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Application of Evolutionary Algorithms for Energy Efficient Grooming of Scheduled Sub-Wavelength Traffic Demands in Optical Networks. Ala Shaabana. University of Windsor School of Computer Science 13-05-2013. Optical Communication Overview Motivation Solution Outline
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Application of Evolutionary Algorithms for Energy Efficient Grooming of Scheduled Sub-Wavelength Traffic Demands in Optical Networks AlaShaabana University of Windsor School of Computer Science 13-05-2013
Optical Communication Overview Motivation Solution Outline Evolutionary Algorithms Related Research Energy Minimization for Scheduled Traffic Results Future work Outline
Communication at a distance to carry information using light. • In 1880, earliest electrical device created to perform optical communication is created. • Photophone • Optical communication today relies on optical fibers to carry the information from point A to point B. Optical Communication
Communication at a distance to carry information using light using optical fibers. Optical fibers: thin glass cylinders or filaments which carry signals in the form of light. A Lightpath (LP): end-to-end optical communication channel – may traverse multiple fibers. Optical Communication
An optical communication system uses: • Transmitter: encodes a message into an optical signal. • Channel: carries the signal to its destination • Receiver: reproduces the message from the received optical signal. Optical Communication A. Shaabana — M.Sc. Thesis Defense
Immense growth in high-bandwidth applications increase in energy consumption. • 1% improvement in total energy consumption $5M USD savings per year in electricity cost [1]. • Existing approaches: put network interfaces and components to sleep [2], switch off line cards [3][4], or even entire links or nodes[5][6]. • Our approach: consider the applications that require periodic use of bandwidth at predetermined times. • Unlike static or dynamic traffic demands, this type of demands (called scheduled traffic demands) is periodic and predictable • Resource allocation can be optimized in both space and time. Motivation
Integer Linear Programs (ILPs) are typically the go-to solution for this kind of optimization problem. However, although ILPs achieve an optimal solution, however they become computationally intractable once the problem set becomes larger (larger network sizes). It ends up taking too long and sometimes consuming too many computational resources to find its solution. Motivation
We present a Genetic Algorithm (GA) and a Memetic Algorithm (MA) to route a set of periodic, sub-wavelength traffic demands over the network. • The primary goal for these two approaches is to: • Route the traffic demands in such a way that the maximum number of LPs can be switched off, hence reducing the overall power consumption. • Reduce the total number of LPs needed to realize the logical topology, such that the capacity constraints of the LPs are not exceeded. Solution Outline
The GA has reduced the energy consumption more so than the simple shortest path holding-time-aware heuristic, which was presented in our previous work in [7]. • In contrast, the MA was designed to build upon the GA and improve on it further by adding local search capabilities,. • Solutions similar to the GA with considerably less time. Solution Outline
Note however, that evolutionary algorithms themselves have been applied to many computational problems. Computer simulations of evolution started as early as 1954 with the work of Nils AalBarricelli. In 1989, Moscato et al. proposed Memetic Algorithms based on Richard Dawkin’s notion of a Meme [8]. Solution Outline
At the time being, there are no applications of evolutionary algorithms to optimize scheduled demands in optical networks, and the current approach seems to be to use a holding-time unaware (HTU) approach. Solution Outline
“Evolution is the survival of the fittest” is a great description of many evolutionary computation systems. When one uses evolutionary computation to solve a problem, it operates on a population (or a collection) of data structures (or creatures/genes). This fundamental difference in the notion of fitness is a key difference between biological evolution and most evolutionary computation. A little Biology…
2 opposing forces that drive evolution: • Variation: process that produces new alleles/genes • Selection: process whereby some alleles survive and others do not A little Biology…
Evolutionary computing can accomplish variation by making random changes in these data structures and by blending parts of different data structures via mutation and crossover (referred to as variation operators). There are good and bad mutations operating on a population of data structures. A little Biology…
Nearly three decades of R&D have demonstrated that the mimicked search process of natural evolution can yield very robust and direct computer algorithms, even though these imitations are crude simplifications of biological reality. The result of these efforts is Evolutionary Algorithms (EAs). Evolutionary Algorithms
The population evolves towards improving regions of the search space by means of randomized processes of mutation, selection and recombination. The population is arbitrarily initialized. The environment delivers fitness information of the individuals, and the selection process favors those individuals of higher fitness to reproduce more often than worse individuals. The recombination mechanism allows for the mixing of parental information while passing it to their descendants, while mutation introduces innovation into the population. Evolutionary Algorithms
