Exploiting Chemistry for Better Packet Flow Management 4: Scheduler Implementation

Exploiting Chemistry for Better Packet Flow Management 4: Scheduler Implementation

This article is the fourth part in a series based on a report reviewing the technical report of Meyer and Tschudin[11] who have extended the notion of an artificial chemistry to an artificial packet chemistry with the intention of exploiting the natural behavior of chemical reactions to design better flow management policies for computer networks. Part 1 introduced the chemistry foundations necessary for study of this technical report, including the Law of Mass Action. Part 2 elaborated on the mathematical model of an artificial packet chemistry. Part 3 discussed the various types of mathematical analyses for various queueing questions now available with the expression of a computer network and its flow as an artificial packet chemistry. This part will discuss an actual engineering application of all the ideas discussed thus far–a scheduler and a chemical control plane.

Implementation of a Scheduler Based on the Law of Mass Action

Likely, this section will be of greatest interest to network engineers. The authors have indeed designed and implemented a scheduler that utilizes this approach in an elegant fashion. In addition, they discuss a “chemical control plane” that can automatically be compiled from the abstract model. In another application, they relax the static nature of the network to allow an active networking approach that reshapes the queuing network at run-time. The authors do discuss specifics of implementation, though this article will only briefly touch on it.


Each network node/reaction vessel has its own scheduler. The scheduler computes the next occurrence time of each rule r \in R_{i} in its local node (this is equivalent to “serving” or processing a packet or set of packets for bimolecular reactions) according to the Law of Mass Action. It then will sort the events into a priority queue, wait until the first event occurs, then execute. The main difficulty for a scheduler is to dynamically react and reschedule events properly as packets are added to or drained from its queues. The authors note that an efficient mass action scheduler can be implemented that requires only O(\log(|\mathcal{R}|)) time to enqueue or dequeue packets. This is based on the Next Reaction Method[4] of Gibson and Bruck. 

Here we’ll recount an explicit example that illustrates the concept. If we return to Figure 1 reproduced below, we can walk through Meyer and Tschudin’s scheduler implementation.


There are two queues, X and Y. Reaction 1 (Server 1) is bimolecular: X+Y \rightarrow Z, so the server pulls packets from two queues to execute the service. Reaction 2 (Server 2) is unimolecular, pulling only from queue Y. If we assume the reaction constants k_{1} = 1000/(\text{packet}\cdot s) and k_{2} = 1000/\text{s}, that X begins with two packets in its queue, and Y begins with 3 packets in its queue, then the reaction rates \nu_{r}, r=1,2 are respectively \nu_{1} = k_{1}c_{X}c_{Y} = 1000\cdot2\cdot3 = 6000 and \nu_{2} = k_{2}c_{Y} = 1000\cdot 3 = 3000. The occurrence time is the reciprocal of the reaction rate, so the occurrence times \tau_{r} are respectively \tau_{1} = \frac{1}{6} ms and \tau_{2} = \frac{1}{3} ms. That means the first server executes its action first, extracting packets from both X and Y. 

Since the occurrence time of r_{2} is coupled with r_{1} (both servers pull from queue Y), the action of r_{1} requires a rescheduling of r_{2}. After r_{1} pulls a packet each from X and Y, there is 1 packet left in X and 2 in Y, which means we have to recalculate the rate \nu_{2} = 1000\cdot 2 = 2000. The occurrence time of r_{2} is at ms \frac{1}{3}, so its time of execution hasn’t arrived. But thanks for r_{1}‘s effect, we have to rescale and reschedule the occurrence time of r_{2}. This is done by the following:

\tau_{r,\text{new}}-\frac{\nu_{r,\text{new}}}{\nu_{r,\text{old}}}(\tau_{r,\text{old}}-t_{\text{now}}) + t_{\text{now}},

where (\tau_{r,\text{old}} -t_{\text{now}}) is the time remaining between the original execution time and the current time. The multiplier in front is a scaling effect. 

In this example, at t_{\text{now}} = 1/6 ms, r_{2} was supposed to go at time 1/3 ms, but will now be prolonged. 

A note here, I did the math for their specific example, and it seems off. I think the multiplier should be as I’ve written above. The authors wrote the reciprocal, which prolongs too far. I’ll work to contact the authors to verify this. 

There are other timed scheduling algorithms utilized in computer networking, such as Earliest Deadline First, which require tagging each packet with a timestamp. This scheduler does not require such an imposition. 

The Chemical Control Plane

Here, the authors describe what they term as a chemical control plane that is intended to avoid the messy necessity of sending packets through a complex queueing network in order to shape packet flow as desired. The control plane takes advantage of concepts in enzymatic chemical reactions in order to control flow. This is a different application than the flow networks discussed thus far (as I understand it).


