Identifying and using energy-critical paths

Abstract

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energyreduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimalityscalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.

Optimizing rule placement in software-defined networks for energy-aware routing

“…Similarly, the authors in [15] have set up a small testbed using OpenFlow switches to evaluate energy savings for their model. OpenFlow switches have also been mentioned in many existing works as an example of the traffic engineering method to implement the EAR idea [27,14]. However, as we can see, the testbed setups with real OpenFlow switches are quite small.…”

“…As shown in literature, many existing works have used OpenFlow as a traffic engineering approach to deploy EAR in a network. For instance, the authors in [13,14,15] have used real testbed with OpenFlow switches to evaluate their energy savings proposals. In these works, the flow table of each switch is assumed to hold an infinite number of rules.…”

Leveraging MPLS Backup Paths for Distributed Energy-Aware Traffic Engineering

“…Online ETE schemes make on the fly decisions according to the obtained information about the state of the network during the live operation of the network. Examples of these schemes were proposed in [8], [9], [21], [29], [30], [31]. While online ETE schemes are more efficient in reacting to unexpected traffic behaviors, a major technical hurdle for these schemes is how to efficiently coordinate between different decision-making entities in order to avoid conflicting decisions that may lead to severe and unpredictable consequences on the network performance.…”

“…Moreover, it uses multi-path routing for each SD pair. The same authors later proposed a simpler ETE scheme in [9]. This simpler scheme uses historical traffic matrices to identify different sets of paths that are suitable for different traffic patterns in the network.…”

“…Indeed, GBP differs significantly from most other existing online traffic engineering schemes (e.g. [7], [8], [9]) which target either energy savings or link failure protection but not both. Furthermore, GBP considers Quality-of-Service (QoS) by actively avoiding the use of excessively long backup paths for energy savings so as to avoid substantial packet delays, but allows the use of such paths for handling link failures.…”