A number of recent works focus on reducing power consumption in today’s core/transport networks. • For example, Orgerie et al. (2012) developed an energy-efficient framework for Bulk Data transfers in dedicated networks with advance reservation. • Meanwhile, Musumeci et al. (2012) evaluated the power consumption of the various devices used at different network architectures. • Coiro et al. (2011) consider the dynamic traffic scenario, and propose an energy aware routing scheme to improve the energy efficiency by minimizing the number of active optical amplifiers in the network [5]. Related Research
Most common of Evolutionary Algorithms. • Generally, a population of candidate solutions to an optimization problem is evolved towards better solutions. • Iteratively, the evolution typically starts from a population of randomly generated individuals, with the population in each generation referred to as a generation. • The more fit individuals are stochastically selected from the current population • The new generation of candidate solutions is then used in the next iteration of the algorithm. Genetic Algorithms (GA)
While GAs have been inspired in trying to emulate biological evolution, Memetic Algorithms (MAs) try to mimic cultural evolution. Essentially, MAs are a marriage between population-based global search and the local search heuristic made by each of the individuals. Memetic Algorithms (MA)
Using the GA and the MA, we aim to minimize the energy consumption for traffic routing in a Wavelength Division Multiplexed (WDM) network. • Given a logical topology • Each logical edge is comprised of one or more lightpaths. • The number of lightpaths needed to implement a given logical edge is determined by: • 1. The number of lightpaths needed to implement on that edge. • 2. The capacity of a lightpath. Energy Minimization in Scheduled Traffic
We are also given a set of the scheduled sub-wavelength demands, with known start and end times. Each demand is represented by the tuple Energy Minimization in Scheduled Traffic
The goal is to route the traffic demands in such a way that the maximum number of lightpaths can be switched off at any given time, reducing the overall power consumption. We also try to implement each logical edge using as few lightpaths as possible. Energy Minimization in Scheduled Traffic
It is necessary to calculate the fitness value of each new individual after we generate it. We use the following fitness function: Fitness Function
The number of active lightpaths can vary, depending on the traffic in interval . • Hence, the number of transceivers used for implementing is determined by the maximum number of active lightpaths needed for in any given interval. • That is, Fitness Function
The selection of individuals from the initial population as parents is carried out using the Roulette-Wheel selection method. We have used k-point crossover for each crossover operation in order to produce new offspring from the selected parents. GA/MA Selection and Crossover
Single candidate chromosome is first selected randomly for mutation • Change a single allele of an individual, with a probability of this event happening. • For our simulation probability of mutation =0. 08 • Obtained by trial and error; does not converge the population too quickly, losing potentially better chromosomes, nor does it ruin the population’s diversity. Mutation
In order to simplify the way local search works, let us first take an example. Population of chromosomes (candidate solutions) Divided into neighborhoods of chromosomes. Local Search
When local search is implemented, it searches through the neighborhoods within population and chooses the most locally optimal chromosome from each neighborhood. That chromosome is then guaranteed to make it through to the next generation. The local search can be integrated within the evolutionary cycle mainly in two ways. Local Search
The first way is the application of the local search to a candidate solution, called lifetime learning. • We have implemented our method using the second way. • The application of the local search during the solution generation phase, that is, the generation of a perfect child. Local Search
Simulations were run with different demand sets on a number of well known networks such as the 14-node NSFNET and 20-node ARPANET. • For each network topology and size of demand set, the results reported in this section represent the average values of at least fiveruns. • We also experimented with different values of the constant for . • Changing the value of did not produce significant change in the results. Results
Although the GA is less computationally demanding than an ILP, an Amazon “Elastic Cloud” server with 8GB of RAM memory and 4 Amazon EC2 Compute Units (ECU) was required. • Where each ECU is equivalent to a 1.0 – 2.0GHz 2007 Opteron or Xeon processor). • In contrast, the MA experiments, although operating on the same data sets, required only a 2GB RAM memory server and utilizing 1 Amazon ECU. Results
Results • knowledge of demand holding times result in significant energy improvements over HTU approaches (26%-40%). • Additional improvements (8% - 13%) compared to the HTA shortest path heuristic in [8]. • MA we can reduce the amount of computational resources and time used while achieving similar results. A. Shaabana — M.Sc. Thesis Defense
The simulation results show that knowledge of demand holding times result in significant energy improvements over HTU approaches. The proposed GA leads to further improvements compared to the HTA shortest path heuristic we have proposed in [8]. Interestingly, they also demonstrate that using the MA we can reduce the amount of computational resources and time used while similar outputs to the solutions presented by the GA. Results
Specifically, knowledge of demand holding times significantly reduced energy consumption, with improvements between 26% and 40%, even using a simple shortest path routing approach. This reduction is achieved by simply switching offlightpaths when they are not carrying any traffic. Results
Tested for and an optical fiber bandwidth capacity of Gbit/s For each value of A in the figures, we took the average of five test cases, and plotted the improvement percentage over the value of A. GA vs. MA Results
