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20120140502016

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  • 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 138 IMPROVED FSS ALGORITHM WITH QoS IN CLOUD K R Remesh Babu1 , Alia Teresa T M2 , A Neela Madheswari2 , Philip Samuel3 1 Government Engineering College, Painavu, Idukki, India 2 KMEA Engineering College, Aluva, India 3 Division of Information Technology, SOE, CUSAT, India ABSTRACT Real world problems are packed with complex issues often hard to be computed. This is due to large dimensionalities of search spaces. Nature inspired algorithms are able to deal with these dimensionalities. A key challenge faced by providers when building a cloud infrastructure is managing physical and virtual resources according to Quality of Service (QoS) demands with respect to the access time to the associated servers, the capacity needed for storage and the heterogeneity aspects traversing different networks in a holistic fashion. Existing system is a combination of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) algorithm to find the solution to resource scheduling problems in cloud environment. It has been observed that Fish Swarm Search (FSS) is powerful in finding more global optimal solutions than PSO. FSS has to be combined with a local optimization technique in order to get better results. Even though existing system uses PSO, it has mapped its operators with genetic algorithm operators. The crossover and mutation operators of genetic algorithm when used with ACO helps in breaking from local optimal areas. If ACO and PSO combination is provided with a global optimal solution in the beginning then more global optimal solutions can be found out. So in the proposed system we have improvised the existing system by adding FSS in the beginning. The QoS factor considered is the completion time of a set of tasks. Queuing factor along with crowding factor has helped in enhancing the FSS to find optimal solutions. Keywords: Cloud Computing, Metaheuristic, Scheduling, QoS. 1. INTRODUCTION The latest craze in the IT (Information Technology) sector is Cloud Computing. Some call it the new frontier. Cloud computing is often called the 5th Utility [13]. Utilities such as water, gas, and electricity are fundamental in our daily life and are exploited on a pay per use basis. The existing INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 2, February (2014), pp. 138-151 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 4.1710 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 139 infrastructures deliver these services almost anywhere and anytime. The usage of these utilities is charged, according to different policies, to the end user. The same idea of utility is applied to computing and a consistent shift towards this approach is seen with the proliferation of Cloud Computing. Cloud Computing is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on a service-level agreement[14]. A key challenge faced by providers when building a cloud infrastructure is managing physical and virtual resources according to user-resources demands, with respect to the access time to the associated servers, the capacity needed for storage and the heterogeneity aspects traversing different networks in a holistic fashion [17]. The orchestration of resources must be performed in a way that rapidly and dynamically provides resources to applications. It’s very challenging considering that resource provisioning systems in existing systems such as grids mainly focus on application performance. Scheduling is concerned with the allocation of limited resources to activities over time. Activities may be tasks and resources may be processors. Methods for scheduling problems depend on the computational complexity. Scheduling problem is a classified NP-hard optimization problem. In the Scheduling Problem, n is the number of jobs, m is the number of machines. Each job is processed by exactly one of the machines. If job j is processed by machine i, it takes pij time units. Scheduling problems involve solving for the optimal schedule under various objectives, different machine environments and characteristics of the job. Some of the objectives of the scheduling problems include minimizing the makespan, or the last completion time of a job, or minimize the total completion time of all jobs. As resources are always limited in real world applications, we have to find solutions to optimally use these resources under various constraints. These optimization problems can be solved using stochastic algorithms. Stochastic algorithms are of two types heuristic and meta-heuristics [18]. Heuristics is a way by trial and error to produce acceptable solutions to a complex problem in a reasonably practical time. The aim is to find good solution. There is no guarantee that the best solutions can be found. Further development over the heuristics is the meta-heuristics algorithm. Meta means beyond and they generally perform better than simple heuristics. Two major components of metaheuristic algorithms are intensification and diversification or exploitation and exploration. Diversification means to produce diverse solutions so as to explore the search space on the global scale, while intensification means to focus on the search in a local region by exploiting the information that a current good solution is found in this region. This is in combination with the selection of best solutions. The selection of the best ensures that the solutions will converge to optimality. Metaheuristic algorithms are of two types population based and trajectory based. Population based algorithms include Genetic algorithms (GA), Particle Swarm Optimization (PSO), Fish School Search (FSS), Ant Colony Optimization (ACO), Bee Colony Optimization etc. A genetic algorithm is a search method based on the abstraction of Darwinian evolution and natural selection of biological systems and representing them in the mathematical operators: crossover, mutation, fitness and selection of the fittest [18]. Ant Colony Optimization (ACO) is a search technique that was inspired by the swarm intelligence of social ants using pheromone as a chemical messenger [11]. Particle Swarm Optimization (PSO) is another optimization technique inspired by swarm intelligence of fish and birds and even by human behaviour. Fish school Search (FSS) greatly benefits from the collective emerging behaviour that increases mutual survivability. FSS is composed of three operators: swimming, feeding and breeding. Together these operators provide computing behaviour such as (i) high dimensional search ability (ii) automatic selection between exploration and exploitation and (iii) self-adaptable guidance towards sought solutions. In this work the power of FSS in finding global optimal solution has been used in addition with the already existing combination of ACO and PSO. FSS is found powerful in many multimodal optimization problems and this fact has led to its usage in our work.
