The extensive growth in adoption of mobile devices pushes global Internet protocol (IP) traffic to grow and content delivery network (CDN) will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. In this praxis, Interconnected Cache Edge (ICE) based on different public cloud infrastructures with multiple edge computing sites is considered to help CDN service providers (SPs) to maximize their operational profit. The problem of resource allocation and performance optimization is studied in order to maximize the cache hit ratio with available CDN capacity.
The considered problem is formulated as a multi-stage stochastic linear programming model that involves jointly optimizing the resource allocation and network performance. The problem is challenged in reality since the multi-cloud SPs have dynamic price strategies in different regions, tasks could be time sensitive, and busy-hour traffic model is hard to simulate. To overcome these challenges, the praxis proposes a method to decompose the problem into (i) a resource-allocation problem with fixed task-offloading decisions and (ii) a performance optimization problem that optimizes the cache hit ratio, round-trip time (RTT) and edge processing time corresponding to the resource allocation.
The praxis addresses the problem using optimization solvers of the General Algebraic Modeling System (GAMS) and proposes a broker scheme (ICE: Interconnected Cache Edge) using cloud-based CDN with edge computing architecture to maximize expected profit. Experimental design shows that ICE performs closely to the optimal solution and that it significantly improves the CDN profitability and network performance over traditional approaches.
Communication, Computer Engineering, Electrical, Electronics Engineering
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Peng, Yang, "Resource Allocation and Task Scheduling Optimization in Cloud-Based Content Delivery Networks with Edge Computing" (2019). Engineering Management, Information, and Systems Research Theses and Dissertations. 6.