Dynamic resource allocation by batch optimization for value-added video services over SDN

Abstract

We propose a video service architecture and a novel resource allocation optimization framework to enable network service providers (NSP) to offer value-added video services (VAVS) over software-defined networking including different service levels, service-level awareness of users, and associated business models. To this effect, we introduce a new batch-optimization framework, where resource (path, bitrate, and admission control) allocations for a small group of flows (consisting of new service requests and some existing ones) are performed simultaneously as the number of new service requests and network conditions vary. The optimization problem becomes NP-complete when path computations are jointly (re-)optimized as a group in order to accommodate all service requests to the extent possible, to best utilize entire network resources in a fair manner, and maximize network service provider's revenue. In order to compute dynamic resource allocations online, we propose a heuristic group-constrained-shortest path procedure that aims for a fair allocation of resources among a group of requests with the same service level, while maximizing the total NSP revenue. Experimental results demonstrate the feasibility of the proposed method for possible deployment by NSP to offer future VAVS, and that the proposed solution is close to the optimal solution, which is approximately computed using a divide-and conquer strategy, for varying network size and traffic load conditions. In particular, we show that processing service requests in batches significantly improves total revenue and fairness in congested mode of operation.