Abstract
Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes.
Highlights
The demand for broadband multimedia applications has been rising exponentially, leading to a dramatic increase in network capital and operating expenditures
Since the content retrieval cost is composed of weighted sum of traffic load and content retrieval delay, we proposes two Greedy algorithms, i.t
We adopted centric networks (CCN) architecture at the edge caching, to exploit the in-network content caching, and to utilize its inherent request aggregation mechanism to further reduce the amount of content request
Summary
The demand for broadband multimedia applications has been rising exponentially, leading to a dramatic increase in network capital and operating expenditures. In addition to content popularity prediction [17], [18], reinforcement learning [19] has been emerging to capture the dynamic popularity characteristics Their formulated optimization problems are able to approximate to the actual system performances with low content request intensity. To handle the massive and highly dynamic content requests at ENs, we formulate a proactive edge caching problem to minimize the content retrieval cost based on CCN architecture. We exploit both the inherent content caching and request aggregation mechanism in CCN to jointly minimize traffic load and content retrieval delay cost. We formulated a proactive edge caching optimization problem to handle the massive and highly dynamic content requests to ENs, with an objective to minimize the content retrieval cost.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
