Implementation and Verification of Autonomous Decentralized Flow

2011 InternationalSymposium Symposiumon onAutonomous AutonomousDecentralized DecentralizedSystems Systems 2011Tenth 10th International

Implementation and Verification of Autonomous Decentralized Flow Control Based on Local Interaction Tatsuro Sumi Graduate School of System Design, Hiroshima City University, 3-4-1 Ozuka-Higashi, Asa-Minami-Ku,Hiroshima, 731-3194 Japan Email: [email protected]

Chisa Takano Graduate School of System Design, Hiroshima City University, 3-4-1 Ozuka-Higashi, Asa-Minami-Ku,Hiroshima, 731-3194 Japan Email: [email protected]

Kenji Ishida Graduate School of System Design, Hiroshima City University, 3-4-1 Ozuka-Higashi, Asa-Minami-Ku,Hiroshima, 731-3194 Japan Email: [email protected]

Masaki Aida Graduate School of System Design, Tokyo Metropolitan University, Hino-shi,Tokyo,191-0065 Japan Email: [email protected]

Abstract—We have already proposed the framework of autonomous decentralized control based on local-interaction as a novel control mechanism for communication networks. This framework is based on the relation between local interaction and the solution yielded by a partial differential equation. In this framework, the state of the complete system is controlled by appropriately designing the autonomous operation of the subsystems. That is, the local action rules (micro-level) are designed to produce an appropriate state of the whole system at the macro-level. Our previous studies proposed diffusiontype flow control (DFC) as a solution for the extremely timesensitive flow control required by high-speed networks and we have evaluated the performance of DFC by simulation. In this paper, to apply DFC to real networks, we show the technique to implement the DFC functions on PCs and to investigate its performance in the presence of TCP.

low-speed network

high-speed network

Figure 1.

I. I NTRODUCTION

link node packet

node

link

node

node

Effect of large bandwidth-delay product.

medium time scale areas, respectively. Individual control mechanisms work well for their appropriate time scales and they cooperate with each other. An end-to-end control such as TCP acts on the time scale of the roundtrip delay (RTT). In high-speed networks, since a lot of packets are in transit on links, any delay in applying control greatly impacts network performance. However, since TCP forces the end hosts to provide flow control, decision-making is impossible in time scales shorter than the RTT. DFC yields the extremely time-sensitive flow control required for high-speed networks, and is designed so that can satisfy the following requirements: • It must be possible to collect the information needed by the control method. • The control should take effect immediately. Most research on flow control mechanisms focuses on some form of optimization problem [4]-[8]. Past studies

The rapid spread of the Internet will necessitate the construction of higher-speed backbone networks in the near future. In a high-speed network, it is impossible to implement time-sensitive control based on collecting global information about the whole network because, even though the propagation delay is identical with that in low-speed networks, node state varies rapidly due to changes in processing speed (Fig .1). We proposed diffusion-type flow control (DFC) to realize the extremely time-sensitive flow control required by high-speed networks [1], [2], [3]. Various control mechanisms in networks can be classified from the viewpoint the time scale of their control operations. Figure 2 shows the relationships of different types of control according to such a classification. They form a layered structure with respect to time scale. For example, routing and call admission control fall into the long and 978-0-7695-4349-9/11 $26.00 © 2011 IEEE DOI 10.1109/ISADS.2011.26

