Article
Computer Science, Artificial Intelligence
Honglin Zhang, Yaohua Wu, Zaixing Sun
Summary: In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. Through ordering rules based on priority constraints and utilizing the HEFT algorithm, the algorithm proves to be effective and superior in simulation experiments.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Minhaj Ahmad Khan
Summary: The proposed normalization-based VM consolidation strategy aims to minimize energy consumption, SLA violations, and the number of VM migrations in cloud environments. The strategy utilizes resource parameters to identify overloaded hosts and compares the capacity of virtual machines and hosts. Experimental results demonstrate that this approach outperforms other well-known methods in reducing energy consumption, SLA violations, and the number of VM migrations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shaimaa Badr, Ahmed El Mahalawy, Gamal Attiya, Aida A. Nasr
Summary: Cloud computing plays a significant role in future technology, and with the growth of the Internet and cloud computing, several service providers have expanded their data centers worldwide. However, challenges such as power consumption arise with the widespread use of the cloud environment. This paper addresses the power consumption issue and presents an efficient algorithm to minimize power consumption.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Information Systems
R. Ghafari, F. Hassani Kabutarkhani, N. Mansouri
Summary: This paper focuses on the task scheduling methods in cloud systems, with a particular emphasis on energy efficiency. It conducts a comparative analysis of 67 scheduling methods, describing the advantages and disadvantages of the proposed algorithms, and presents future research areas and further developments in this field.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Huixi Li, Yongluo Shen, Huidan Xi, Yinhao Xiao
Summary: The recent COVID-19 pandemic has accelerated the use of cloud computing, leading to challenges in managing computing resources for cloud service providers. This paper proposes a cost model-based solution to reduce operating costs and ensure quality of service by predicting and migrating virtual machines to alleviate overload situations and penalty costs.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Jinjiang Wang, Hangyu Gu, Junyang Yu, Yixin Song, Xin He, Yalin Song
Summary: This paper proposes an energy efficient and QoS-aware VM consolidation method that utilizes a combined prediction model and provides new VM placement and selection policies. The experimental results demonstrate significant reductions in energy consumption and other metrics compared to benchmark methods.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xiaojian He, Junmin Shen, Fagui Liu, Bin Wang, Guoxiang Zhong, Jun Jiang
Summary: This paper proposes a method for task scheduling in a cloud environment, aiming to reduce energy consumption while ensuring quality of service. The method achieves efficient handling of deadline-constrained tasks through a two-stage scheduling approach, reducing completion time and energy consumption while improving task completion rate. Experimental results show significant improvement in task scheduling performance compared to other methods.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Karima Saidi, Dalal Bardou
Summary: There is increasing interest in distributed models for addressing resource allocation issues in Cloud computing environments. Two main approaches include task scheduling and VM-to-Physical Machine mapping. These aspects are closely related to the crucial issue of energy consumption in Cloud computing. A systematic review of recent literature was conducted to analyze the challenges and current state of research, as well as highlight new opportunities and provide guidance for future research in this field. This work aims to advance resource allocation in Cloud computing environments.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Bartosz Kopras, Bartosz Bossy, Filip Idzikowski, Pawel Kryszkiewicz, Hanna Bogucka
Summary: Fog networks provide varying computing resources at different distances from end users. This study focuses on the task distribution between fog and cloud nodes and proposes algorithms to minimize task transmission and processing energy while satisfying delay constraints. The results show a significant decrease in the number of computational requests with unmet delay requirements and reduced energy consumption using the proposed algorithms.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Poria Pirozmand, Ali Asghar Rahmani Hosseinabadi, Maedeh Farrokhzad, Mehdi Sadeghilalimi, Seyedsaeid Mirkamali, Adam Slowik
Summary: Cloud computing systems are a shared asset structure where users can access services based on their needs. Challenges include scheduling and energy consumption, and this study proposes a Genetic Algorithm-based energy-conscious scheduling heuristic that outperforms other methods.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Nimra Malik, Muhammad Sardaraz, Muhammad Tahir, Babar Shah, Gohar Ali, Fernando Moreira
Summary: This article addresses the issue of energy consumption and efficient resource utilization in virtualized cloud data centers, proposing an algorithm based on task classification and thresholds for efficient scheduling. Experiments validate the effectiveness of the proposed technique over other algorithms in terms of energy consumption, makespan, and load balancing.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Theory & Methods
Seyedhamid Mashhadi Moghaddam, Michael O'Sullivan, Charles Peter Unsworth, Sareh Fotuhi Piraghaj, Cameron Walker
Summary: Cloud service providers use load balancing algorithms to avoid SLAVs and wasted energy consumption. A key consideration is the balance between reducing migrations and decreasing host over-utilization. The paper proposes an alternative metric that considers QoS for customer VMs and compares load balancing methods with both existing and new metrics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Sampa Sahoo, Bibhudatta Sahoo, Ashok Kumar Turuk
Summary: Scheduling deadline sensitive tasks in cloud computing systems is challenging, requiring a balance between minimizing energy consumption and ensuring satisfactory service delivery.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Information Systems
Junwen Lu, Hao Yongsheng, Kesou Wua, Yuming Chen, Qin Wang
Summary: Mobile cloud computing provides rich computational resources for mobile users, network operators, and cloud computing providers. Offloading applications to remote cloud resources can save energy in a dynamic mobile cloud computing environment. Our proposed algorithm outperforms other methods in energy consumption reduction and number of finished jobs.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Huixi Li, Langyi Wen, Yinghui Liu, Yongluo Shen
Summary: This paper addresses the challenges brought by a massive number of users in managing multi-core cloud data centers (CDCs) that host cloud service providers. It solves the problems of ensuring the quality of service (QoS) for multiple users and reducing operating costs of CDCs. By establishing a cost model based on multi-core hosts and designing a solution that considers various costs, it effectively reduces the total operating cost.
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)