  • 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 140 2. RELATED WORK M Dorigo et al discusses the general purpose optimization technique known as Ant Colony Optimization that takes inspiration from the foraging behaviour of some ant species [11]. These ants deposit pheromone on the ground in order to mark some favourable path that should be followed by other members of the colony. ACO exploits a similar mechanism for solving optimization problems. A combination of genetic algorithm with ACO has been proposed in [4]. Firstly it adopts a GA to give information pheromone to distribute. Secondly it makes use of ACO to improve the precision of the solution. However convergence of GA is not much when compared to swarm intelligence techniques. ACO has also been used in implementing load adaptive cloud resource scheduling model by finding a hotspot [5]. The hot spot is determined which depends on criteria’s such as CPU usage, memory and network bandwidth. Boundary conditions for these criterions have been set. A resource selection constraint function determines the remaining amount of resources for a node. A node with minimum value for resource selection constraint function is selected as idle node. Even though a maximum value is set for the resource selection constraint function, the impacts of this value on completion time of tasks with computationally intensive tasks have not been done. The paper [7] introduces a factor called degree of imbalance that is used to measure the imbalance among virtual machines. Another hybrid algorithm [6] which is a combination of ACO and cuckoo search is used to reduce the energy consumption while scheduling the tasks. A combination of ACO and FSS [2] has been used to resolve the combinatorial optimization problem. Congestion degree has been used to control the crowding of fishes around a node. Artificial fish cannot ultimately build up to all around the optimal value and congestion degree has a negative impact on optimizing performance towards the late of optimization. So FSS is performed first and then ACO is performed. An improved artificial fish swarm algorithm (AFSA) for solving a combinatorial optimization problem berth allocation problem (BAP), which was formulated [8]. Its objective is to minimize the turnaround time of vessels at container terminals so as to improve operation efficiency customer satisfaction. In the paper [9] Particle swarm simulated annealing algorithm is used in which firstly, better swarm is got by fast searching ability of PSO, secondly, partly better individual is optimized by jumping ability of Simulated Annealing (SA). This improves the probability and speed of convergence to the optimal solution with advantages of PSO and SA. As a whole, GA algorithm and SA algorithm spends more time as the number of tasks increase. ACO algorithm execute task slowly at first, but at the later period its time increase is less than GA algorithm and SA algorithm with improved positive feedback. ACO is also better for finding local optimal solutions when compared to SA. 3. SCHEDULING Scheduling problems are considered NP-hard problems. It means there are no known algorithms for finding optimal solutions in polynomial time. Single-objective optimization problems are usually solved for finding a single optimal solution, despite the existence of multiple optima in the search space. In the presence of multiple global and local optimal solutions in a problem, an algorithm is usually preferred if it is able to avoid local optimal solutions and locate the true global optimum [19]. Being a heuristic problem the resource scheduling problem always prefers in finding a more optimal solution. Considering this fact we can improvise the existing system by adding another nature inspired algorithm which is named as Fish School Search (FSS). FSS has proved in outperforming PSO in many multimodal functions.
