Towards joint resource allocation and routing to optimize video distribution over future internet

Abstract

Given the exploding growth of video traffic, efficient video distribution is essential to the future Internet. Therefore, how to optimize its networking cost is a critical research problem. In this paper, we introduce Network Function Virtualization (NFV) in conjunction with Software-Defined Networking (SDN) to minimize the cost via joint orchestration of caching, transcoding and routing functions. Specifically, we propose a two-step iterative approach. First, in NFV-based resource allocation phase, we maximize total cache hits by optimally allocating storage and computing resources for a giving routing policy. Second, in SDN-based routing phase, we minimize the networking cost by optimally configuring the routing matrix for a given resource placement. Finally, we analytically prove their iterative repeat converges to the joint optimum. Through extensive simulations, we verify its convergence, and performance gains compared with the optimal solution of either phase alone. By examining numerical results, we obtain some operational guidelines. From the resource allocation aspect, we should allocate more resources to the node with heavier request rate. From the routing aspect, for each node-server pair, the node should split the traffic across multiple paths with identical shortest hops if there are many, or use the shortest path alone if there is only one.

Extendable NFV-Integrated Control Method Using Reinforcement Learning

“…Jin et al [11] proposed optimization method to minimize the cost of caching, transcoding, and routing functions for cost-efficient video distribution over the future Internet. They developed two algorithms for maximizing total cache hits and minimizing the networking cost.…”

Bitrate Adaptation-aware Cache Partitioning for Video Streaming over Information-Centric Networks

“…Examples include building cache models that accommodate multiple bitrates of the same content [6], [21], [22]. However, these work either drives caching mechanisms using the steady states that emerge from modelling bitrate adaptation as a Markovian process [6], or assumes random behaviour from bitrate adaptation generated by Gaussian model [21]. While insightful, these studies are built on assumptions that overlook the variations of realistic client-side bitrate adaptations.…”

Embracing social big data in wireless system design

“…For example, cloud CDN, built on the cloud computing infrastructure, is cost efficient due to its on-demand pricing strategy [20,21] . Software defined networking [22] separates the control plane and data plane from the traditional network design philosophy and adopts a centralized control mechanism to collect network status and make better decisions than the traditional distributed mechanisms do. Finally, we can utilize various optimization tools to find the optimal system deployment policy, such as data driven optimization, game theory, approximation optimization etc.…”