Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective
Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …
implementation of 5G networks and the Internet of Things, as it is the best method of …
Energy aware edge computing: A survey
Edge computing is an emerging paradigm for the increasing computing and networking
demands from end devices to smart things. Edge computing allows the computation to be …
demands from end devices to smart things. Edge computing allows the computation to be …
Energy-sustainable iot connectivity: Vision, technological enablers, challenges, and future directions
OLA López, OM Rosabal… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Technology solutions must effectively balance economic growth, social equity, and
environmental integrity to achieve a sustainable society. Notably, although the Internet of …
environmental integrity to achieve a sustainable society. Notably, although the Internet of …
Spiking neural network (snn) with memristor synapses having non-linear weight update
Among many artificial neural networks, the research on Spike Neural Network (SNN), which
mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor …
mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor …
An artificial neural network based approach for energy efficient task scheduling in cloud data centers
M Sharma, R Garg - Sustainable Computing: Informatics and Systems, 2020 - Elsevier
Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost
and meets the standard set in green computing. Task scheduling an important problem …
and meets the standard set in green computing. Task scheduling an important problem …
Applications of machine learning in networking: a survey of current issues and future challenges
Communication networks are expanding rapidly and becoming increasingly complex. As a
consequence, the conventional rule-based algorithms or protocols may no longer perform at …
consequence, the conventional rule-based algorithms or protocols may no longer perform at …
Resource management in cloud radio access network: Conventional and new approaches
Cloud radio access network (C-RAN) is a promising mobile wireless sensor network
architecture to address the challenges of ever-increasing mobile data traffic and network …
architecture to address the challenges of ever-increasing mobile data traffic and network …
Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment
Fault Tolerance (FT) is one of the cloud's very critical problems for providing security
assistance. Due to the diverse service architecture, detailed architectures & multiple …
assistance. Due to the diverse service architecture, detailed architectures & multiple …
Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions
With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
Dag-based workflows scheduling using actor–critic deep reinforcement learning
GP Koslovski, K Pereira, PR Albuquerque - Future Generation Computer …, 2024 - Elsevier
Abstract High-Performance Computing (HPC) is essential to support the advance in multiple
research and industrial fields. Despite the recent growth in processing and networking …
research and industrial fields. Despite the recent growth in processing and networking …