Amani Proposal Final

7/23/2019 Amani Proposal Final

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Artificial Bee Colony Algorithm For Optimizing EnterpriseNetwork Topology Design with Multiple Objectives

A proposal submitted for the Degree of Masters inInformation Technology

Amani D.SaadStudent I.D. No.20121061

SupervisorDr. Salman A.Khan

Co-SupervisorDr. Amjad Mahmood

9 May 2014



Contents

1 Statement of the problem 2

2 Background and History 2

3 Justification of the Problem 3

4 Literature Review 5

4.1 Network topology design using iterative algorithms . . . . . . . . . . . 54.2 Artificial Bee Colony Optimization . . . . . . . . . . . . . . . . . . . . 5

5 Theoretical Framework 6

5.1 Contributions of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . 8

6 Methodology 9

7 The Proposed Thesis Outline 9

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1 Statement of the problem

The purpose of this proposal is to investigate the applicability and effectiveness of Artificial Bee Colony (ABC) Algorithm for multi-objective enterprise network topologydesign optimization problem. The application domain of the proposed thesis will bedistributed local area networks with the focus on wired networks. Five design objectiveswill be considered, which are network reliability, network availability, Maximum linkutilization, network financial design cost, and network latency. The performance of the proposed algorithm will be evaluated through comprehensive empirical study andcomparison with another algorithm of the same algorithm class.

2 Background and History

Today, computer networks are vital in any modern corporation or establishment. Inbusiness world, a computer network is more than number of interconnected devices.Computer networks are the prime channels that enable gathering, analyzing, and shar-ing information. With a chaotic network layout, many institutes may suffer from theproblem of ”info-sclerosis” which can be defined as hardening and clogging of its infor-mation arteries [1].

In technical terms, a computer network consists of a number of devices that are con-nected to each other via a communication channel. These devices are mainly dividedinto two types: end-users, which consist of computers, printers, and other peripheraldevices, and network elements such as routers, switches, and hubs. End-users are con-nected to network elements through the communication channel. Computer networksare usually designed hierarchically, in which a small network (referred to as a node

herein) is connected to a larger one to form a multiple-tier, massive network. Thiscan result in huge number of possible layouts, whereas each such layout is termed asnetwork topology . For the purpose of this thesis, a tree topology is considered. In atree topology, each node is connected to other node via a unique path without redun-dancy, which indicates that a single path exists between two communicating node. Theobjective then is to find the best grid layout for a tree topology.

It is an established fact that the topology design of a network significantly affectsthe entire network performance [2]. Designing an optimum topology is a major and

complex task since for n number of connected nodes, there will be 2n(n−1)

2 −1 distinct

topologies [3]. Hence, for a network consisting of only ten nodes, there will be 244

distinctive topologies. A typical real network comprises tens of interconnected nodes,which discordantly increases the number of possible topologies. It is, therefore, acomputationally complex task to find the best topology. This complexity is furthermagnified by the presence of various design constraints. Based on these facts, networktopology design has been classified as an NP-hard problem [4]. The performance of a given topology is usually measured according to a number of predefined criteriasuch as availability, utilization, reliability, cost, delay, hop count, throughput, etc.Although these parameters are closely related to each other, topology optimizationoften compromises between the various design criteria. For example, management of

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an organization might be concerned mostly about the financial cost, while for the user,network delay might be a more important issue. Likewise, a network administrator’smajor concerns might be reliability and availability along with link utilization.

Due to its sheer complexity, the network topology design, with its numerous vari-ants, has been traditionally solved with artificial intelligence techniques. More specif-

ically, nature-inspired and other metaheuristics have been extensively used for thispurpose. Popular algorithms used in the domain include genetic algorithms, particleswarm optimization, ant colony optimization algorithms, simulated annealing, tabusearch, among many others. Further details are provided in the following sections.

3 Justification of the Problem

The solution to info-sclerosis is to establish an efficient and cost effective disseminationchannel. This requires the optimum design of a network, and network topology playsthe main role in this process, with consideration of multiple design objectives. Thus,

the network topology design problem can be viewed as a multi-objective optimizationproblem.

