Requests For Dynamic Content Research Articles

Joint Resource, Deployment, and Caching Optimization for AR Applications in Dynamic UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

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.

Addressing niche demand based on joint mobility prediction and content popularity caching

We present an efficient mobility-based proactive caching model for addressing niche mobile demand, along with popularity-based and legacy caching model extensions. Opposite to other proactive solutions which focus on popular content, we propose a distributed solution that targets less popular, personalised or dynamic content requests by prefetching data in small cells based on aggregated user mobility prediction information. According to notable studies, niche demand, particularly for video content, represents a significant 20–40% of Internet demand and follows a growing trend. Due to its novel design, our model can directly address such demand, while also make a joint use of content popularity information with the novelty of dynamically tuning the contribution of mobility prediction and content popularity on local cache actions.Based on thorough performance evaluation simulations after exploring different demand levels, video catalogues and mobility scenarios including human walking and automobile mobility, we show that gains from mobility prediction can be high and able to adapt well to temporal locality due to the short timescale of measurements, exceeding cache gains from popularity-only caching up to 41% for low caching demand scenarios. Our model’s performance can be further improved at the cost of an added computational overhead by adapting cache replacements by, e.g. in the aforementioned scenarios, 41%. Also, we find that it is easier to benefit from requests popularity with low mobile caching demand and that mobility-based gains grow with popularity skewness, approaching close to the high and robust gains yielded with the model extensions.

Adaptive Fast Path Architecture

Adaptive Fast Path Architecture (AFPA) is a software architecture that dramatically improves the efficiency, and therefore the capacity, of Web and other network servers. The architecture includes a RAM-based cache that serves static content and a reverse proxy that can distribute requests for dynamic content to multiple servers. These two mechanisms are combined using a flexible layer-7 (content-based) routing facility. The architecture defines interfaces that allow these generic mechanisms to be exploited to accelerate a variety of application protocols, including HTTP. Efficiency is derived from maximizing the number of requests that are handled entirely within the kernel, using a deferred-interrupt context instead of threads wherever possible. AFPA has been implemented on several server platforms including Microsoft Windows NT® and Windows® 2000, OS/390®, AIX®, and most recently Linux. By conservative estimates, AFPA more than doubles cap acity for serving static content compared to conventional server architectures, and has allowed IBM to establish a leadership position in Web server performance. A prototype implementation of AFPA on Linux delivers more than 10000 SPECweb96 operations per second on a single processor.