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Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Deep configuration performance learning: A systematic survey and taxonomy
Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …
software system. However, given the increasing scale and complexity of modern software …
[PDF][PDF] Security enhancement by identifying attacks using machine learning for 5G network
Need of security enhancement for 5G network has been increased in last decade. Data
transmitted over network need to be secure from external attacks. Thus there is need to …
transmitted over network need to be secure from external attacks. Thus there is need to …
[HTML][HTML] A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers
The rapid growth of cloud computing in the last decade has led to an increasing concern
about the energy requirement of cloud data centers. Dynamic virtual machine (VM) …
about the energy requirement of cloud data centers. Dynamic virtual machine (VM) …
Edge computing-enabled Internet of Vehicles: Towards federated learning empowered scheduling
F Sun, Z Zhang, S Zeadally, G Han… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Classical edge computing algorithms assume that the execution time is always known in
resource allocation. However, in practice, the execution time in the edge server is hard to …
resource allocation. However, in practice, the execution time in the edge server is hard to …
Machine learning threatens 5G security
Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of
mobile networks. However, ML will also open the network to several serious cybersecurity …
mobile networks. However, ML will also open the network to several serious cybersecurity …
A two-stage multi-population genetic algorithm with heuristics for workflow scheduling in heterogeneous distributed computing environments
Y **e, FX Gui, WJ Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Workflow scheduling in Heterogeneous Distributed Computing Environments (HDCEs) is a
NP-hard problem. Although a number of scheduling approaches have been proposed for …
NP-hard problem. Although a number of scheduling approaches have been proposed for …
Resource allocation and task offloading for heterogeneous real-time tasks with uncertain duration time in a fog queueing system
Fog computing has become the primary infrastructure on the Internet for improving the
quality of service. We consider a fog queueing system with limited infrastructure resources to …
quality of service. We consider a fog queueing system with limited infrastructure resources to …
Reliability based workflow scheduling on cloud computing with deadline constraint
Distributed computing workflow is an effective paradigm to express a range of applications
with cloud computing platforms for scientific research explorations. One of the most difficult …
with cloud computing platforms for scientific research explorations. One of the most difficult …
Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures
Many resource management techniques for task scheduling, energy and carbon efficiency,
and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime …
and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime …