Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

AM Rahmani, E Azhir, S Ali, M Mohammadi… - PeerJ Computer …, 2021 - peerj.com
Recent advances in sensor networks and the Internet of Things (IoT) technologies have led
to the gathering of an enormous scale of data. The exploration of such huge quantities of …

A survey on scheduling techniques in computing and network convergence

S Tang, Y Yu, H Wang, G Wang, W Chen… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Intelligent resource management in blockchain-based cloud datacenters

C Xu, K Wang, M Guo - IEEE Cloud Computing, 2017 - ieeexplore.ieee.org
Nowadays, more and more companies migrate business from their own servers to the cloud.
With the influx of computational requests, datacenters consume tremendous energy every …

Experience-driven computational resource allocation of federated learning by deep reinforcement learning

Y Zhan, P Li, S Guo - 2020 IEEE International Parallel and …, 2020 - ieeexplore.ieee.org
Federated learning is promising in enabling large-scale machine learning by massive
mobile devices without exposing the raw data of users with strong privacy concerns. Existing …

Optimal decision making for big data processing at edge-cloud environment: An SDN perspective

GS Aujla, N Kumar, AY Zomaya… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
With the evolution of Internet and extensive usage of smart devices for computing and
storage, cloud computing has become popular. It provides seamless services such as e …

Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning

C Xu, K Wang, P Li, R Xia, S Guo… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In the age of big data, companies tend to deploy their services in data centers rather than
their own servers. The demands of big data analytics grow significantly, which leads to an …

Reinforcement-learning-and belief-learning-based double auction mechanism for edge computing resource allocation

Q Li, H Yao, T Mai, C Jiang… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
In recent years, we have witnessed the compelling application of the Internet of Things (IoT)
in our daily life, ranging from daily living to industrial production. On account of the …

Load balance based workflow job scheduling algorithm in distributed cloud

C Li, J Tang, T Ma, X Yang, Y Luo - Journal of Network and Computer …, 2020 - Elsevier
As the scale of the geo-distributed cloud increases and the workflow applications become
more complex, the system operation is more likely to cause the waste of resources and …

Learn-as-you-go with megh: Efficient live migration of virtual machines

D Basu, X Wang, Y Hong, H Chen… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Cloud providers leverage live migration of virtual machines to reduce energy consumption
and allocate resources efficiently in data centers. Each migration decision depends on three …

Cluster frameworks for efficient scheduling and resource allocation in data center networks: A survey

K Wang, Q Zhou, S Guo, J Luo - IEEE Communications Surveys …, 2018 - ieeexplore.ieee.org
Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …