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Article
Computer Science, Theory & Methods
Gerta Sheganaku et al.
Summary: In business process landscapes, providing necessary computational resources for single process steps is a common challenge. This paper proposes an approach to optimize resource allocation for elastic processes using containers, aiming to minimize costs and maintain Service Level Agreements. The approach applies a four-fold auto-scaling method and utilizes Mixed-Integer Linear Programming for multi-objective optimization.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Physics, Multidisciplinary
Shuangming Yang et al.
Summary: This research proposes a novel spike-based framework called MeMEE, which uses entropy theory to establish a gradient-based online meta-learning scheme in a recurrent SNN architecture. The proposed MeMEE model effectively improves the accuracy and robustness of spike-based meta-learning performance. This research provides new perspectives for integrating information theory into machine learning to enhance the learning performance of SNNs.
Article
Neurosciences
Shuangming Yang et al.
Summary: This paper proposes a novel spike-based framework called HESFOL, which uses the entropy theory to establish a gradient-based few-shot learning scheme in a recurrent SNN architecture. Experimental results show that HESFOL can effectively improve the accuracy and robustness of spike-driven few-shot learning performance.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Neurosciences
Shuangming Yang et al.
Summary: This article presents a novel neuron model called SAM, which integrates spike-based learning with working memory. The experimental results demonstrate the energy efficiency and robustness of SAM in various challenging tasks. The study also explores the effects of SAM model variations on its working memory, aiming to uncover the biological mechanisms of working memory in the brain.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Spyridon Chouliaras et al.
Summary: Cloud computing offers new business opportunities, but efficiently managing hardware resources in the cloud environment remains challenging. This article introduces a containerized cloud application auto-scaling framework based on workload demand, utilizing threshold-based rules and convolutional neural networks to generate elastic scaling policies.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang et al.
Summary: This study introduces a scalable hardware framework for fault-tolerant context-dependent learning in neuromorphic computing, demonstrating an improvement in network throughput. The proposed system can be utilized for real-time decision-making, brain-machine integration, and research on brain cognition during learning.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang et al.
Summary: This article introduces a large-scale cerebellar network model and a cerebellum-inspired neuromorphic architecture, demonstrating improved biomimicry. Experimental results show that real-time operation can be achieved, with system throughput up to 4.70 times larger than previous works.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Diaz Jorge-Martinez et al.
Summary: Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) assists in decision making for controlling load scheduling and shifting, improving efficiency and reducing costs. The new system presented in this study significantly improves constraints associated with existing container scheduling techniques, providing multicriteria node selection.
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2021)
Article
Engineering, Multidisciplinary
Li Sun et al.
Summary: This paper discusses the applications and potential benefits of smart power generation control technology, focusing on the use of machine learning and data-driven control techniques in monitoring, optimizing, and controlling power generation systems, as well as their ability to counter uncertainties. A comprehensive view from the regulation level to the planning level is provided, highlighting the potential value of ML and DDC techniques in improving visibility, maneuverability, flexibility, profitability, and safety.
Proceedings Paper
Computer Science, Information Systems
Lucia Schuler et al.
Summary: Serverless computing has emerged as a new paradigm in cloud computing, promising large-scale and cost-effective services without the need for infrastructure management. Concurrency configuration plays a crucial role in determining the performance of serverless applications within the framework.
21ST IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2021)
(2021)
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Mahmoud Imdoukh et al.
NEURAL COMPUTING & APPLICATIONS
(2020)
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Shuangming Yang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2020)
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Computer Science, Theory & Methods
Fan Zhang et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
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Proceedings Paper
Computer Science, Hardware & Architecture
Fabiana Rossi et al.
2019 IEEE 12TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (IEEE CLOUD 2019)
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Vitor Goncalves da Silva et al.
APPLIED COMPUTER SYSTEMS
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Proceedings Paper
Computer Science, Theory & Methods
Shay Horovitz et al.
2018 IEEE 6TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD (FICLOUD 2018)
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Proceedings Paper
Computer Science, Hardware & Architecture
Yahya Al-Dhuraibi et al.
2017 IEEE 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD)
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Proceedings Paper
Computer Science, Hardware & Architecture
Philipp Hoenisch et al.
SERVICE-ORIENTED COMPUTING, (ICSOC 2015)
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