MLCache proposes an efficient parallel writing back strategy to improve fairness.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Yang, Laurence T. Yang, Hao Wang, Yuan Gao, Yaliang Zhao, Xia Xie, Yan Lu
Summary: This paper investigates the progress and function of representation learning models adopted in knowledge fusion and reasoning, providing new perspectives and ideas for scholars. The paper comprehensively reviews classic methods and investigates advanced and emerging works. Additionally, an integrated knowledge representation learning framework and tensor-based knowledge fusion and reasoning models are proposed.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Sushil Kumar Singh, Laurence T. Yang, Jong Hyuk Park
Summary: This article proposes a scheme called FusionFedBlock to address privacy issues in Industry 5.0 by combining blockchain and federated learning. In the scheme, industry departments can perform local learning updates and communicate with a global model, with validation conducted through a blockchain network. The scheme demonstrates excellent performance in privacy preservation and accuracy improvement.
INFORMATION FUSION
(2023)
Article
Computer Science, Hardware & Architecture
Shunli Zhang, Laurence T. Yang, Yue Zhang, Xiaokang Zhou, Zongmin Cui
Summary: This article presents a data-driven system-level design framework for responsible cyber-physical-social systems (CPSS), addressing ethical and legal concerns regarding decision-making in artificial intelligence systems.
Editorial Material
Computer Science, Hardware & Architecture
Sahil Garg, Jia Hu, Giancarlo Fortino, Laurence T. Yang, Mohsen Guizani, Xianjun Deng, Danda B. Rawat
Article
Engineering, Multidisciplinary
Yunzhi Xia, Xianjun Deng, Lingzhi Yi, Laurence T. Yang, Xiao Tang, Chenlu Zhu, Zhongping Tian
Summary: This paper proposes a 6G IoT coverage hole recovery algorithm based on Mobile Edge Computing (MEC) and Artificial Intelligence (AI). The algorithm utilizes the fusion model of the disc model and the confident information model to guide the movement of mobile edge nodes and repair the coverage holes through repeated games based on Q-learning.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Xiaokang Zhou, Xuzhe Zheng, Xuesong Cui, Jiashuai Shi, Wei Liang, Zheng Yan, Laurence T. Yang, Shohei Shimizu, Kevin I-Kai Wang
Summary: This paper proposes a three-layer Federated Reinforcement Learning (FRL) framework with an end-edge-cloud structure, incorporating a digital twin system. It aims to enable lightweight model training and real-time processing in high-speed mobile networks. The proposed dual-reinforcement learning scheme and model splitting scheme effectively reduce communication costs and improve the non-IID problem.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Huazhong Liu, Jiawei Wang, Xiaoxue Yin, Jihong Ding, Laurence T. Yang, Tong Yao, Jing Yang, Yuan Gao
Summary: This article proposes a tensor-train (TT)-based multiuser multivariate multiorder (3M) physical Markov prediction approach for multimodal industrial trajectory pattern mining. The proposed approach improves the computational efficiency up to three times compared with the original tensor-based 3M approach, while ensuring basically consistent prediction accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Zongmin Cui, Zhixing Lu, Laurence T. Yang, Jing Yu, Lianhua Chi, Yan Xiao, Shunli Zhang
Summary: There are three key roles in Intelligent Transportation Systems: driver, vehicle, and road. Existing static interactions among them are not dynamic enough, and unable to reflect changes in driver preferences, vehicle conditions, and road conditions. To address this issue, a data-driven Cloud-Fog-Edge Collaborative Driver-Vehicle-Road (CFEC-DVR) framework is proposed, which continuously adapts and evolves to provide better ITS services for humans.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Jia Zhou, Guoqi Xie, Haibo Zeng, Weizhe Zhang, Laurence T. Yang, Mamoun Alazab, Renfa Li
Summary: In this paper, a clock-skew-based approach is proposed to pinpoint the sender and detect intrusion on proprietary CAN bus. By analyzing data from real vehicles, a box-plot algorithm based on score mechanism is presented to filter and describe the hardware characteristics of ECUs accurately.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xu Li, Feilong Tang, Luoyi Fu, Jiadi Yu, Long Chen, Jiacheng Liu, Yanmin Zhu, Laurence T. Yang
Summary: The provisioning of satellite controllers has a significant impact on the performance of software-defined satellite networks. The challenge lies in achieving low control overhead throughout the operation period, despite the difficulty in predicting network load accurately. Existing methods struggle to address this issue, leading to frequent controller migrations. In this paper, we propose globally optimized strategies utilizing current network load information and introduce approximate and heuristic algorithms to solve the Controller Provisioning Problem in SDSNs.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Hui Zhu, Xiaohu Tang, Laurence Tianruo Yang, Chao Fu, Shuangrong Peng
Summary: This paper proposes an efficient sampling-based scheme for collecting and analyzing key-value data. By utilizing probability sampling and optimizing budget allocation, the proposed scheme improves the probability of users submitting valid key-value data and achieves accurate frequency and mean estimation.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Bocheng Ren, Laurence T. Yang, Qingchen Zhang, Jun Feng, Xin Nie
Summary: Various stream learning methods are emerging to provide solutions for artificial intelligence in streaming data scenarios. However, when each data stream is oriented to a different target space, it becomes impracticable to use the previous approaches. Therefore, we propose an adaptive learning scheme using tensor and meta-learning to mitigate domain shift and improve performance for few-shot streaming tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaokang Wang, Lei Ren, Ruixue Yuan, Laurence T. Yang, M. Jamal Deen
Summary: In this article, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented as an approach for efficiently processing CPSS big data. By decomposing the multi-attributes CPSS big data into the QTT form, and utilizing a distributed cloud-edge computing model, the proposed method effectively improves training efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Bocheng Ren, Laurence T. Yang, Qingchen Zhang, Jun Feng, Xin Nie
Summary: The development of artificial intelligence and the Internet of Things has provided opportunities for healthcare transformation, but data privacy and security remain concerns. We propose a blockchain-powered intelligent healthcare system that uses tensor meta-learning models to efficiently model heterogeneous healthcare data and protect private data.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
