Distributed Energy-Aware Routing Protocol

Transcription

1 Distributed Energy-Aware Routing Protocol Erol Gelenbe and Toktam Mahmoodi Intelligent Systems & Networks Group Department of Electrical & Electronic Engineering Imperial College, London SW7 2BT, UK Abstract. This paper presents an energy aware routing protocol (EARP) whose purpose is to minimise a metric that combines the total consumed power in the network and the QoS bounds requested by the incoming flows. The algorithm performs in a fully distributed manner thanks to the functionalities provided by the Cognitive Packet Network (CPN) which runs at each node of the network. Measurements on an experimental test-bed are presented showing a reduction in power consumption, as compared to a purely QoS driven approach. We also observe that the requested QoS level is also respected. 1 Introduction Energy efficient protocols have been extensively studied for wireless networks, because energy savings for battery powered nodes is crucial [8], while research on energy consumption is relatively new in wired networks even though the amount consumed is a significant fraction of the energy used for ICT systems [1]. Energy can be saved in the Internet by modifying routing policies, e.g. aggregating traffic alonga few routes and putting some network nodes to sleep [12]. The scheme in [2] examines various configurations of line cards so that switching on/off some components can minimise power consumption. A heuristic approach is presented in [4] that switches off some nodes in the network to minimise power. The work in [15] combines rate adaptation for active nodes with putting idle nodes to sleep to save energy in the network. An energy-aware congestion control technique is presented in [16], which controls the capacity each network node can offer to meet the actual traffic demand. Finally, [3] provides an estimate of potential energy savings that may be obtained in the Internet. In this paper we present an energy aware routing protocol (EARP) that attempts to minimise the total consumed power in the network, and also respects the QoS requested by each incoming flow. EARP relies on the underlying Cognitive Packet Network (CPN) [6] for the information it requires, and uses it to minimise power consumption. In other words, the smart packets in CPN [9] collect information with regard to the power usage at the nodes so that the CPN source routing scheme can include power consumption as a decision criterion. In previous work CPN has been proposed as a means to optimise energy consumption [11], and we continue this previous research.

2 2 E. Gelenbe and T. Mahmoodi The remainder of this paper is organised as follows. Section 2 elaborates the energy-aware routing protocol. After presenting the configuration of our network testbed, Section 3 illustrates the experimental results. Conclusions are presented in Section 4. 2 Energy and QoS Aware Routing At each node i, let us denote by T i, the traffic this node carries in packets/sec (pps). Assuming that a flow l carries a traffic of rate t l pps, then T i can be computed as: T i = t l (1) l F (i) where F (i) denotes the set of flows that use node i. Let p i (T ) and Q i (T ) be the power consumption and QoS requirements of node i when the traffic it carries is T. Adding a new flow k to node i will result in a change in power consumption and QoS at that node. Let p i (x) be the instantaneous power consumption in watts at node i when it carries x packets per seconds, including all aspects of packet processing: storing, routing, and forwarding them through appropriate link drivers. Define the Power Cost associated with the k th flow at node i by m k i (t k, T i ), as a combination of the flow s own power consumption, and of the impact it has on other flows which are using the node: m k i (t k, T i ) = ap i (T i + t k ) + b[p i (T i + t k ) p i (T i )] (2) where a, b. Here the first term is the total power (watts) due to adding the k th flow, multiplied by some constant a. The second term represents the increase in wattage for the other flows, multiplied by some constant b. The power related cost functions for the k-th traffic flow of rate t k on a path π(i) originating at node i is written as: m k π(i) (t k, T π(i) ) = m k n(t k, T n ), (3) n π(i) Similarly, we would have the QoS criterion, such as loss, delay or some other metric: Q k π(i) (t k, T π(i) ) = Q k n(t k, T n ) (4) n π(i) where T π(i) = (T n1,..., T n π(i) ) where n 1 = i, and the n j, with 1 j π(i) are the successive nodes of path π(i). The power related information are gathered from across the network using the approach presented by CPN, which runs autonomously at each node using Reinforcement Learning with a recurrent Random Neural Network [5]. The measurement results for this protocol are summarised in [7]. Since EARP is expected to minimise the overall cost of power while satisfying the requested QoS,

