A Hybrid Imperialist Competitive-Gravitational ... - Semantic Scholar

A Hybrid Imperialist Competitive-Gravitational Attraction Search Algorithm to Optimize Cloud Service Composition Amin Jula1, Zalinda Othman2, Elankovan Sundararajan3(IEEE member) 1, 2 Data Mining and Optimization Research Group, Centre for Artificial Intelligence, 3 Industrial Computing Programme, School of Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, 43600 Selangor. [email protected], {zalinda, elan}@ftsm.ukm.my

run the required services on their local infrastructures and resources, which could require spending immense amounts of money and time. One of the largest challenges that cloud providers face is taking advantage of different services that are located on different servers in different geographical locations with respect to QoS attributes. These challenges include preparing appropriate services for the customers, reaching high reliability services and executing the required services in less time and with a reasonable fiscal cost. This challenge led us to introduce the idea of composing services on clouds. Service Composition (SC) results from the need to consider a large number of uniform and non-uniform services that are located on a large number of servers. To select appropriate services in a suitable amount of time, SC is considered as an optimization problem. To optimize user requests, Simone A. Ludwig extracted a model for web service composition that was based on a workflow database that contained all of the possible abstract workflows for the specific requests [1]. He attempted to optimize several workflow requests at the same time and assumed that the number of available services exceeded the requests. In 2011, a framework for self-organizing service composition based on an agent-based system was presented by J. Octavio Gutierrez-Garcia and Kwang-Mong Sim [2]. Here, each cloud service together with a cloud server are introduced as an agent. They applied three new innovations, namely 1) designing a distributed framework for cloud service composition, 2) applying agent-based techniques for cloud computing and 3) designing and implementing self-organizing agent protocols in an environment for agent-based test and for guiding experiments to evaluate the efficiency and selforganizing features of the agent-based solution for services in the cloud. Wenbin Wang et al. proposed an improved particle swarm optimization algorithm to solve the Web Service Selection problem with respect to QoS [3]. They attempted to improve PSO by applying a mutation-like operator called Non-Uniform to the general best particle for increasing the diversity of the

Abstract—Service composition is among the most important challenges that cloud providers have ever faced. Optimization of QoS attributes when composing simple atomic services to obtain a complex service can be considered to be an NP-hard problem, which could be solved properly by using Hybrid optimization algorithms. In this research, the hybridization of an improved Gravitational Attraction Search (as a local search algorithm) with an Imperialist Competitive Algorithm has led us to introduce and apply a new memetic algorithm for gaining optimal or near optimal response time and execution fees simultaneously, for cloud computing service composition. Using a roulette wheel selection algorithm to make well-advised and nonblind decisions to choose the number of countries in each empire that should be selected to apply a local search to has assisted the hybrid algorithm at achieving better solutions. Introducing a new equation to calculate the QoS eligibility of the solutions that were generated based on the normalization of the response time and execution fee has also led us to compute the results fairly and in a scientifically based manner. Keywords—cloud computing; service composition; imperialist competitive search; gravitational attraction search; QoS attributes

I.

INTRODUCTION

With the increasing complexity in the services that are required by users, the implementation and performance cost of the services has also increased. It is unavoidable to use more servers with higher capabilities to execute a modern systems service, which leads to a significant increase in the performance costs. Under such circumstances, the idea of using Cloud Computing (CC) can be considered to be a helpful solution. As a simple and comprehensive definition, Cloud is a set of software and hardware computing resources that are introduced and placed at the users’ disposal through a network, such as the internet, so that it is possible for cloud customers to request their required services to cloud brokers and receive the results in a reasonable time with a low cost. When using a cloud, it is not essential for all of the endusers to provide all of the necessary computing equipment to