packet

187 179

Effective time scales

Dozens of minutes Several minutes

Call Admission Control

RTT

TCP, ECN

control by end hosts, including TCP, is widely used in current networks, and there has been a lot of research in this area [7], [8], [10]. However, since end-to-end or endto-node control cannot be applied to decision-making on a time scale shorter than the round-trip delay, it is inadequate to support decision-making on very short time scales. In low-speed networks, control delay of the order of RTT has a negligible effect on network performance. However, in highspeed networks, the control delay greatly affects the network performance. This is because the RTT becomes large relative to the unit of time determined by the node’s processing speed, although the RTT is itself unchanged. This means that nodes in high-speed networks experience a larger RTT, and this causes an increase in the sensitivity to control delay. To achieve rapid control on time scales shorter than the RTT, it is preferable to apply control by the nodes rather than by the end hosts (see Fig. 3). Let us consider the situation where the RTT is 100 ms when the network is congested. The upper panel of Fig. 3 shows the relationship between network speed and the number of packets influenced by the control delay, when flow control is implemented by end hosts. If the network speed is 10 Mbps, the number of packets influenced by control delay is only a few hundred. However, if the network speed is 100 Gbps, the number of packets is several million. Even if RTT is unchanged, the increase in the network speed significantly impacts network performance. If we apply node-by-node control (the lower panel of Fig. 3), the control delay is reduced by 99.95 %. We have evaluated the performance of DFC by the simulations in a high-speed network environment. In addition, we have showed that a combination of DFC and TCP achieves higher network performance than just TCP by simulations [11]. In this paper, to apply DFC to real networks, we show the technique to implement the DFC functions on PCs and to investigate its performance in the presence of TCP. The remainder of this paper is organized as follows. Section II describes the framework of DFC and in Sec. III, we show the details of our implementation of DFC. Sec. IV evaluates the network performances by PCs having the DFC functions (workig progress). Finally, Sec. V provides a conclusion to this paper.

Routing

small Most Time-sensitive

Diffusion-type Flow Control

number of packets influenced by control delay

Figure 2. Classification of various control mechanisms with respect to their effective time scales. control delay RTT = 100ms 10s under congestion 100,000,000 1s control delay of end 10,000,000 host cannot become 100ms smaller than RTT 1,000,000

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Figure 3. Relationship between the number of packets influenced by control delay and network speed. The upper panel illustrates control by end hosts and the lower one illustrates node-by- node control. (This calculation assumed average distance between nodes of 10 km, average number of hops = 5, and link utilization of 0.5.)

II. D IFFUSION -T YPE F LOW C ONTROL M ECHANISM In this section, we describe the framework of DFC as applied to high-speed wired networks.

overlook the requirement that network control must be time sensitive. The principles adopted for time-sensitive control are inevitably those of autonomous decentralized systems [9]. In DFC, by using predetermined rules, each node in a network manages its local traffic flow on the basis of only the local information directly available to it. In addition, the decisions made at each node can lead to optimal performance of the whole network. That is, the state of the whole network is controlled indirectly through the autonomous actions of each node. Decentralized flow

A. DFC Applying Local Interaction [12] When thinking about the interaction of two objects that are separated in space, there are two theories depending on the action mechanism; “action at a distance (non-local interaction)” and “action through a medium (local interaction). “Non-local interaction” is the direct interaction of two objects that are not linked by any field or medium. For 188 180

example, Newton’s law of universal gravitation is an example of “non-local interaction”; gravity force, F , established between object A and object B is directly proportional to mass m1 and m2 of the objects, and inversely proportional to the square of the distance |r1 − r2 | between object A and B as follows: m1 m2 , F (r1 , r2 ) = G |r1 − r2 |2 where G is the gravitational constant. Function F (r1 , r2 ) is decided by the distance, that is to say that it is necessary to know the value of the distance, in other word, the positions of both object A and B. From the viewpoint of network control, this is same as the requirement of centralized control that all global information about the network state must be collected. In the case of “local interaction”, on the other hand, any variation in the physical value at a point of space is transmitted to the adjacent point via the medium of the gravitational field at a limited speed. A temporal variation in such a gravitational field can be described by using a partial differential equation. We use the following example to elucidate “local interaction”. When we let a few drops of black ink fall into a glass tube filled with water, the ink density distribution follows a normal distribution and the ink spreads through the whole tube by diffusion (Fig. 4). In this process, the action within a minute region of water in the glass tube is very simple: the ink flows from the higher density side towards the lower density side. The rate of ink flow is proportional to the density gradient. Even though each segment acts autonomously and only local information is available, the ink density distribution throughout the glass tube exhibits orderly behavior. As an example of autonomous decentralized control based on local interaction, we previously proposed DFC mechanism that overcomes the difficulty of controlling high-speed networks. In this control mechanism, the state of the whole network is controlled indirectly through the autonomous action of each node. Each node manages its local traffic flow on the basis of only the local information directly available to it, by using predetermined rules. By applying DFC, the distribution of the total number of packets in each node in the network becomes uniform over time, and it exhibits orderly behavior. This property is suitable for fast recovery from congestion.

