972 research outputs found
{\mu}-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments
Fog and Edge computing extend cloud services to the proximity of end users,
allowing many Internet of Things (IoT) use cases, particularly latency-critical
applications. Smart devices, such as traffic and surveillance cameras, often do
not have sufficient resources to process computation-intensive and
latency-critical services. Hence, the constituent parts of services can be
offloaded to nearby Edge/Fog resources for processing and storage. However,
making offloading decisions for complex services in highly stochastic and
dynamic environments is an important, yet difficult task. Recently, Deep
Reinforcement Learning (DRL) has been used in many complex service offloading
problems; however, existing techniques are most suitable for centralized
environments, and their convergence to the best-suitable solutions is slow. In
addition, constituent parts of services often have predefined data dependencies
and quality of service constraints, which further intensify the complexity of
service offloading. To solve these issues, we propose a distributed DRL
technique following the actor-critic architecture based on Asynchronous
Proximal Policy Optimization (APPO) to achieve efficient and diverse
distributed experience trajectory generation. Also, we employ PPO clipping and
V-trace techniques for off-policy correction for faster convergence to the most
suitable service offloading solutions. The results obtained demonstrate that
our technique converges quickly, offers high scalability and adaptability, and
outperforms its counterparts by improving the execution time of heterogeneous
services
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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