4.7 Article

HunterPlus: AI based energy-efficient task scheduling for cloud-fog computing environments

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Theory & Methods

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Shreshth Tuli et al.

Summary: Intelligent task placement and management in large-scale fog platforms is a challenging task due to the volatile nature of modern workload applications and sensitive user requirements. This paper proposes a Gradient Based Optimization Strategy using Back-propagation (GOBI) and a Coupled Simulation and Container Orchestration Framework (COSCO) to address this challenge. Experimental results show that these approaches significantly improve energy consumption, response time, Service Level Objective, and scheduling time compared to state-of-the-art algorithms.

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS (2022)

Article Computer Science, Software Engineering

HUNTER: AI based holistic resource management for sustainable cloud computing

Shreshth Tuli et al.

Summary: The article introduces a comprehensive resource management technique called HUNTER, based on artificial intelligence, for sustainable cloud computing. By formalizing the goal of energy optimization in data centers as a multiobjective scheduling problem and considering three important models: energy, thermal and cooling, HUNTER outperforms existing baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature.

JOURNAL OF SYSTEMS AND SOFTWARE (2022)

Article Computer Science, Artificial Intelligence

Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO

Fatma M. Talaat et al.

Summary: Fog computing is a decentralized computing structure that faces challenges in task scheduling and resource management. This paper introduces a dynamic load balancing technique using convolutional neural network and modified particle swarm optimization to achieve load balancing in fog computing.

KNOWLEDGE AND INFORMATION SYSTEMS (2022)

Review Computer Science, Theory & Methods

Fog computing: A taxonomy, systematic review, current trends and research challenges

Jagdeep Singh et al.

Summary: Fog computing has become a well-established paradigm for optimizing Quality of Service requirements among IoT devices. This systematic literature review paper investigates the current status in the area of fog computing, discussing important characteristics and issues related to its architectural design and implementation details. Various open research challenges and promising future directions in the field are highlighted for further research.

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING (2021)

Article Computer Science, Information Systems

BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

Md Ebtidaul Karim et al.

Summary: With the advancement of cloud computing technology, the demand for maximizing cloud resources is increasing. Effective virtual machine consolidation and migration can reduce energy consumption in cloud data centers, while accurate workload prediction is crucial for effective task scheduling. Our proposed hybrid prediction model, BHyPreC, utilizing Bidirectional LSTM on top of stacked LSTM and GRU, demonstrates better accuracy compared to other statistical models for predicting cloud VM's future CPU usage workload.

IEEE ACCESS (2021)

Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

Article Mathematics, Applied

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network

Alex Sherstinsky

PHYSICA D-NONLINEAR PHENOMENA (2020)

Article Engineering, Electrical & Electronic

Gated Graph Recurrent Neural Networks

Luana Ruiz et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2020)

Article Computer Science, Software Engineering

Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge

Sukhpal Singh Gill et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2019)

Article Computer Science, Theory & Methods

Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management

Joseph Nathanael Witanto et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)

Article Computer Science, Theory & Methods

Holistic energy and failure aware workload scheduling in Cloud datacenters

Xiang Li et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)

Article Computer Science, Information Systems

Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers

Ziqian Dong et al.

JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS (2015)

Article Computer Science, Information Systems

Metaheuristic Scheduling for Cloud: A Survey

Chun-Wei Tsai et al.

IEEE SYSTEMS JOURNAL (2014)

Article Construction & Building Technology

Data center optimization using PID regulation in CFD simulations

Baptiste Durand-Estebe et al.

ENERGY AND BUILDINGS (2013)

Article Computer Science, Hardware & Architecture

Energy efficient utilization of resources in cloud computing systems

Young Choon Lee et al.

JOURNAL OF SUPERCOMPUTING (2012)