600 research outputs found
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin
Learning to Solve Climate Sensor Placement Problems with a Transformer
The optimal placement of sensors for environmental monitoring and disaster
management is a challenging problem due to its NP-hard nature. Traditional
methods for sensor placement involve exact, approximation, or heuristic
approaches, with the latter being the most widely used. However, heuristic
methods are limited by expert intuition and experience. Deep learning (DL) has
emerged as a promising approach for generating heuristic algorithms
automatically. In this paper, we introduce a novel sensor placement approach
focused on learning improvement heuristics using deep reinforcement learning
(RL) methods. Our approach leverages an RL formulation for learning improvement
heuristics, driven by an actor-critic algorithm for training the policy
network. We compare our method with several state-of-the-art approaches by
conducting comprehensive experiments, demonstrating the effectiveness and
superiority of our proposed approach in producing high-quality solutions. Our
work presents a promising direction for applying advanced DL and RL techniques
to challenging climate sensor placement problems
Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method
Multi-access Edge Computing (MEC) is an emerging computing paradigm that
extends cloud computing to the network edge (e.g., base stations, MEC servers)
to support resource-intensive applications on mobile devices. As a crucial
problem in MEC, service migration needs to decide where to migrate user
services for maintaining high Quality-of-Service (QoS), when users roam between
MEC servers with limited coverage and capacity. However, finding an optimal
migration policy is intractable due to the highly dynamic MEC environment and
user mobility. Many existing works make centralized migration decisions based
on complete system-level information, which can be time-consuming and suffer
from the scalability issue with the rapidly increasing number of mobile users.
To address these challenges, we propose a new learning-driven method, namely
Deep Recurrent Actor-Critic based service Migration (DRACM), which is
user-centric and can make effective online migration decisions given incomplete
system-level information. Specifically, the service migration problem is
modeled as a Partially Observable Markov Decision Process (POMDP). To solve the
POMDP, we design an encoder network that combines a Long Short-Term Memory
(LSTM) and an embedding matrix for effective extraction of hidden information.
We then propose a tailored off-policy actor-critic algorithm with a clipped
surrogate objective for efficient training. Results from extensive experiments
based on real-world mobility traces demonstrate that our method consistently
outperforms both the heuristic and state-of-the-art learning-driven algorithms,
and achieves near-optimal results on various MEC scenarios
- …