  • 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 141 Hybrid algorithm of existing system uses ACO and PSO to find the optimal solution. The problem under consideration of existing system has a single objective function i.e. to minimize the total completion time of tasks under consideration. In existing system the PSO makes use of crossover and mutation operators to find the new position of the particle in each iteration. This is because in PSO a particle is analogous to a chromosome (member) in a population. Similarly as a chromosome in GA represents a solution, a particle in PSO represents a candidate solution. PSO doesn’t label its operations in the same way as GA, but analogies exist. These analogies depend on the implementation of the GA operation [16]. Even though existing system claims the usage of PSO it has limited many of its steps to crossover and mutation analogy steps. In this it cannot claim a complete PSO implementation. FSS being another swarm intelligence algorithm and more specific in its operations and operators enables us to provide more optimal solutions for many multimodal optimization functions. The proposed system uses enhanced FSS in the beginning of hybrid ACO-PSO algorithm. 4. PROPOSED METHODOLOGY There are three different modules in the proposed system as given below. 1. Generation of input 2. Enhanced Fish school search implementation 3. Hybridization of ACO and PSO 4.1. Generation of Input A set of independent tasks is generated with varying arrival times. This is simulated with the help of random generation of values for tasks characteristics such as task length in Million Instructions (MI) and task arrival times in seconds. 4.2. Enhanced Fish School Search Implementation In this section the enhanced hybrid FSS has been implemented. The operators such as swimming and feeding were used. Since fish school search has proved itself better than Ant colony optimization and Particle swarm optimization to find more global optimal solution, we have used it in the beginning of our implementation. This will help the existing combination of Ant colony optimization and particle swarm optimization to find more local optimal solutions around the region of global optimal solution. The different movements of FSS include individual movement; collective instinctive movement and collective volitive movement are implemented. In the enhanced fish school search each fish moves individually in a random fashion. Collective movements of the fishes are controlled by crowding factor and queuing factors. 4.3. Hybrid of ACO and PSO The proposed method starts with a few iterations of ACO runs in the beginning. The remaining iterations use the hybridized ACO and PSO algorithm. This module starts functioning after the enhanced fish school search runs for a specified number of iterations. In each iteration the hybrid ACO-PSO algorithm finds an optimal solution. Then this solution set will be compared with the solution obtained using FSS. The nodeset is used to represent the solution set. The nodeset will be string of node ids assigned for each task set. The nodeset which gives the minimum value i.e. minimum completion time for a set of tasks will be stored during each iteration, in order to compare with the solution in the next iteration. In this way we would be left with the nodeset that gives minimum value during the entire iterative process.
  • 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 142 5. SETTING UP OF CLOUD ENVIRONMENT The experimental cloud environment consists of multiple nodes. The numbers of nodes are set to maximum 10. Each node is characterized by the number of CPUs in it and processing capability in terms of MIPS. The details of cloud environment are given in Table1. Table 1: Cloud Environment Settings Parameter Value Number of nodes in cloud 10 MIPS of PE 250 -2000 MIPS Number of PEs per node 2-8 Task Length 5000 - 15000 MIPS 5.1. Input Generation Random values were assigned to each input task lengths and for its arrival times. The task arrival times were in periodic intervals. Values for number of CPUs and processing capability for each node has been generated randomly [7]. The experiments were conducted for different number of jobs and nodes. 5.2 Fish School Search The search process in FSS is carried out by a population of limited memory individuals as the fish. Each fish represents a possible solution to the problem [15]. The main feature of the FSS paradigm is that all fish contain an innate memory of their successes their weights. Another major feature of FSS is the idea of evolution through a combination of some collective swimming, i.e. operators that select among different modes of operation during the search process, on the basis of instantaneous results. The concept of food is related to the function to be optimized in the process. For example, in a minimization problem the amount of food is inversely proportional to the function evaluation in this region. The aquarium is defined by the delimited region in the search space where the fish can be positioned. The operators are grouped in the same manner in which they were observed when drawn from the fish school. They are as follows: Feeding: Food is a symbol for indicating to the fish the regions of the aquarium that are likely to be good spots for the search process. Swimming: A set of operators that are responsible for directing the search effort globally towards subspaces of the aquarium, that are jointly sensed by all individual fish as more promising with regard to the search process. The group reasons of swimming are grouped into three classes. 