There are two dimensions of the above problem. First is the multi-objectivity andsecond is the optimization. The optimum design is governed by both issues. In theproposed topology design problem, there are many design objectives which need tobe optimized. However, optimizing multiple objectives simultaneously is a complextask due to the conflicting nature of these objectives. That is, if one tries to optimizeone objective, then another one is negatively affected. For example, if financial cost isreduced, then it would result a less reliable network. In this situation, a compromiseor trade-off is required between the objectives that would find the optimum balance

between the design objectives. Many techniques, such as weighted sum method, fuzzylogic, goal attainment, lexicographic ordering, and many others, have been proposedover the years to handle the multi-objective aspects of an optimization problem. Goalprogramming (GP) is one of the famous approaches, proposed by Charnes, Cooper andFerguson in 1955 [5]. As a multi-objective optimization approach, goal programmingwas defined to address linear models and has proven to be efficient and effective ap-proach to address various industrial optimization problems. The approach is to set atypical value for each objective parameter to be considered as a targeted goal. Then,the aim is to minimize the absolute deviations of all objective parameters simultane-ously. This is achieved by minimizing the sum of the absolute values of the differencesbetween target values and actually achieved values [6].

The second dimension of the multi-objective network topology design problem isthe algorithm that performs the optimization process. Many optimization algorithmshave been proposed over the last fifty years. Some of these are constructive and othersare iterative . Although constructive algorithms are fast in terms of giving a finalsolution, but they do not perform well in situations where multiple objectives arepresent, coupled with design constraints. In many cases, constructive algorithms failto even give a feasible solution. In such situations, researchers resort to iterativealgorithms which possess the capability to find optimum or near-optimum solutions

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in a very constrained search space. Attempts have been made to efficiently solvethe variants of network topology design problem using iterative algorithms such asgenetic algorithms, particle swarm optimization, simulated annealing, tabu search, antcolony optimization, and simulated evolution. However, there are still a list of recentoptimization algorithms whose effectiveness levels have yet to be explored, specially in

the context of multi-objective network topology design. One such algorithm is artificialbee colony optimization algorithm which has been applied to a number of optimizationproblems, but is yet to be applied to network topology design problem consideredherein.

The artificial bee colony (ABC) algorithm is a swarm based metaheuristic [7]. In-spired by the artificial behavior of honey bees, the algorithm was first proposed as aglobal optimization algorithm to solve continuous numerical problems [8, 9]. In orderto be simulated as a distributed problem solving system, the algorithm has been mod-ularized in such a way that the basic model of this algorithm would be consisting of two components [7–11]. First, agents (employed and unemployed bees) searching for

the best problem solutions, and the food sources which serve as the second compo-nent of this algorithm. Agents are bees recruited by the mother bee hive, and can befunctionally classified into two major groups: employed agents and unemployed agents.Employed agents are recruited to utilize a specific food source. Unemployed agents areextendedly classified into two groups, unemployed on-lookers that are waiting in thehive, with their duty being summarized as information sharing channels, and unem-ployed scouts which are searching globally around the hive for new food sources. Thesecond component, the food source, represents a possible solution for the problem beingconsidered. The amount and/or richness of nectar in that source implicitly expressesthe feasibility and quality. With high richness being preferable, a fitness function canbe defined to filter the proposed paths for new food sources in such a way that the

ones with the lower values would be neglected. However, a food source is also affectedby its path availability and ease of extraction [8]. The process of exploitation and/orexploration of local and global food sources respectively are governed by the decisionof the colony [12].

Apart from the fact that ABC has not been applied to the network topology designproblem as yet, another strong reason to consider ABC for the proposed work is the factthat the algorithm completely fulfils the requirement of being a strong swarm-basedoptimization algorithm. Generally, two viewpoints exist about being a strong swarm-based algorithm (and for that sake, any iterative optimization algorithm in general).One viewpoint is by Millonas [13] who defined five principles of proximity, quality,

diversity, stability, and adaptability that should be proven for any swarm to maintainan intelligent behavior. Although the ABC algorithm showed an explicit adherenceto these principles, it was only recently applied to real world problems [10]. Thesecond viewpoint is by Bonabeau, who interpreted the two concepts of self- organization and division of labor as a fundamental key features for any algorithm to obtain theintelligent swarm behavior [14, 15], and ABC elaborates on these features to obtainswarm intelligence [8,10,11].

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4 Literature Review

Since the proposed work focuses on the enterprise network (a type of local area net-work) topology design using iterative algorithms, as well as the bee colony optimizationalgorithm, the literature review encompasses both aspects.

4.1 Network topology design using iterative algorithms

Network topology design problem has received much attention through years. Re-searchers and network engineers extensively tried to define the best possible represen-tation of a network topology. As a complex optimization problem, network topologyoptimization has been classified as an NP-hard problem. Several approaches have beenproposed to address this problem. For example, use of genetic algorithm has beenreported in many studies focussing on local area network topology design [16–24]. Sim-ulated annealing has also been used extensively in the network design domain [25–32].