c 978-1-4673-5891-0/13/$31.00 2013 IEEE

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population, with the goal of overcoming the impertinence of PSO. In 2012, Adrian Klein et al. proposed a new approach for cloud computing service composition with respect to network parameters [4]. Their approach basis had three new features: 1) extracting a general model for a network that can be filled in a scalable way while using state-of-the-art algorithms from the community of network research, 2) determining a QoS model that allows the calculation of the QoS of a network, such as one that transfers rates and latencies, and 3) using a genetic algorithm for presenting a selection algorithm. Philipp Leitner et al. institutionalized finding the optimal set of adaptation problems that minimizes the total costs caused by service-level agreement (SLA) violations and the conformities for preventing them in 2011 [5]. They discussed three different techniques to solve the above-mentioned problem. They selected the branch-and-bound algorithm (a deterministic algorithm), the genetic algorithm (a famous combinatorial algorithm) and a new local search for solving the problem. Kevin Kofler et al. presented a technique in 2011 [6] for planning the requirements of a customer by using functional and non-functional fields for the services. They also introduced a measure to ensure the satisfaction of the users and made a parallelizable service composition algorithm to optimize the measure. They finally proposed a heuristic approach on the basis of service composition historical information to rapidly react to changes in customer requirements at the design time and to indicate run-time alternatives for service failures. In this paper, a new memetic algorithm is proposed on the basis of the hybridization of Imperialist Competitive Search (ICS) as a social-based heuristic search algorithm and a Gravitational Attraction Search (GAS) that serves the local search, to gain the best solution for an SC problem in CC in less time. Additionally, a roulette wheel selection algorithm is applied to calculate the number of countries of each empire that should be selected for receiving a local search. For scientific-based calculations of the objective function of the problem, a new equation is introduced based on the response time and the execution fee normalization. The structure of this paper is organized as follows. After the introduction, the Cloud Computing Service Composition problem will be defined and described in part II. Imperialist Competitive Search and Gravitational Attraction Search algorithms will be explained in parts III and IV, respectively. In part V, the Imperialist Competitive-Gravitational Attraction Search Algorithm (ICGAS) will be introduced and discussed, and its experimental results are given in part VI. The last sections of the paper contain the conclusions and references. II.

CLOUD COMPUTING SERVICE COMPOSITION PROBLEM

Fast developments in the utilization of cloud computing lead to publishing more cloud services on the worldwide service pool. Because of the presence of complex and diverse services, a single simple service cannot satisfy the existing functional requirements for many real-world cases. To

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complete a complex service, it is essential to have a batch of atomic simple services that work with each other. Therefore, there is a strong need to embed a Service Composition (SC) system in cloud computing. On the other hand, increasing the number of available services causes an increase in the number of similar operating services on different servers. These similar services are located in different places and have distinct values in terms of QoS parameters. For this reason, it is unavoidable for SC to apply appropriate techniques to select an atomic service among the different similar services that are located on distinct servers, to allow the highest quality of service to be achieved, in accordance with end-user requirements and priorities. Because of intrinsic changes in cloud environments, available services and end user requirements, it is important for SC to be designed dynamically, with automated function capabilities. Therefore, selecting appropriate and optimal simple services to be combined together to provide composite complex services is one of the most important problems in service composition. The Service Composition problem in cloud computing can be defined as how to choose atomic simple services such that the obtained complex composite service satisfies both the functional and QoS requirements based on the end-user requirements. In this paper it is assumed that every Composed Service (CS) in cloud is consisted of n Unique Services (US) that any of them has p QoS parameters. For terminating a CS, combinatory unique services act respectively in a ordinal workflow (wf). Let define qi (US j ) the value of QoS parameter i for unique service j. Accordingly, the QoS vector of unique service j is defined as (1):

q(US j ) = (q1 (US j ), q2 (US j ), ..., q p (US j ))

(1)

On the other hand, if wf k is the workflow of CS k , it is possible to define Qi ( wf k ) to show the value of QoS parameter i for workflow k. Whereupon, the QoS vector of workflow k is indicated as (2):

Q(wf k ) = (Q1 (wf k ), Q2 (wf k ), ..., Q p (wf k )) III.