1. Individual 2. Collective-Instinct 3. Collective-Volitive Movement. 5.2.1. Individual Movement Individual movement occurs for each fish in the aquarium at every cycle of the FSS algorithm. The swim direction is randomly chosen [15]. Provided the candidate destination point lies within the aquarium boundaries, the fish assesses whether the food density there seems to be better than at its current location. If this is not the case or if the step-size is not possible, the individual movement of the fish does not occur. Soon after each individual movement, feeding occurs. To
  • 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 143 include more randomness in the search space we multiply the individual step by a random number generated by a uniform distribution in the interval [0, 9]. In each iteration, each fish randomly selects a new position according to formula. n(t) =x(t) + rand(0,9) * stepind (5.1) Here n(t) stands for new position of fish and x(t) stands for current position of fish. Function rand() takes random values between 0 and 9. Stepind is linearly decreased during each iteration as follows [10]. (5.2) 5.2.2. Feeding operator Update fish weight according to formula. (5.3) wi(t) is the weight of fish i, ∆f(i) is the difference of the fitness at current and new location, max(∆f) is the maximum ∆f among all the fishes. 5.3. Collective Instinctive Movement After all fish have moved individually, weighted average of individual movements based on the instantaneous success of all fish is computed. All fish that successfully performed individual movements influence the resulting direction of the school movement. The resulting direction m(t) is evaluated by (5.4) All fish of the school update their positions according to m(t). xi(t + 1) = xi(t) + m(t) (5.5) 5.4. Collective volitive movement This movement is devised as an overall success/failure evaluation based on the incremental weight variation of the whole fish school. If the fish school is putting on weight (meaning the search had been successful) then the radius of the school should contract according to formula. Otherwise dilate according to formula. x(t + 1) = x(t) - stepvol * rand(0, 1) * [x(t) - b(t)] (5.6) x(t + 1) = x(t) + stepvol * rand(0, 1) * [x(t) - b(t)] (5.7) stepvol = 2 * stepind (5.8)
  • 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 144 b(t) represents the fish school barycenter and is calculated as follows. (5.9) 5.4. Psuedocode for Fish School Search The following algorithm includes steps followed in a general FSS [10]. Algorithm 1: Pseudocode for Fish School Search 1 Initialize randomly all fish 2 while stop criterion is not met do 3 for each fish do 4 individual movement 5 evaluate fitness function 6 feeding operator 7 for each fish do 8 collective instinctive movement 9 Calculate barycentre 10 for each fish do 11 Collective volitive movement 12 evaluate fitness function 13 update stepind 5.5. Enhanced pseudo code for Fish School Search Fish School Search can be improved for our scenario by introducing queuing factor. In the proposed work, for the enhancing the FSS, both queuing factor and crowding factor are considered as two main parameters. Our problem being a resource scheduling problem we have mapped fishes in FSS to cloudlet. The locations in the aquarium are considered as resources in the cloud environment. The modified algorithm after parameter introduction is as follows. Algorithm 2: Enhanced Psuedocode for FSS 1 Initialize randomly all fish 2 while !(stop criterion) do 3 for each fish do 4 if f(xi(t+1)) < f(xi(t)) then 5 if (nf/fishnum) < crowding factor then 6 if ΣiЄfish in queue ((normalized(f(xi))/ total no of fish in queue) < queuing factor then 7 Individual movement 8 feeding operator 9 for each fish do 10 collective instinctive movement 11 Calculate barycentre 12 for each fish do 13 Collective volitive movement 14 evaluate fitness function 15 update stepind
  • 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 145 According to proposed enhanced FSS algorithm, the fishes will move individually based on the values of crowding factor and queuing factor. Crowding factor around a location (resource) is determined by the formula nf/fishnum, where 'nf' is the number of fishes in that region and fishnum is the total number of fish in the aquarium. Here the queuing factor depends on the fitness value in that region. After individual movement then the fishes move collectively. Table 2: Parameter settings for FSS Parameter Values stepind initial 10 stepind final 0.1 initial weight of the fish 50 crowding factor 0.5 queuing factor 0.5 Number of food_location 10 No of fishes 40 5.5.1. Queuing factor introduced for FSS A new parameter has been introduced which is called as queuing factor to enhance the FSS