Active measurements are integral to the operation and management of networks, and invaluable to supporting empirical network research. Unfortunately, it is often cost-prohibitive and logistically difficult to widely deploy measurement nodes, especially in the core. In this work, we consider the feasibility of tightly integrating measurement within the infrastructure by using Software Defined Networks (SDNs). We introduce "SDN as Active Measurement Infrastructure" (SAAMI) to enable measurements to originate from any location where SDN is deployed, removing the need for dedicated measurement nodes and increasing vantage point diversity. We implement ping and traceroute using SAAMI, as well as a proof-of-concept custom measurement protocol to demonstrate the power and ease of SAAMI's open framework. Via a large-scale measurement campaign using SDN switches as vantage points, we show that SAAMI is accurate, scalable, and extensible.
Zeng al first posited the notion of SDN controllers instructing switches to send test packets in the context of their Automated Test Packet Generation system in @cite_18 . We extend this idea to the variety of active measurements in use today, and implement such a system in real SDN hardware and software.
@cite_18
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In this paper we address the problem of Software Defined Networking (SDN) controller placement in large networks. Indeed, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. However, the number and the location of controllers in large networks remain an issue. In this context, several works have been proposed to find the optimal placement of SDN controllers. Most of them consider latency among SDN controllers and switches as the main metric. In this work, we go beyond the state of art by proposing a solution that considers at the same time three critical objectives for the optimal placement of controllers: (i) the latency and communication overhead between switches and controllers; (ii)the latency and communication overhead between controllers; (iii) the guarantee of load balancing between controllers. We then solve the system by using Bargaining Game in order to find a fair trade off between these objectives. Simulation results clearly demonstrate the effectiveness of the proposed solution in finding the optimal placement of controllers that enforces this trade-off.