(2)

IMPERIALIST COMPETITIVE SEARCH ALGORITHM

Unlike other evolutionary algorithms, which are inspired by natural behaviors or events, the Imperialist Competitive Search Algorithm (ICS) is a new evolutionary algorithm in the Evolutionary Computation field; this approach is based on the sociopolitical evolution of humans [7, 8]. The algorithm begins with a preliminary randomly generated population for which the individuals are called countries. A few of the best countries are selected to be imperialists, and the other countries constitute imperialist colonies. If there is an optimization problem with n dimensions, then a country is formed as a 1 × n array, as in (3).

2013 IEEE Workshop on Memetic Computing (MC)

Country = [ p1 , p2 , ..., pn ]

(3)

The power of country i is calculated using the objective function f, which is a function of the variables ( p1 , p2 , ..., pn ) ; then, we have the following: Power (Countryi ) = f (Countryi ) = f ( pi1 , pi 2 , ..., pin )

(4)

ICS begins initially with m countries, and the nimp most powerful of them are selected as the imperialists. Other countries are known as imperialist colonies, and each nonimperialist country belongs to an empire. To distribute nonimperialist countries among imperialists, the procedure is simple. For each non-imperialist country, an imperialist country is selected randomly, and the non-imperialist country is allocated to the randomly selected imperialist country. In the process of algorithm execution, each imperialist country absorbs its colony countries toward itself based on the total power of the empire and on the colonies’ power. The total power of each imperialist is determined by the power of the empire power added to a coefficient multiplied to obtain an average of the imperialist colonies’ power. The total power of each imperialist country is calculated using (5), as follows: TC n = cn + (α × Average{ power (colonies of empiren )})

(5)

where TC n is the total power of the nth empire, and α is a positive number less than 1. In the country movement process, a colony moves toward its imperialist country a length of x units. The direction of movement can be considered to be a vector from the country to its related imperialist, as shown in Fig. 1, wherein d is the distance between the imperialist country and the colony, and x is a random value that can be obtained using a uniform distribution, as described in (6). x ≈ U (0, β × d )

a unique solution for the problem and holds a mass that could be calculated using an objective function. Whenever the quality of a solution is higher, its mass would be more. After the formation of a search space, its rules should be realized to manage it with respect to the reality that motion and gravitational laws alone can govern it. Gravitation law: Each particle in a space can attract other particles toward itself using a force. The amount of this force is proportional to the particle’s gravitational attribute and is inversely proportional to the distance between the two particles. Laws of Motion: the real velocity of every particle is equal to the sum of a percent of the particle’s previous velocity and its acceleration. Acceleration is equal to the imposed force on the particle divided by its gravitational mass. A. Gravitational Attraction Search The search space is considered to be a set of m particles that are scattered in the n-dimensional search space of the problem. Each particle’s position in the search space is a point that is accounted for as a possible solution to the problem. The position that is gained is given in (7), in which the position of particle i in dimension d is shown by x di .

(6)

where β is greater than 1 and is near to 2. There is a very important operator in ICS, which is called Imperialistic Competition. This operator is a function such that the power and colonies of the weakest empire will be decreased. This process continues until the weakest empire is destroyed. The imperialistic competition operator can be implemented in different forms and is based on the algorithm’s design requirements. IV.

Figure 1 Moving colonies toward their relevant imperialist

GRAVITATIONAL ATTRACTION SEARCH, MAIN IDEA

The problem search space is considered to be a space with many points or particles in n dimensions, based on the type of problem and its parameters and such that each point constitutes

X i = ( x1i ,..., xid ,..., xin )

(7)

At time t, different forces are imposed from particle i toward each particle j in the direction of dimension d, in the amount of Fijd (t ) . The magnitude of the force is obtained using (8), where İ is a small number, Rij (t ) is the distance between particles i and j at time t, and G(t ) is the gravitation constant at the same time. As shown in (9), the Euclidean distance is used to apply distances between particles. Fijd (t ) =

G (t ) × M i (t ) × M j (t ) Rij (t ) + ε

( x dj (t ) − xid (t ))

Rij (t ) = X i (t ), X j (t )


2

(8) (9)

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The exerted force on particle i in time t in the direction of dimension d, Fi d (t ) , is equal to the sum of all of the forces imposed on this particle by other particles in the system, as shown in (10),