Here the forwarding plane which executes actions is separated from the control plane which will shape the flow of packets in the forwarding plane. 

The chemical control plane will dynamically determine the service rates; the servers do not have them predefined. There are some number of FIFO queues n, one for each type of ingress packet flow and they are drained by one server each, representing a unimolecular reaction. In the control plane, each queue is represented by an input species X_{i} and product species X_{i}^{*}. The chemical reaction network lives abstractly in the control plane, which is designed by a traffic engineer and can look like any digraph or network he wishes. 

Here we note the difference here between the prior sections, which dealt with physical flows modeled by a chemical reaction network, and moving the chemical reaction network to an abstract control plane. The queues now are not necessarily physically linked together, but we can choose to couple them abstractly to shape traffic. 

When a packet physically enters one of the queues, the control plane injects one instance of the corresponding molecule species into the abstract network. The scheduler described previously is implemented and eventually an instance of the output species is generated. Once this happens, the corresponding server in the forwarding plane physically processes the packet and dequeues the next. The advantage here is that the abstract molecules in the control plane have no payload, so implementation of this model only requires storing an integer value for each species that keeps track of the number of packets in each queue. This allows analysis of behavior at the design phase. 

In the simplest case, a unimolecular reaction X \to X^{*} in the chemical control plane acts like a low-pass filter to the packet flow, smoothing bursts with high frequency components. If the differential equation \dot{x} = \lambda-kx that approximates a unimolecular reaction is converted to the frequency domain via the Laplace transform, the transfer function F(s) has a cut-off frequency at k, the reaction constant:

F(s) = \frac{\mu(s)}{\lambda(s)} = \frac{k}{s+k}

That is, higher-frequency flows will be attenuated, much like dark glasses do with sunlight. Applying this filter at an ingress point of a network leads to less chaotic traffic patterns, but with a cost of a delay \frac{1}{k} and memory to buffer the packets. Therefore, the mean queue length for this single queue will grow proportionally with the delay and flow rate. That is, \hat{x} = \frac{\lambda}{k}.

Another consideration of the LoMA queues described by Meyer and Tschudin that differs from the standard M/M/1 queuing models is that the service rate is ultimately unbounded (for infinite capacity queues/networks), since it is proportional to the queue length. This is undesirable to allow in a network, and thus the authors borrow from biological systems and design an abstract enzymatic reaction to limit the rate of packet flow.

In biological systems, enzymes bind to reactant molecules X, called substrates in order to prevent a particular molecule from reacting immediately. Some amount of enzyme molecules E exist, and they can either exist free-form or bound in a complex (EX). The more enzyme molecules in bound form, the slower the rate of transmission grows for an increasing arrival rate. At equilibrium, the influx and efflux of substrate-enzyme complex molecules are equal according to Kirchoff’s Law, so 

k_{w}c_{X}c_{E} = k_{s}c_{EX}

Take a look at Figure 8 above in the chemical control plane to see this action. The number of enzymes is constant, so c_{E} + c_{EX} = e_{0}, which yields the Michaelis-Menten equation, expressing the transmission rate \mu in terms of the queue length c_{X}.

\mu = \nu_{\max}\frac{c_{X}}{K_{M} + c_{X}},

which yields a hyperbolic saturation curve. \nu_{\max} = k_{s}e_{0}, and K_{M} = \frac{k_{s}}{k_{w}} and specifies the concentration of X at which half of \nu_{\max} is reached. 

When the queue length at queue X is high, the transmission rate converges to \nu_{\max}, and behaves like a normal unimolecular reaction when queue length is short. 

The authors also extend this model to handle dynamic changes to the topology of the queuing network, which means that instances of queues and flow relations can be generated “on the fly,” as it were. Tschudin[13] has created an executable string and multiset rewriting system called Fraglets that allow for the implementation and running of protocols based on the ideas put forth thus far. They describe in the paper how to implement explicitly the enzymatic rate-limiter in the chemical control plane in Figure 8. In this implementation, rather than flow interactions being static and determined at the design phase, each fraglet (packet) sorts itself into a queue. After a packet is serviced, the header of a fraglet is treated as code, allowing a packet to determine its route comparable to active networking. The relationship between the abstract model and execution layer remains, which allows a mathematical model of the behavior of a Fraglets implementation to be generated automatically, and a queuing network to be design and then realized easily in Fraglets language. 


The final part of this work will discuss an application of artificial packet chemistry to the implementation of a congestion control algorithm, briefly discuss design motifs, and conclude. 


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