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In [24], the authors focus on the hybrid SDN network scenario and discuss about the migration algorithms from the perspective of traffic engineering in an ISP network. It mainly concentrates on searching for an optimized migration sequence of the routers in the traditional IP network to minimize the maximum link utilization. But it does not give a specific SDN switch deployment plan and needs to set the switch ports. We deploy the minimum number of SDN switches within budget and achieve almost all of the flows go through at least one SDN switch. In [11], Chu et al. summarize different types of hybrid SDN networks including topology- based, service-based and class-based hybrid SDN networks. They show a number of use cases in which hybrid models can mitigate the respective limitations of traditional and SDN approaches, pro- viding incentives to (partially) transition to SDN. We propose the flow-based scheme that can control almost all the flows by deploying only a small number of SDN switches.

The main idea in [25] is that the controller first chooses the set of routes that minimizes the number of used network equipments for the current traffic, and then they put SDN nodes in sleep mode by putting them in power save mode which also turns off net- work interfaces. The authors consider a typical dynamic traffic of an operator and adapt the numbers of active and inactive network equipments during the day. They reused the idea in [9] to reroute the flows from any node, with a turned off link, to any other node with a direct path towards the destination which does not include a disabled link. The goal is to avoid waiting for the convergence of legacy routing protocols by using tunnels from a node with a failing link to an SDN node which can reach an alternative OSPF shortest path in one hop. Thus, their scheme need to pre-configure the switch in advance, and also need to set a large number of tun- nels. The authors in [15] consider to find minimum-power network subsets in partially deployed SDNs. The objective of this paper is to develop a scheme that is able to dynamically adjust the active network subsets in the partially deployed SDN to satisfy changing traffic loads, as well as to reduce unnecessary energy consumption. The controller is responsible for all the SDN elements power states, but those non-SDN elements power states cannot be operated by the controller. Therefore, the basic idea of their solution is to shut down as many SDN elements as possible, in the condition that the alive network subset can satisfy the traffic demand, so that they can reduce unnecessary energy consumption. But the power states of non-SDN elements cannot be operated by the controller, the total saving energy in the network only include the power consumption of the SDN elements that can be shutdown.

3 Motivation and Overview

Throughout this paper, we try to solve the challenge of how to incrementally deploy SDNs to achieve energy saving. To achieve the efficient energy saving, we are faced with two questions: 1) given a limited number of SDN switches, how to place them to achieve the maximum NCA improvement? 2) Fixed SDN switches deployment location, how to maximize energy savings by rerouting the flows. We first deploy SDN switches to allow almost all of the flows to go through at least one SDN switch. Then we design the EPC algorithm to achieve energy savings by turning off the idle links and switches.

The latest OpenFlow protocol supports MPLS technology [26], and the Push MPLS header actions can push new MPLS headers onto the packet. The controller can install flow table entries on SDN switches to encapsulate MPLS labels for each packet to indicate the routing information of forwarding path. Therefore, we can achieve accurate path control for each flow by encapsulating MPLS in networks. We propose an energy-aware rerouting model for hybrid SDN networks based on MPLS labels. The key is that we can flexibly change the forwarding path of the flows by encapsulating MPLS tags to indicate the forwarding information, and then we shutdown idle links and switches to achieve energy saving. Moreover, we can aggregate the flow table entries based on the segmented routing between SDN switches. Next we show how to deploy SDN switches and how to precisely control the flow path to achieve energy savings in detail.

3.1 Incrementally deploy SDN switches

We cannot deploy SDN switches for the entire network at one- time limited by resource constraint. The key is how to deploy limited SDN switches to achieve maximum NCA improvement. We assume the NCA is the flows that can be flexibly managed by deploying SDN switches in the network [27]. Through investigation we find that there exist key nodes in the WAN and date center that most of the traffic pass through them [9,19]. We can incrementally upgrade the least key nodes that all the flows must go through to improve NCA. The upgrading cost of a network is the total cost for upgrading conventional switches to SDN switches in the network. For switch vi, its cost depends on many factors, such as ni, the number of forwarding ports in the node, and si, the traffic processing speed in the node [28]. For example in Fig. 1, the SDN switch S1, S2, are the key nodes that all the flows of source- destination pairs must pass through them. We propose the Greedy upgrading algorithm to incrementally deploy SDN switches to improve the NCA. After deploying the SDN switches, the SDN controller collects network information and reroutes the flows to optimize the energy saving in the networks.

3.2 Explicit path control to achieve energy saving