Exploiting Chemistry for Better Packet Flow Management 1: Introduction

Exploiting Chemistry for Better Packet Flow Management 1: Introduction

Perhaps the phrase “don’t reinvent the wheel” is overused. However, many newer disciplines, particularly in the technology sector, seem to insist on it. One thing physical engineers learned long ago was to study the world around them, work with it, and emulate it in their designs. Network engineering should be no different. In a technical report from 2011, authors Thomas Meyer and Christian Tschudin from the University of Basel describe a highly elegant natural flow management method [11] that exploits much of the hard work done in the chemistry sector in chemical kinetics. They describe a scheduling approach that creates an artificial chemistry as an analogue of a queueing network, and uses the Law of Mass Action to schedule events naturally. The analysis of such networks utilizing their implementation is simplified regardless of the depth one wishes to go into. In addition, they show a congestion control algorithm based on this framework is TCP fair and give evidence of actual implementation (a relief to practitioners).

This report will discuss their paper at length with a goal of covering not only their work, but the underlying ideas. Since the paper requires knowledge of chemical kinetics, probability, queueing theory, and networking, it should be of interest to specialists in these disciplines, but a comprehensive discussion of the fundamentals glossed over in the paper would make dissemination more likely.

Overall Review of the Paper

The paper is well written, readable, and quite clear despite the breadth of scope it contains. It’s a major benefit to the authors and readers that their theoretical model has been tested in a proof-of-concept network. The work shows much promise for the networking space as a whole.

Overview of the Paper and Chemistry Basics

Just as in chemistry and physics, packet flow in a network has microscopic behavior controlled by various protocols, and macro-level dynamics. We see this in queueing theory as well–we can study (typically in steady-state to help us out, but in transient state as well) the stochastic behavior of a queue, but find in many cases that even simple attempts to scale the analysis up to networks (such as retaining memorylessness) can become overwhelming. What ends up happening in many applied cases is a shift to an expression of the macro-level properties of the network in terms of average flow. The cost of such smoothing is an unpreparedness to model and thus deal effectively with erratic behavior. This leads to overprovisioning and other undesirable and costly design choices to mitigate those risks.

Meyer and Tschudin have adapted decades of work in the chemical and physical literature to take advantage of the Law of Mass Action, designing an artifical chemistry that takes an unconventional non-work-conserving approach to scheduling. Non-work-conserving queues add a delay to packets and have tended to be avoided for various reasons, typically efficiency. Put simply, they guarantee a constant wait time of a packet regardless of the number of packets in a queue by varying the processing rate with fill level of the queue. The more packets in queue, the faster the server processes those packets.

Law of Mass Action in Chemistry

If we have some chemical reaction with reactants A_{1}, A_{2},\ldots, A_{n} and products B_{1}, B_{2}, \ldots, B_{m}, and the reaction is only forward1, then we may express the reaction as

A_{1} + A_{2} + \ldots + A_{n} \longrightarrow_{k} B_{1} + B_{2} + \ldots + B_{n}

where k is the rate constant. In a simple reaction A \to P, with P as the product, we can see the rate expressed nicely in a very basic differential equation form[9]:

-\frac{\text{d}c_{A}}{\text{d}t} = k\cdot c_{A}

This should actually look somewhat similar to problems seem in basic calculus courses as well. The rate of change of draining the reactant is a direct function of the current concentration.

The reaction rate r_{f} of a forward reaction is proportional to the concentrations of the reactants:

r_{f} = k_{f}c_{A_{1}}c_{A_{1}}\cdots c_{A_{N}}

for a set of reactants \{A_{i}\}.

The Queuing Analogue and Assumptions

Meyer and Tschudin[11] express the networking version of these chemical reactions in a very natural way. Packets are molecules. A molecular species is a queue, so molecules of species X go into queue X. The molecular species is a temporary buffer that stores particular packets types until they are consumed by a reaction (processed by some server in the queueing space). FIFO (first-in-first-out) discipline is assumed.


The figure above from the technical report shows how a small system of reactions looks in the chemical space and the queuing space. Where analysis and scheduling can get complicated is in the coupled nature of the two reactions. The servers both drain packets from queue Y, so they are required to coordinate their actions in some way. It’s important to note here that this equivalence rests on treating the queuing system as M/M/1 queues with a slightly modified birth-death process representation.

Typically, in an M/M/1 queue, the mean service rate is constant. That is, the service rate is independent of the state the birth-death process is in. However, if we model the Law of Mass Action using a birth-death process, we’d see that the rate of service (analogously, the reaction rate) changes depending on the fill-level of the queue (or concentration of the reactant). We’ll investigate this further in the next sections, discussing their formal analysis.

Related Work and Precedent

The authors noted that adding packet delay is not unheard of in the networking space. Delay Frame Queuing[12] utilizes non-work-conserving transmission at edge nodes in an ATM network in order to guarantee upper bounds on delay and jitter for virtual circuits. Three other researchers in 2008 (Kamimura et al) proposed a Constant Delay Queuing policy that assigns a constant delay to each packet of a particular priority stream and forward other best-effort packets during the delay[8].


The next article will discuss the formal mathematical model of an artificial packet chemistry. 



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  1. Obviously we can deal with both directions in chemistry to model chemical equilibrium, but only the forward part makes sense for our networking analogue.
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