TY - GEN
T1 - Data-driven approaches to edge caching
AU - Li, Guangyu
AU - Shen, Qiang
AU - Liu, Yong
AU - Cao, Houwei
AU - Han, Zifa
AU - Li, Feng
AU - Li, Jin
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/8/7
Y1 - 2018/8/7
N2 - Content caching at network edge is a promising solution for serving emerging high-throughput low-delay applications, such as virtual reality, augmented reality and Internet-of-Things. The traditional caching algorithms need to adapt to the edge networking environment since old traffic assumptions may no longer hold. Meanwhile, user/group content interest as a new important element should be considered to improve the caching performance. In this work, we propose two novel caching strategies that mine user/group interests to improve caching performance at network edge. The static user-group interest patterns are handled by the Matrix Factorization method and the temporal content request patterns are handled by the Least-Recently-Used or Nearest-Neighbor algorithms. Through empirical experiments with a large-scale real IPTV user traces, we demonstrate that the proposed caching algorithms outperform the existing caching algorithms and approach the caching performance upper bound in the large cache size regime. Leveraging on offline computation, we can limit the online computation cost and achieve good caching performance in realtime.
AB - Content caching at network edge is a promising solution for serving emerging high-throughput low-delay applications, such as virtual reality, augmented reality and Internet-of-Things. The traditional caching algorithms need to adapt to the edge networking environment since old traffic assumptions may no longer hold. Meanwhile, user/group content interest as a new important element should be considered to improve the caching performance. In this work, we propose two novel caching strategies that mine user/group interests to improve caching performance at network edge. The static user-group interest patterns are handled by the Matrix Factorization method and the temporal content request patterns are handled by the Least-Recently-Used or Nearest-Neighbor algorithms. Through empirical experiments with a large-scale real IPTV user traces, we demonstrate that the proposed caching algorithms outperform the existing caching algorithms and approach the caching performance upper bound in the large cache size regime. Leveraging on offline computation, we can limit the online computation cost and achieve good caching performance in realtime.
KW - Data-driven
KW - Edge caching
UR - http://www.scopus.com/inward/record.url?scp=85056385826&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056385826&partnerID=8YFLogxK
U2 - 10.1145/3229574.3229582
DO - 10.1145/3229574.3229582
M3 - Conference contribution
AN - SCOPUS:85056385826
T3 - NEAT 2018 - Proceedings of the 2018 Workshop on Networking for Emerging Applications and Technologies, Part of SIGCOMM 2018
SP - 8
EP - 14
BT - NEAT 2018 - Proceedings of the 2018 Workshop on Networking for Emerging Applications and Technologies, Part of SIGCOMM 2018
PB - Association for Computing Machinery, Inc
T2 - ACM SIGCOMM 2018 Workshop on Networking for Emerging Applications and Technologies, NEAT 2018
Y2 - 20 August 2018
ER -