Community oriented in‐network caching and edge caching for over‐the‐top services in adaptive network conditions to improve performance

Abstract

SummaryOver‐the‐top (OTT) services such as Netflix, Amazon Prime, and YouTube generate the most dominant form of traffic on the Internet today. There is increasingly high demand for resource intensive 3D contents, interactive media, 360 media, and user‐generated contents. As the amount of contents keep increasing in multiple folds, it is important to cache contents intelligently. Caching algorithm needs to exploit in‐network caching, community‐based pre‐caching, and a combined approach. Hence, we survey CDN‐based edge caching infrastructures including OpenConnect (Netflix) and Google Edge, followed by CCN based in‐network caching. We implement and compare four different approaches for caching contents including (1) in‐network caching, (2) edge caching, (3) community‐based in‐network caching, and (4) community‐based edge caching. We run our algorithms on adaptive network conditions with different topologies, cache size, content popularity, and request arrivals in and compared the delay for all these four approaches. We verify our model by calculating important performance parameters including hop count, redundancy, and hop count variances. Hopcount is an important performance parameter as it influences the processing, queuing, and transmission delays. We focus on determining if an in‐network caching approach is any better than edge caching. We reach several conclusions. First, in most of the scenarios, community‐based in‐network caching performs the best. Second, if the cache size is lesser than 30% of the total content size then community‐based edge caching is better for less popular contents. Finally, our statistical analysis also reveals that a community‐based edge caching mechanism is least affected by varying cache sizes and dynamic user behavior, which makes it a better choice for providing Service Level Agreement.

Design and Analysis of Distributed Tree Growing Algorithms

“…Pandey et al [32] implements and compares different approaches of content cache on the internet. The approaches are evaluated using different network topologies, cache size, content popularity, and a number of requests.…”

Proactive content caching in edge computing environment: A review

“…Yuan 21 states that the Lagrange multipliers, in addition to the cache hit rate, are useful for the delay. Pandey, 37 using Barabasi–Albert distributions and tree structure and graphs, significantly impacts delay parameters.…”

“…Pandey et al, 37 to solve caching problems, proposed integrating community‐based caching with in‐network and edge caching. They designed four different algorithms with inputs: contents, communities, popularity, popularity distribution, cache replacement, cache size.…”