Improving Fairness for Distributed Interactive Applications in Software-Defined Networks

Abstract

With the popularization of distributed interactive applications (DIAs), for getting good interactive experience among participants, efficient and fair allocation of network resource should be considered. In software-defined networks, the presence of central controllers provides novel solution to deploy customizable routing for interactive applications, which allows fine-grained resource allocation for DIAs to achieve fairness among participants. But opportunities always come with challenges, the wide spread user locations often require distribution of controllers to meet the requirements of applications. Hence, the latency involved among participants is directly affected by the processing time of controllers. In this context, we address the DIAs’ fair resource provisioning problems on computing and links load with the objective of balancing the achievable request rate and fairness among multiple flows in SDN networks. We firstly formulate the problems as a combination of controller loading and routing optimization. Then, we propose proactive assignment controller algorithm based on deep learning and fairness path allocation algorithm to share the bottleneck links. Compared with the state-of-the-art greedy assignment algorithm and priority order allocating algorithm, the final result is proven to get better fairness on controller and link load among DIAs’ participants by trace driven simulation.

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“…As a result, a manual partitioning method or a self-adaptive partitioning technique is necessary [35].…”

Towards fairness and QoE-based edge allocation for multiplayer virtual reality applications in edge computing

“…In [29], the authors proposed an edge assignment method that manages both network conditions and resource allocation, in order to balance the interaction delay among users belonging to the same cloud gaming group. On a similar note, the authors in [30] adopted a proactive deep learning assignment approach, in SDNassisted edge systems, to estimate the amount of traf ic originating from a speci ic user group, based on historical data. Subsequently, they employed a routing path allocation algorithm to improve fairness among the users, capitalizing on the knowledge acquired during the irst stage.…”