2,017 research outputs found
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks
Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth
Generation (6G) of wireless networks with the promise to significantly reduce
users' latency via offering storage capacities at the edge of the network. The
efficiency of the MEC network, however, critically depends on its ability to
dynamically predict/update the storage of caching nodes with the top-K popular
contents. Conventional statistical caching schemes are not robust to the
time-variant nature of the underlying pattern of content requests, resulting in
a surge of interest in using Deep Neural Networks (DNNs) for time-series
popularity prediction in MEC networks. However, existing DNN models within the
context of MEC fail to simultaneously capture both temporal correlations of
historical request patterns and the dependencies between multiple contents.
This necessitates an urgent quest to develop and design a new and innovative
popularity prediction architecture to tackle this critical challenge. The paper
addresses this gap by proposing a novel hybrid caching framework based on the
attention mechanism. Referred to as the parallel Vision Transformers with Cross
Attention (ViT-CAT) Fusion, the proposed architecture consists of two parallel
ViT networks, one for collecting temporal correlation, and the other for
capturing dependencies between different contents. Followed by a Cross
Attention (CA) module as the Fusion Center (FC), the proposed ViT-CAT is
capable of learning the mutual information between temporal and spatial
correlations, as well, resulting in improving the classification accuracy, and
decreasing the model's complexity about 8 times. Based on the simulation
results, the proposed ViT-CAT architecture outperforms its counterparts across
the classification accuracy, complexity, and cache-hit ratio
Space-Efficient Predictive Block Management
With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes
- …