Fid (t ) =

m

d j ij (t )

¦r F

(10)

j =1, j ≠ i

where r j is a random number in (0, 1] and has been used to maintain the random characteristics of the search process. Based on Newton's second law, each particle gains acceleration in the direction of dimension d, which is proportional to the burdened force on the particle in the same dimension divided by the particle’s gravitational mass. The acceleration of particle i in dimension d and time t is given by aid (t ) and is obtained using (11).

aid (t ) =

Fi d (t ) M i (t )

(11)

Equation (12) shows that the velocity of particle i can be obtained from the sum of a percent of the current velocity of the particle and its acceleration. The new position of particle i in dimension d is equal to the sum of its current position and its velocity, as calculated by (13). Vi d (t + 1) = ri × Vi d (t ) + aid (t )

(12)

xid (t + 1) = xid (t ) + Vi d (t + 1)

(13)

5825 prepared simple services, which were provided by Zibin Zheng et al. in their produced dataset WSDream-QoSDataset2, according to Real-world QoS evaluation results [9, 10]. The problem search space involves n dimensions. These required simple services can be selected from a variety of 339 servers and should be executed in a non-parallel mode to achieve appropriate results, similar to what the complex service should obtain. First, a set of countries are generated by a random assignment of servers to the Required Services in the first step of the algorithm. Every country specifies a preliminary solution. Calculating the Power of each country, which represents the eligibility of the solution, should be accomplished immediately after the country is generated. For this purpose, first it is required for the simple service responsetimes to be read from a dataset, and their sum is calculated and multiplied by the related applicant specified weight. On the other hand, the sum of the execution fee of each service on the selected server is calculated and is multiplied by its related weight, which is similar to the other sum. Currently, there are two values that have different measures. One value is based on a time unit (e.g., milliseconds), and the other value is based on the currency. Hence, adding them together to obtain an eligibility value is incorrect from a scientific point of view. To obtain an appropriate value, it is essential to use a normalized responsetime and execution fee instead of the original values. In terms of statistics, it is possible to calculate the normalized value of each value in a set of values by using (15). SV ( xi ) =

where r j are applied to maintain the characteristics of a random search. To specify the gravitational constant, (14) is used. It is obvious that the effect of the gravitational constant tends to increase exponentially. G (t ) = β

V.

−α

1 T

(14)

HYBRID IMPERIALIST COMPETITIVE-GRAVITATIONAL ATTRACTION SEARCH ALGORITHM (ICGAS)

xi − x SD

where SV(xi ) is the Standard Value of xi , x is the average of all of the values of the set, and SD shows the Standard Deviation of the set. It is worth mentioning that, in this research, all of the standard values should be increased to the same extent to avoid negative values. On this basis, (16) is introduced to calculate the eligibility of the solutions.

EligSol (i) = (( RTW + ε ) × n

In this section, to obtain an optimal solution for a cloud computing service composition, the proposed memetic algorithm will be introduced. In the beginning, the algorithm receives two weights from complex service applicants, for the Response-time and the Execution fee, such that the sum of those two weights is 1. The applicant determines the importance of each of the two parameters, using these weights. Suppose that the required Complex Service will be completed by a composition of n different simple services from

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(15)

¦

n

SVRT ( j )) + (CW + ε ) ×

j =1

¦ SVC ( j))

(16)

j =1

where RTW and CW are response-time and execution fee weights, and SVRT and SVC are standard values of the response-time and execution fee, respectively. Because it is possible for an applicant to specify 0 for each of the weights, adding ε to RTW and CW helps the algorithm to avoid neglecting parameter with weight 0 in its search, and to select the better solution among two or more solutions with the same values in one specified weight parameter.


It should be notified that because the algorithm has to find the solution with the minimum value of response-time and cost, and ICA and GAS act based on the maximum value of power and mass, the obtained value of (16) for each solution, should be subtracted from a constant value to reach the maximum while the QoS values have minimum values. Obviously, the original value of eligibility will be saved and considered. Countries should be sorted in descending order based on their power in the second step. After that, the first ImpNo countries will be selected as Imperialists, and the other countries will be divided among imperialists randomly. Thus, if the number of countries and Imperialists are CountryNo and ImpNo, respectively, then the number of Colonial countries that belong to each imperialist on average will be as mentioned in (17).