Other than this, there are little applications of various other iterative algorithms such asant colony optimization, particle swarm optimization, simulated evolution, stochasticevolution etc. to the local area network topology design problem [33–37]. No appli-cation of ABC has been reported in literature as far as network topology design isconcerned.

4.2 Artificial Bee Colony Optimization

Karaboga 2005 was the first who proposed the Artificial Bee Colony algorithm, simulat-ing original natural behavior of honey bees colony system. The algorithm was deployedto optimize continuous numerical problems [8]. The algorithm was earlier modelled by

Tereshko and Loengarov in which a system was developed by considering the foragingbehavior of bees for environmental information gathering and adjusting behaviors ac-cordingly [12]. Gaining the promising results the algorithm has received an increasingattention from researchers all over the world. Basturk and Karaboga published severalresearch papers on ABC [10,11,38]. According to [11] published in 2012, investigationfrom analytical perspective showed that the studies performed on ABC algorithm werecategorized into three main directions. The first direction focused on performancecomparison of ABC with other well-known evolutionary algorithms such as Geneticalgorithms, particle swarm optimization, and ant colony optimization [39–41]. Succes-sive and intensive comparative studies were followed revealing the efficient applicationof this algorithm to address almost all the addressed problem domains [42]. The sec-ond aspect focused on hybridization and modified versions of the algorithm [43, 44].The third dimension focussed on applications in various disciplines such as in neuralnetworks, industrial engineering, electrical engineering, mechanical engineering, elec-tronic engineering, civil engineering image processing, software engineering, controlEngineering, data mining, and sensor networks [11,45].

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5 Theoretical Framework

In the work proposed herein, artificial bee colony algorithm will be engineered to solvethe network topology design problem. Moreover, a new formulation of the networktopology design problem will be done. This new aspect mainly comes due to the opti-mization objectives being considered in the optimization process. These optimizationobjectives are network availability, maximum link utilization, network reliability, fi-nancial cost, and average network delay per packet. These design objectives are brieflydiscussed below.

Reliability: The goal is to maximize reliability. Reliability can be defined as theprobability of proper delivery of the transmitted data with respect to the expectedloss in case of any occasional occurrences of network failure. Severe problems canbe reported when network reliability is concerned [46]. For a tree topology, networkreliability can be calculated as the product of reliabilities of all individual links presentin the topology, as shown in the following equation [3] .

Rs =L

i=1

Ri (1)

Where, Rs is the reliability of the entire network, Ri is the reliability of link i in theNetwork.

Utilization: The aim is to maximize link utilization to maintain a reasonablyacceptable performance level. Link utilization can be defined as the ratio of the currentlink traffic to the maximum traffic that the same link can handle. While high linkutilization indicates that the link is extremely busy, low utilization indicates that the

link is under-utilized. However, higher link utilization often compromises latency. Thatis, under normal conditions, exceeding a threshold value can cause severe congestionlevels [47]. Several approaches have been proposed in literature [48–50] to estimatethe upper bound of link utilization. One such approach is by Igai and Oki [50], whosework aimed to balance both utility and congestion. The following equation is used tocalculate link utilization:

U link = Dbit

BW ∗ T % (2)

Where Dbit is data per bits, BW is bandwidth and T is the time interval.

Availability: Availability is a key characteristic of any system design. It equally

becomes a crucial and complex issue for a network topology design problem. Note thata typical tree network topology consists of links and nodes, in which each node has anaccess to every other node via a unique path. Even a small fault in a cable or a networkdevice can threat the availability of entire network. For example, if the availability of anetwork per day unit was 99.993 %, it indicates the expected unavailability to be 1.02minutes per day. Thus, the accumulated annual unavailability would be approximately386 minutes per year. For network involving sensitive applications and huge datahandling (such as in stock exchanges or space mission program), this may lead todisastrous results [51].

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Commercially, a network service provide must ensure provision of maximum avail-ability, since it may serve as a critical factor in attracting customers. Therefore, theusual accepted value of availability in the industry is very high, which is precisely99.999 % (also known as 5 nines), as adopted for networks in military sectors [52]. Asan optimization problem the objective is to maximize availability. However, for ease of

calculation, the unavailability is considered as an objective to be minimized [53]. Theavailability of a connection can be defined by the following equations [54]:

A = 1 − U W .U P (3)

U W

n

i=1

U i,W (4)

U P

m

j=1

U j,P (5)

where U W and U P are the Unavailability of the ith element in path P respectively.

