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Issue No.05 - May (2007 vol.19)
pp: 614-630
Ling Liu , IEEE
ABSTRACT
In recent years, edge computing has emerged as a popular mechanism to deliver dynamic Web content to clients. However, many existing edge cache networks have not been able to harness the full potential of edge computing technology. In this paper, we argue and experimentally demonstrate that cooperation among the individual edge caches coupled with scalable server-driven document consistency mechanisms can significantly enhance the capabilities and performance of edge cache networks in delivering fresh dynamic content. However, designing large-scale cooperative edge cache networks presents many research challenges. Toward addressing these challenges, this paper presents cooperative edge cache grid (cooperative EC grid, for short)—a large-scale cooperative edge cache network for efficiently delivering highly dynamic Web content with varying server update frequencies. The design of the cooperative EC grid focuses on the scalability and reliability of dynamic content delivery in addition to cache hit rates, and it incorporates several novel features. We introduce the concept of cache clouds as a generic framework of cooperation in large-scale edge cache networks. The architectural design of the cache clouds includes dynamic hashing-based document lookup and update protocols, which dynamically balance lookup and update loads among the caches in the cloud. We also present cooperative techniques for making the document lookup and update protocols resilient to the failures of individual caches. This paper reports a series of simulation-based experiments which show that the overheads of cooperation in the cooperative EC grid are very low, and our architecture and techniques enhance the performance of the cooperative edge networks.
INDEX TERMS
Dynamic content caching, edge computing, cooperative caching, cache clouds.
CITATION
Lakshmish Ramaswamy, Ling Liu, Arun Iyengar, "Scalable Delivery of Dynamic Content Using a Cooperative Edge Cache Grid", IEEE Transactions on Knowledge & Data Engineering, vol.19, no. 5, pp. 614-630, May 2007, doi:10.1109/TKDE.2007.1031
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