Colonial ( IMPi ) =

CountryNo − Im pNo Im pNo

(17)

The third step is to move countries that are located in each empire toward their imperialist. To move each country, it is necessary to calculate its distance to the imperialist in every dimension. Then, a random number is generated between 1 and the distance. The value of the country in that dimension increases or decreases according to the generated number, allowing the country to move toward its imperialist. At the end of this step and as the forth step, the power of all of the countries is calculated, and if there is a country that has a higher power than its imperialist, it becomes imperialist, and the former imperialist changes to a colonial country. At this time, the fifth step starts, and imperialists enter into an imperialistic competition to attract the weakest country of the weakest empire, to empower their empire’s power and to maintain their empire while facing the others. To accomplish these goals, the algorithm applies a competition probability that is equal to 0.1, which means that attracting a country from the weakest empire can be simply executed, in one tenth of the iterations. It helps all of the empires to have the chance to empower their countries and consequently empower an empire’s power to have the largest amount of power for facing the others. After this competition, if there is not any other country in the weakest empire, then the imperialist itself is attracted by another imperialist in an imperialistic competition. Applying a local search is performed in the sixth step. A number of countries from each empire should be selected for a local search, and their powers are very important for enhancing the local search performance. To gain the appropriate results and to maintain the random approach of the algorithm while avoiding blind actions, the Roulette Wheel Selection algorithm (as described in [11]) is selected to generate the number of countries to be selected in each empire. On the other hand, the number of countries that the local search should be imposed on

is directly related to the empire’s power. To reach this goal, a simple equation is proposed, as in (18), which calculates the percentage of selected countries for each empire. SelectedCo untriesPer cent (i ) =

EmpirePowe r (i ) SumEmpires Power

(18)

where EmpirePower(i) is the sum of the powers of all of the countries that are located in empire i and SumEmpiresPower is the sum of all EmpirePowers. As mentioned before, the Gravitational Attraction Search Algorithm (GAS) is selected as a local search algorithm to make a hybrid with the Imperialist Competitive Algorithm. To obtain better results in an appropriate amount of time, it is important to use a sufficient number of particles in GAS. Based on extensive experiments, the number of particles in GAS should be equal to 20% of the number of all of the countries in ICA, including the imperialist countries. ICGAS Pseudocode Step 1:

Initialization of first generation of countries Calculation eligibility of countries based on normalized value of Response-time and execution fee with respect to applicant specified

Step 2:

Sorting countries in descending order based on their power Selecting Imperialists and dividing other countries among them

Step 3:

Moving countries that are located in each empire toward their imperialist

Step 4:

Recalculation eligibility of moved countries based on normalized value of Response-time and execution fee with respect to applicant specified weight Replacement each imperialist with the most powerful country in the empire

Step 5:

Entering imperialists to imperialistic competition in one tenth of the iterations to attract the weakest country of the weakest empire Using roulette wheel selection algorithm to generate the number of countries to be selected in each empire for doing local search

Step 6:

GAS execution on previous phase-selected countries and replacing each imperialist with the best found solution for its empire Sorting imperialists based on their power in descending order Saving the best found imperialist and then going to step 3 Figure 2 the Pseudocode of ICGAS


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Therefore, the number of particles must be generated for each country, which is selected by a local search to be 20% of the number of all of the countries, including the imperialists. It should be noted that the concepts of particle and mass in GAS are the same as the concepts of country and power in ICA, respectively. The procedure of GAS, which is applied in this research, is described in [12]. The best particle found in each calling of GAS can be replaced by the original country provided that its mass is greater than the power of the country. After the local search is terminated, to find the current best solution and to prepare all of the countries to begin the next iteration, it is necessary to simply sort the imperialists based on their power; thus, not all of the countries must be sorted. This approach is one of the advantages of this implementation that helps the algorithm to decrease the time needed to find the best solution. To start the next iteration, it is needed only to save the best imperialist and then to go to step three. Countries must be moved again if it is necessary to continue the process. ICGAS pseudocode is shown in Fig. 2. VI.

