Article
Computer Science, Hardware & Architecture
Yashwant Singh Patel, Rishabh Jaiswal, Rajiv Misra
Summary: Dynamic virtual machine consolidation techniques, which focus on reducing actively used physical servers based on their current resource utilization, may lead to inaccurate predictions and high migration costs. To address this issue, a new prediction method is proposed that considers both current and future resource usage, resulting in significant improvements in accuracy and computational complexity.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Multidisciplinary Sciences
Muhammad Ibrahim, Muhammad Imran, Faisal Jamil, Yun-Jung Lee, Do-Hyeun Kim
Summary: The proposed Efficient Adaptive Migration Algorithm (EAMA) effectively reduces the number of migrations and SLA violation, increases resource utilization, and decreases energy consumption compared to the PACPA and RUAEE algorithms.
Article
Computer Science, Information Systems
Pengcheng Wei, Yushan Zeng, Bei Yan, Jiahui Zhou, Elaheh Nikougoftar
Summary: Virtualization technology represented by Virtual Machines (VMs) plays a crucial role in cloud computing infrastructure, and Virtual Machine Placement (VMP) poses challenges for energy efficiency in cloud data centers. This paper proposes VMP-A3C, a Deep Reinforcement Learning (DRL) based strategy, to optimize the allocation of VMs to Host Machines (HMs) in order to achieve load balancing and reduce energy consumption, while avoiding Service Level Agreement Violations (SLAV). The effectiveness of VMP-A3C is evaluated in terms of deployment rate, energy consumption, SLAV, and the numbers of shutdown HMs and migrated VMs. Notably, VMP-A3C outperforms the best existing method by reducing energy consumption by 2.54% and requiring 7.14% fewer HMs.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Priyanka Nehra, A. Nagaraju
Summary: This paper proposes a Support Vector Regression-based methodology to predict a host's future utilization using multiple resource's utilization history. Compared to existing approaches, the proposed method performs better in terms of root mean square error, mean absolute percentage error, mean square error, mean absolute error, and R2.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Wenbin Yao, Zhuqing Wang, Yingying Hou, Xikang Zhu, Xiaoyong Li, Yamei Xia
Summary: In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers leads to inefficient resource utilization and energy wastage in data centers. We propose a load balancing strategy based on virtual machine consolidation (LBVMC) to reduce energy consumption and service level agreement (SLA) violations. We present algorithms for load state classification, migratable VM selection based on multi-dimensional resource utilization, and VM placement based on resource fitness and load correlation. Experimental results demonstrate the superior performance of LBVMC in reducing energy consumption and SLA violations.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Seyyed Meysam Rozehkhani, Farnaz Mahan
Summary: With the continuous development of cloud computing as a successful business and the increasing interest of organizations in utilizing its environment, there is a high demand for provided services. However, the high maintenance costs and limited resources in the cloud computing environment make it essential to find an appropriate approach for controlling and managing resources. Existing exploratory methods have their own distinct features and may not evaluate all quality of service (QoS) criteria. Therefore, this study proposes a granular model that incorporates various computation criteria and utilizes past data to derive membership functions and inference rules. Experimental analysis demonstrates that this method outperforms other related methods and consistently delivers satisfactory performance across all QoS criteria.
INFORMATION SCIENCES
(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
Pingping Li, Jiuxin Cao
Summary: Virtual machine (VM) consolidation is an effective method to improve resource utilization and reduce energy consumption in cloud data centers. However, most existing studies ignore the long-term relationship between VMs and hosts, resulting in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique is proposed.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Hardware & Architecture
Jean Pepe Buanga Mapetu, Lingfu Kong, Zhen Chen
Summary: This paper investigates the trade-off between energy consumption, SLA violations, and VM migration quantity in cloud data centers, proposing a dynamic VM consolidation approach-based load balancing. Through four different methods, the approach efficiently controls relevant metrics to tackle the NP-hard optimization problem in data centers.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Information Systems
Sahul Goyal, Lalit Kumar Awasthi
Summary: The demand for cloud-based computation is increasing exponentially, but it also brings issues such as energy consumption and SLA violations. Therefore, it is crucial to improve resource utilization in cloud data centers. This research proposes an adaptive VM placement approach based on a meta-heuristic algorithm, which combines energy conservation, resource utilization, and required quality of services. Experimental results show that this approach significantly reduces energy consumption and decreases SLA violations and VM migrations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Pingping Li, Jiuxin Cao
Summary: This study proposes a virtual machine consolidation approach based on dynamic load mean and multi-objective optimization to address the issues of energy consumption and resource utilization in cloud data centers. By considering multiple factors and utilizing optimization algorithms, it achieves reduced energy consumption, optimized resource utilization, and ensured quality of service.
Article
Computer Science, Software Engineering
Shveta Verma, Anju Bala
Summary: This paper proposes an efficient auto-scaling approach for predicting host load through VM migration. The approach uses an ensemble method with different time-series forecasting models to predict the workload on the host. Based on the predicted load, algorithms have been designed to detect and migrate over-utilized and under-utilized hosts, effectively improving resource utilization.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
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, 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, Hardware & Architecture
Wan-Chi Chang, Ying-Li Chen, Pi-Chung Wang
Summary: Mobile edge computing (MEC) provides cloud computing capabilities in close proximity to mobile users, leading to better performance and reduced power consumption. However, the varying workloads of MEC servers result in load imbalance and hotspot issues. To address this, we propose a scheme called COHM, which considers the computation tasks for MEC servers with different states to mitigate hotspots.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Jia Ke, Ying Wang, Mingyue Fan, Xiaojun Chen, Wenlong Zhang, Jianping Gou
Summary: This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Shi Qiu, Huping Ye, Xiaohan Liao, Benyue Zhang, Miao Zhang, Zimu Zeng
Summary: This paper proposes a multilevel-based algorithm for hyperspectral image interpretation, which achieves semantic segmentation through multidimensional information fusion, and introduces a context interpretation module to improve detection performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jianteng Xu, Qingguo Bai, Zhiwen Li, Lili Zhao
Summary: This study constructs two optimization models for the omnichannel closed-loop supply chain by leveraging the combined power of leader-follower game and mean-variance theories. The focus is on analyzing the performance of manufacturers who distribute products through physical stores. The results show that the risk-averse attitude of the physical store has a positive impact on the overall system profitability, but if the introduced physical store belongs to another firm, total profit experiences a decline.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jiahao Xiong, Weihua Ou, Zhonghua Liu, Jianping Gou, Wenjun Xiao, Haitao Liu
Summary: This paper proposes a novel remote photoplethysmography framework, named GraphPhys, which utilizes graph neural network to extract physiological signals and introduces Average Relative GraphConv for the task of remote physiological signal measurement. Experimental results show that the methods based on GraphPhys significantly outperform the original methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Zhiyao Tong, Yiyi Hu, Chi Jiang, Yin Zhang
Summary: The rise of illicit activities involving blockchain digital currencies has become a growing concern. In order to prevent illegal activities, this study combines financial risk control with machine learning to identify and predict the risks of users with poor credit. Experimental results demonstrate high performance in user financial credit analysis.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)