Financial Cost: The goal is to find the topology with minimum cost. In order tocalculate the cost of the topology design, the cost of all cable links should be consideredalong with the cost per unit of the used cable. For this purpose the following equationis used [3]:

Cost = Length× C cable. (6)

where C is the link cost per unit.

Network Delay: The objective is to find the network topology with lowest networkdelay (latency). To calculate the average network delay of the topology, both linkdelay and device delay should be taken into account. The following equation is usedto calculate the delay time per network topology [3].

Dtotal = Dlink + Ddevice (7)

D = 1

γ

L

i=1

λi

λmax,i − λi+

1

γ

d

i=1

γ ij

Bij (8)

Where, D is the total number of networking devices in the network . L is thenumber of links in the topology. Λ is traffic on link i in bits per second (bps), andλmax,i is the capacity of link i in bps. γ is the total traffic in the network.

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5.1 Contributions of the thesis

Considering the various issues discussed earlier in this proposal, following will be themajor contributions of the thesis:

1. Formulation of multi-objective enterprise network topology design prob-lem. This will involve proposing a new variant of the network topology designproblem. Previous studies on the work [3, 55] considered four optimization ob-

jectives, namely, network financial cost, network latency, maximum hop countbetween two nodes, and network reliability. The novel aspect of the proposedwork will be the formulation of a new multi-objective problem considering fiveoptimization objectives. These are network financial cost, network latency, net-work reliability, maximum link utilization, and network availability.

2. Employing goal programming as the multi-objective optimization strat-

egy for the underlying problem: The multi-objective aspect of the proposed

ABC will be handled by incorporating the goal programming approach for fitnessevaluation of a solution. More specifically, this will require aggregating the afore-mentioned five optimization objectives into a single optimization function via thegoal programming approach. This will be the first such attempt with regard tothe application of the goal programming approach to the ABC algorithm in anydomain.

3. Engineering the artificial bee colony optimization algorithm for multi-

objective enterprise network topology design: This will include tailoringthe general ABC algorithm to become a multi-objective objective optimizationalgorithm considering the five objectives mentioned in point (1) while using the

goal programming approach in point (2). This will be the first such attempt withregard to the application of the ABC algorithm to the problem being consideredin this proposal.

4. Propose an adaptive artificial bee colony optimization algorithm: Thiswill be an enhancement of the proposed ABC mentioned in point (3). The adap-tive variant will attempt to dynamically adjust the algorithm parameters [56] byextracting the problem specific information from the execution run. It will be afirst such attempt of its kind in the domain of ABCO in general, as well as forthe problem being studied.

5. Propose a goal programming based ant colony optimization algorithm:This will be a modification of the ant colony optimization algorithm (ACO)proposed in [3] which used fuzzy logic for multi-objective optimization, whileconsidering financial cost, network latency, maximum number of hops betweena source destination pair, and network reliability as the optimization objectives.The modification will involve employing goal programming, instead of fuzzy logic,for multi-objective optimization. The modified ACO will also consider the fiveobjectives mentioned in point (1) above in the optimization process.

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6. Empirical analysis and mutual comparisons of ABC algorithms: Anempirical analysis of the proposed goal programming based multi-objective ABCalgorithms mentioned above will be done and the proposed algorithms will bemutually compared.

7. Comparison of the proposed ABC algorithms with the modified ACOalgorithm: In order to ascertain the effectiveness and efficiency of the proposedABC algorithms, a comparison will be done with the modified ACO algorithmmentioned in point (5) above. ACO has been specifically chosen for this com-parison since it is also a population-based swarm intelligence algorithm similarto ABC. Another motivation to choose ACO is that a multi-objective ACO hasbeen applied to a variant of the network topology design problem [3] and exhib-ited the best performance when compared with many other iterative optimizationalgorithms.

6 Methodology

The proposed multi-objective artificial bee colony optimization algorithm will be de-veloped for the enterprise network topology design problem considering the five designobjectives and possible constraints. A code in C++ will be developed to accomplishthis. Five test cases used in many previous studies will be used for experimentation andevaluation, and experiments with different algorithm parameters will be conducted. Asper the standard practice in the domain, at least 30 independent runs will be performedfor each of the objectives listed in Section 5.1, and average fitness of the solutions willbe recorded. Furthermore, results will be validated statistically using the Wilcoxonranked-sum test which is a popular test used for such valdiations. ‘Statistica’ packagewill be used for statistical analysis.

7 The Proposed Thesis Outline

1. Introduction.

2. Historical overview and literature review.

3. Preliminaries

4. Goal-programming based artificial bee colony algorithm for enterprise networktopology design.

5. Experiments and results

6. Conclusion.

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