The rapid growth of cloud computing, network virtualization and big data brings new challenges for computer networks. By decoupling the control plane and the data plane, software defined networking becomes an emerging paradigm to enable network innovation with unprecedented programmability. The major concerns are performance issue for large networks and how to facilitate network management by SDN visibility.

Initially, we explore the extra flow setup latency by the controller and switch communication. To eliminate the overhead, we propose a system which predicts frequent communication pair and proactively installs forwarding wildcard rules.

Then, we concentrate on software defined measurement and propose three novel schemes to optimize network monitoring efficiency in a...[ Read more ]

The rapid growth of cloud computing, network virtualization and big data brings new challenges for computer networks. By decoupling the control plane and the data plane, software defined networking becomes an emerging paradigm to enable network innovation with unprecedented programmability. The major concerns are performance issue for large networks and how to facilitate network management by SDN visibility.

Initially, we explore the extra flow setup latency by the controller and switch communication. To eliminate the overhead, we propose a system which predicts frequent communication pair and proactively installs forwarding wildcard rules.

Then, we concentrate on software defined measurement and propose three novel schemes to optimize network monitoring efficiency in a top-down approach. First, we present a cross-layer optimization for sketch-based measurement. We observe the diminishing marginal utility property of sketch-based measurement. By trading a little accuracy, we dramatically shrink the measurement resource usage, and develop a two-stage heuristic to efficiently assign concurrent measurement tasks to underlying switches.

Second, we propose schemes to optimize flow statistics collection. We point out flow statistics polling is a fundamental primitive for software defined measurement. Based on this observation, we propose a generic optimization which is compatible with all existing measurement frameworks. Two monitoring schemes are presented to achieve different levels of measurement granularity.

Finally, we propose a measurement-aware controller placement which reduces the overhead in the physical layer. Our proposal is cost-effective and application-agnostic. The placement model takes both the synchronization and flow statistics polling cost into account. Two heuristics are presented to efficiently generate near-optimal placements for large-sized networks.

We demonstrate the effectiveness of our proposals by conducting experiments on various network topologies with real-world traffic traces.