Figure 4.

Example of local interaction.

packet streams

feedback info. Figure 5.

flow ID =

node

Node interaction in our flow control model.

the transmission rate for packets to the downstream node i + 1 using the received feedback information, and it adjusts its transmission rate towards the downstream node i + 1 accordingly. Let us assume that there are Mi flows sharing the link between node i and node i + 1; we identify them as j (j = 1, 2, . . . , Mi ). Each node i autonomously determines the transmission rate Jij for flow j on the basis of the feedback information obtained from the downstream node i + 1 and its own information. The transmission of packets and feedback information both experience the same propagation delay. The transmission rate Jij (t) for flow j of node i at time t is determined by Jij (t) = max(0, min(Lji (t), J˜ij (t))), J˜j (t) = rj (t − di ) − Di (nj (t − di ) − nj (t)), i

i

i+1

i

(1) (2)

where Lji (t) denotes the available bandwidth for flow j of the link from node i to node i + 1 at time t, nji (t) denotes the number of packets belonging to flow j in node i at time t, rij (t − di ) is the required rate for flow j derived from the feedback information from the downstream node i + 1 (hereafter called the “notified rate”), and di denotes the propagation delay between nodes i and i + 1. Di is a parameter used by DFC.

B. Behavior of Diffusion-Type Flow Control Mechanism Figure 5 shows the interactions between nodes (routers) in DFC, using a network model with a simple 1-dimensional configuration. In DFC, node i (i = 1, 2, . . . ) transfers packets to node i + 1, and node i + 1 sends feedback information Fi+1 to node i. When node i receives feedback information from downstream node i + 1, it determines

Let the bandwidth of the link from node i to node i+1 be Bi . Lji (t), the available bandwidth for flow j, is derived by assuming that Bi is shared by the different flows according to weight Wij (t), that is, 189 181

Table I A DDITIONAL F UNCTIONS OF DFC

Kij (t) = Mi

Bi

k=1 1{k=active}

Wij (t) = Mi

− Di (nji+1 (t − di ) − nji (t)),

Layer of OSI model Layer 3 and 4

(3)

Kij (t)

k k=1 1{k=active} × Ki (t)

,

Layer 7

(4)

Lji (t) = Wij (t) × Bi ,

FOR

OSI

MODEL .

Functions of DFC Receipt of DFC packets, I/F for provision of DFC control information Flow management, etc. Making and exchange DFC control information Calculation of transmission rate, Logging information for DFC, etc.

(5)

where 1{k=active} is the indicator function. It is equal to 1 if flow k is active at time t, otherwise 0. This rule means that a flow with larger second term in Eq. (2), can get a larger transmission rate and can transmit a larger volume of traffic to the downstream node. The feedback information for flow j is created at fixed intervals, τi , by node i and consists of three quantities as follows: j Fji (t) = (ri−1 (t), nji (t), ji (t)). (6)

To guarantee the diffusion effect for any condition, it is necessary to set the value of D in the following range [13]: 1 . (11) 2 To enable an intuitive understanding, we briefly explain the physical meaning of DFC. We replace i with x and apply a continuous approximation. Then the propagation delay becomes di → 0 for all i and the packet flow Eq. (2) is expressed as 0