3 Distributed Energy-Aware Routing Protocol 3 the goal G i to be optimised will combine the power consumption with the QoS constraint. All quantities of interest for some flow k will relate to the forward path from any node i to the destination node of that flow. Thus the goal will take the form: G i = m k π(i) (t k, T π(i) ) + γ1[q k π(i) (t k, T π(i) ) Q k o > ](Q k π(i) (t k, T π(i) )) ν where ν 1, and γ > are constants meant to match the delay units with respect to power, while Q k o is the QoS value that should not be exceeded for flow k. Moreover, 1[X] is the function that takes the value if X is true, and takes the value 1 if X is false. The CPN reward function, R, is defined based on the function in (5) i.e. R = G 1. This reward function is utilised by the RL algorithm in the similar way as [9] to seek the most optimal path. 3 Experiments Our experimental testbed and the parameters that are used in the experiments are the same as those described in [14, ]. Each experiment ran for 6 sec, and measurements were collected from each node every five seconds. The performance of our proposed energy-aware routing scheme was compared with the CPN routing protocol that is only QoS driven, i.e., its aim is to minimise endto-end delay, and also with the routing algorithm that seeks shortest path. The shortest path here is selected by the CPN protocol and based on the available information in the smart packets. We use the presented model in [13] for the power consumption model of the routers that is defined based on the offline measurements from our testbed s nodes. It has been shown that a polynomial function can represent the relation between power consumption and traffic of a router, where the polynomial coefficients depend on the router s operating mode i.e. either the router receives, transmits or forward data. In the first scenario, we choose three pairs of source-destination nodes, and have set up three flows (23-12, 3-14, and 33-2). All three flows were first run at a data rate of.5 Mbps, which was then increased to 1, 1.5, 2, and 2.2 Mbps in successive rounds of the experiment simultaneously for all three flows. Figure 1(a) shows an interesting comparison of the measured total power consumption versus the five different data rates for the three flows. On the other hand, as would be expected, EARP results in higher end to end delays, shown in Figure 1(b). It can be seen that the delay achieved by the delay minimisation scheme is approximately 45% smaller in comparison to EARP. A similar experiment was s conducted with CPN, seeking shortest paths without consideration for the QoS. Although shortest path routing can potentially save energy by engaging fewer routers, our experimental results summarised in Figure 1(a) seem to contradict this, probably due to the non-linear relation between the routers power consumption and traffic. In the second scenario there are nine pairs of source-destination nodes. Three flows are initially active: (23-12, 3-14, and 33-2). Another three flows are initiated secs later: (2-26, 28-, and 35-7), and the remaining three flows are

4 4 E. Gelenbe and T. Mahmoodi 6 Total consumed power in the network (kw) Routing based on Delay Routing based on Shortest Path Average round trip delay (ms) Routing based on Delay Examined flows data rate (Mbps) (a) Total power consumption in the network vs. traffic rate Flow number (b) Average round trip delay Fig. 1. Scenario one: power consumption and round trip delay of the three examined flows. set up 2 secs after the experiment begins: (11-8, 13-24, and 29-6). Each flow s lifetime is 4 secs, and the experiment lasts 6 secs. All nine flows were first run at a data rate of.5 Mbps, which was then increased to 1, 1.5, 2, and 2.2 Mbps in successive rounds, simultaneously for all the flows. The power consumption in the network for data rates per flow of 1 Mbps, and 2 Mbps is shown in Figure 2 for the first 4 secs of the experiment (while the first three flows are yet active), versus the elapsed time. This figure shows a step increase in power consumption when the experiment s elapsed time is sec, and another step increase when the elapsed time is 2 sec, which refers to the newly initiated flows in the network at those two time instances. Further observation from Figure 2 reveals the savings in power consumption with EARP when the data rates for both flows is 1 and 2 Mbps. We report the end-to-end delay observed with EARP in this scenario and compare it with the delay minimisation scheme in Figure 3(a), where the average delay experienced by each flow is presented (the average is computed over all five data rates). It is seen that the QoS-only driven scheme experiences approximately 45% less latency as compared to EARP. Investigations on the path used by EARP are summarised in Figure 3(b); the average route length of each flow using EARP appears to be [.6) hops longer that of routes chosen by the shortest path algorithm. 4 Conclusions This paper introduces the energy-aware routing protocol (EARP) that attempts to minimise the total power consumption of each flow in a packet network, while keeping the principal QoS metric below an acceptable upper bound. EARP is fully distributed and uses the functionalities of CPN. We have experimented with EARP to compare its performance to a version of CPN that only attempts to minimise delay. Further experiments are carried out to compare EARP s per-

5 Distributed Energy-Aware Routing Protocol 5 Total consumed power in the network (kw) (Rate = 1Mbps) Routing based on Delay (Rate = 1Mbps) Routing based on Shortest Path (Rate = 1Mbps) (Rate = 2Mbps) Routing based on Delay (Rate = 2Mbps) Routing based on Shortest Path (Rate = 2Mbps) Experiments Elapsed Time (s) Fig. 2. Scenario two: Total power consumption in the network Vs. the experiment s elapsed time. Average round trip delay (ms) Routing based on Delay Average length of the end to end Path Routing based on Shortest Path Flow number (a) Average round trip delay Flow number (b) Average length of the end-to-end path Fig. 3. Scenario two: round trip delay and the route length of the active flows. formance with CPN when it seeks the shortest path. Although shortest path routing protocol can potentially save energy by engaging less routers, the nonlinear dependence of the routers power consumption on the data rates seem to contradict this intuitive assumption. Acknowledgements We are grateful for the support and technical motivation that has been provided by the EU FP7 Fit4Green Project. References 1. Berl, A., Gelenbe, E., Girolamo, M.D., Giuliani, G., de Meer, H., Dang, M.Q., Pentikousis, K.: Energy-efficient cloud computing. The Computer Journal 53(7),

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