EXPERIMENTAL RESULTS

The ICGAS algorithm has been implemented in Visual C#.Net 2010 and executed to optimize a different type of timecharge optimization for a combination of services on cloud computing. To ensure the accuracy of the results, it is important to use real-world datasets. Hence, WSDream-QoSDataset2 is a real-world dataset that has a large amount of data and that has been considered. To compare the ICGAS results with different optimization algorithms and to evaluate the results, some of the other known algorithms, such as Genetic Algorithms, PSO and the original version of ICA, have been implemented and applied to solve generated problems. All of the algorithms could be executed in 60 seconds, and the results obtained for all of the algorithms were compared in the form of Eligibility, which is calculated by (16). For the first evaluation, a time-charge optimization problem for cloud computing service composition was generated randomly based on requiring 20 simple atomic services to be composed. The number of countries and imperialists has been 500 and 10, respectively. The problem was solved 10 times for each method, using ICGAS, GA, PSO and ICA, and the final average results were placed in table I. The analysis information in Table I shows that PSO found the best solution and had a better eligibility value than those found by GA and the original Imperialist Competitive Search.

Nonetheless, ICGAS obtained the best solution and has a better value than PSO; overall, their value differences are significant. For further evaluation, a larger, randomly generated timecharge optimization problem for cloud computing service composition was considered, which was to compose 50 atomic services to reach a complex service for a customer. As in the previously mentioned problem, this problem was solved 10 times with each method, using ICGAS, GA, PSO and ICA. The average results of each algorithm were calculated separately and were inserted into Table II. The number of countries and imperialists has been 800 and 15, respectively. ICGAS reached the best solution for the new larger and consequently harder problems compared with the other algorithms. It is remarkable that, for the larger problem, proportions of ICGAS results on PSO and other algorithm results are larger than the same values related to previous problems. To prove the increasing ICGAS optimality while increasing the size of the problems, the third problem that was generated randomly was to compose 100 atomic services. ICGAS has used 1000 countries and 20 imperialists to solve the problem. The average of the results, which were run 10 times with four different algorithms, was placed in Table III, similar to with earlier problems addressed in this paper. Comparing the results in Table III and the previously obtained results reveals that the optimality of ICGAS increased while the size of the problem increased. It is possible to define the optimality of ICGAS compared to PSO, as mentioned in (19).

Optimality =

Eligibility of the best Solution

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Final Eligibility obtained using different algorithms for composing 20 atomic services GA

PSO

ICA

ICGAS

85.3

79.944

80.601

71.075

(19)

where BestSolution(ICGAS) and BestSolution(PSO) are the best solution that is obtained using ICGAS and PSO, respectively. Based on this definition, if the PSO results in the three experiments conducted were better than for the GA, and if the ICA results are the basis for calculating the optimality and are considered to be the fractional denominator, then Fig. 3 shows that there is an increased optimality of ICGAS over PSO. TABLE II.

TABLE I.

BestSolution ( ICGAS) BestSolution ( PSO)


Final Eligibility obtained using different algorithms for composing 50 atomic services GA

PSO

ICA

ICGAS

294.409

266.859

279.113

215.476


TABLE III.

Final Eligibility obtained using different algorithms for composing 100 atomic services


GA

PSO

ICA

ICGAS

615.917

578.011

598.201

447.987

When the differences between the ICGAS results are more than one hundred percent, then ICGAS has better performance in comparison to PSO. These differences were 11.09, 19.25 and 22.5 for problems one, two and three, respectively. Because of the large numbers of required services that customers request using the cloud, these differences can be considered to be very significant. As stated in section V, applicants should specify the priority of the time and charge that they need. To analyze the effects of these coefficients, another service composition problem with 20 atomic services to compose were generated randomly and executed in three different cases. Case one is a case in which the priority values were equal to 50% for time and 50% for charge. The priority values for cases two and three have been set equal to 80% (time), 20% (charge) and 20% (time), 80% (charge), respectively. The average response-times and charges obtained from executing ICGAS 10 times for each of the three cases are given in Table IV.

