A novel context-aware caching scheme for 5G networks

Dr. Noman Islam

13th International Conference - Mathematics, Actuarial, Computer Science & Statistics (MACS 13), IoBM, Karachi

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December 14-15th, 2019

Samsung’s 5G rainbow

1. Very high data rates
2. High spectral efficiency
3. Speed during mobility conditions
4. High data transmission rates even at the boundary of a cell
5. Maximum number of concurrent connections
6. Reduced delay in communication
7. Low cost

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Caching has been regarded as amongst the five most disruptive technologies for 5G networks

Introduction

• Most of the requests on cellular networks are for videos
• Popular contents can be cached
• Where to cache?
• Cache at the edge on small base stations or mobile terminals

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Objective

• The paper analyzes the current approaches available for caching in 5G networks
• Discusses a context-aware collaborative filtering based caching scheme

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Increasing demand for data

• Rise in computing devices, connectivity medium
• How to cope with soaring demands for data?
• Capacity can be increased by installing more access points or base stations
• There is a limit to the network densification
• Most of the contents are videos or social media data

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Caching in 5G network

• If the popular contents (such as videos) can be proactively cached at the edges closer to the user, the backhaul network can be offloaded.
• Caching can not only avoid the network congestion but also reduce the latency incur in response to user request.

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Current approaches

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Research gap

• Caching is a very important research problem in 5G networks
• Only one study is found that considered context parameters such as mobility of the nodes while performing caching.
• Context which is of paramount importance in next generation network has not been considered in most of the proposals.

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Collaborative filtering

• Collaborative filtering is based on the idea that people who liked similar things in past would likely to have same opinion about future items.
• Collaborative filtering approaches are classified as neighborhood based and latent factor approaches.
• The former finds a set of neighbors to a user or item. While in latent factor approach, the rating of a user is decomposed using matrix factorizing technique.

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Bastug et al. (2014)

• Bastug et al. have proposed an approach based on recommendation system for caching in 5G network. A popularity matrix is calculated by solving a least square problem. The regularized singular value decomposition was chosen to decompose the popularity matrix into two sub-matrices.

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Popularity matrix

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Context-aware collaborative filtering

• In the established domain of CF, context-aware approaches already exist
• They are classified as contextual pre-filtering, contextual post-filtering and contextual modeling
• We suggest employing these techniques to Bastug et al. (2014)

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TF-based context-aware collaborative filtering

• The demands for data items of a particular user are predictable based on a popularity matrix P
• Each small cell base station is equipped with storage capabilities M to cache the popular contents.
• As the storage available is small, a popularity matrix P is used to decide what particular contents to be cached. It is also assumed that the user arrives randomly in the network and the file samples to be cached are drawn from ZipF(α) distribution.

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Popularity matrix

• We extend the popularity function P such that:

P: Users × Item × Context → Ratings

• Hence, the popularity matrix is a function of not only users and items, but also the context in which user issued the request for the particular item.

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• Six contextual information has been identified based on Schmidt et al. [27]: information about user, social information, user’s tasks, location, infrastructure and physical conditions.
• The contextual information can be obtained from various sources such as sensors and logs.
• The popularity matrix is indexed according to a user, item and the specific context in which a data / video item is requested.

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Tensor factorization

• Using tensor factorization, a low rank version of popularity matrix is constructed as shown in Figure.
• The tensor factorization decomposes the popularity matrix into factors of users, items and context inferred from popularity.
• There are a number of tensor factorization techniques available. We recommend the technique proposed by Karatzoglou et al. (2010).

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Performing caching

• The resultant popularity matrix can be used for deciding the items to be cached proactively.
• The most popular files are greedily cached until there is not enough storage available.
• This helps in offloading the network during peak hours as most of the requests are satisfied from the cache.

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Conclusion

• A collaborative filtering based context-aware caching scheme has been proposed for 5G networks.
• The proposed approach stores the popularity matrix as a combination of user, item and context’s rating.
• The popularity matrix is used to decide about caching the data items.
• The future work lies in the implementation of proposed approach

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References

• A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, "Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering," presented at Proceedings of the fourth ACM conference on Recommender systems, 2010.
• E. Bastug, M. Bennis, and M. r. Debbah, "Living on the edge: The role of proactive caching in 5G wireless networks," IEEE Communications Magazine, vol. 52, pp. 82-89, 2014

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