VII. CONCLUSIONS It is inevitable to consider QoS parameters in the service composition. In the view of most of the end users, the response-times and execution fees are two important increased parameters among the many effective QoS attributes that are considered. In this research, a new memetic algorithm made from the hybridization of an imperialist competitive algorithm and a gravitational attraction search has been introduced and studied. The significant effects of selecting an appropriate number of ICS generated solutions using a roulette wheel selection algorithm to apply a local search is undeniably a success for ICGAS. Comparing the results obtained from ICGAS with GA, PSO and the original ICS results confirms that reaching optimal or near optimal solutions for problems with different sizes is achievable. TABLE IV.

Response time and charge obtained using ICGAS for composing 20 atomic services

Case 1

ICGAS

Case 2

Case 3

Response time

Charge

Response time

Charge

Response time

Charge

12.423

30.529

8.903

38.720

17.337

23.917

FIGURE 3. OPTIMALITY OF ICGAS ON THE BASIS OF PSO

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S. A. Ludwig, "Applying Particle Swarm Optimization to Quality-ofService-driven Web Service Composition", Proceedings of 26th IEEE International Conference on Advanced Information Networking and Applications (AINA), Fukuoka, Japan, 2012, pp. 613-620. [2] J. Octavio Gutierrez-Garcia and Kwang-Mong Sim, "Self-Organizing Agents for Service Composition in Cloud Computing ", 2nd IEEE International Conference on Cloud Computing Technology and Science, 2011, pp. 59-66. [3] Wenbin Wang and et.al, "An improved Particle Swarm Optimization Algorithm for QoS-aware Web Service Selection in Service Oriented Communication", International Journal of Computational Intelligence Systems, Suppl. 1, 2010, pp. 18-30. [4] Adrian Klein and et.al, "Towards Network-aware Service Composition in the Cloud", International World Wide Web Conference, France, 2012, pp. 959-968. [5] Philipp Leitner and et.al, "Cost-Based Optimization of Service Compositions", Distributed Systems Group, Vienna University of Technology, for Publication in IEEE Transactions on Services Computing, 2011. [6] Kevin Kofler and et.al, "User-Centric, Heuristic Optimization of Service Composition in Clouds", Springer-Verlag Berlin Heidelberg, Euro-Par 2010, Part I, LNCS 6271, 2010, pp. 405–417. [7] Atashpaz-Gargari, Caro Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", Evolutionary Computation, CEC 2007. IEEE Congress on, 2007, pp. 4661-4667. [8] Helena Bahrami and et.al, "Imperialist Competitive Algorithm with Adaptive Colonies Movement", I.J. Intelligent Systems and Applications, issue 2, 2012, pp. 49-57. [9] Zibin Zheng, Yilei Zhang, and Michael R. Lyu, ˬ"Distributed QoS Evaluation for Real-World Web Services", in Proceedings of the 8th International Conference on Web Services (ICWS2010), Miami, Florida, USA, July 5-10, 2010, pp.83-90. [10] Yilei Zhang, Zibin Zheng, and Michael R. Lyu, ˬ"Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing", in Proceedings of the 30th IEEE Symposium on Reliable Distributed Systems (SRDS 2011), Madrid, Spain, Oct.4-7, 2011, pp. 1-10. [11] James E. Baker, "Reducing bias and inefficiency in the selection algorithm", Proceedings of the Second International Conference on Genetic Algorithms and their application, Massachusetts Institute of Technology, Cambridge, Ma, 1987, pp. 14-21. [12] Amin Jula, Narjes Khatoon Naseri, Amir Masood Rahmani, "Gravitational Attraction Search with Virtual Mass (GASVM) to solve Static Grid Job scheduling Problem", The Journal of Mathematics and Computer Science, Vol .1 No.4, 2010, pp. 305-312.


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