algorithm. Queuing factor alone with crowding factor strengthens the task scheduling process in fish school. Crowding factor determines the number of fishes are around a food_location (node) waiting to feed themselves. Whereas queuing factor reminds us that a longer crowd doesn’t necessarily results in a longer time to feed. It can happen a task in a longer queue can be served earlier than the same task in a shorter queue. This arises from the fact that a tasks completion time not only depends on the computing capability of the selected node but the time spent in the queue. In the enhanced algorithm the queuing factor is set as a value between 0 and 1. The fish selects a new location only if it finds the fitness function being better and crowding and queuing factors are within a range. The queuing factor is calculated as the average of the normalized values of the fitness functions of fishes in the queue. (5.9) N stands for number of fishes in queue, f(xi) stands for the fitness value of fish i at position x. 5.6. ACO Algorithm In ACO algorithm ants are the basic agents for computation. Each ant will set out to find a solution. The ants will leave a pheromone on visiting a node. The ant selects a computing node depending on strength of the pheromone on the node. More pheromone means more chance of being selected. In cloud simulation an ant will find out the solution for task scheduling problem. Here pheromone is associated with the computing capability of a node. In the beginning stage each node is initialized with a pheromone value that depends on the number of processors and MIPS of each processor. When an ant selects a node the pheromone strength of that node is reduced. The process of selecting the next node is determined by the probability function given in 5.12. The pheromone in CPUs are initialized using the following equation 5.10 (5.10) Here τi indicates the CPU pheromone value for node i, m is the number of processors and p is the MIPS rating of each processor.
  • 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 146 (5.11) τi (t1) represents the CPU pheromone of node i at time t1. The probability for selecting the next node for a task is determined by the equation 5.12 (5.12) Here pj denotes the probability that ant will select the node j, τj α denotes the pheromone strength of node j, α denotes the regulatory factor which determines the importance of τj, and βj denotes the importance of heuristic information. (5.13) is the expected execution time for new task Jd at time t2 on node j with load nd, where nd is the current load (5.14) Jv is the previously completed task and nv is the load of the previous task. 5.7. PSO Algorithm The basic concept of the PSO algorithm is to create a swarm of particles which move in the space around them (the problem space) searching for their objective or the place which best suits their needs given by a fitness function. In each iteration, the particles update themselves according to two extreme values: one is the optimal solution finding by a single particle, namely the individual extremum; the other is the optimal solution finding by whole particle swarm, that is, namely the global extremum. Particles update their velocity and position according to the two extreme values above and the following two formulas. V = ω*V + C1*rand()*(pBest-X) + c2*rand()*(gBest-X) (5.15) X= X + V (5.16) Among them, V= [v1, v2…….vd] is the velocity of the particle, X= [x1, x2 …xd] is the current position of the particle, d is the dimension of solution space. pBest is the individual extremum, gBest is the global extremum, rand() is random number between 0 and 1. c1, c2 are called learning factors, which are used to adjust the particle update step, and ω is a weighted factor. The PSO can be modified using idea of genetic algorithm. So in PSO, V can be seen as the mutation operation of genetic algorithm, and c1 * (pBest - X) + c2 * (gBest - X) in equation 5.15 can be seen as the crossover operation of genetic algorithm, which lets the current solution do crossover operator with the individual extremum and the global extreum so as to produce a new location. For this the crossover strategy fixed as follows: select a cross- region randomly in the second string, old2 is added to the old1 corresponding position, delete the nodes in old1 appeared in the old2 cross- region. Variation strategy: select the node of j1 visits from the nodes of 1-n visit, exchange the j1 visit
  • 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 147 node and the ji+1 visit node in node set N0, and the rest keep unchanged. At this time, the path set node is N1. Table .3 Parameter settings for ACO and PSO Parameter Values α 1.5 β 2 ρ 0.9 λ 0.5 Mutation probability 0.5 Number of ants 10 5.8. Hybrid Algorithm of ACO and PSO Step 1: Initialize iterations nc=0, use ACO algorithm to complete the first traverse (form m path node set). Step 2: Calculate the fitness value based on the current path node set (the expected execution time) and set the current fitness value to individual extremum called ptbest, and set the current path nodes set to individual extreme nodes set called pcbest, then find out the global extremum called gtbest and the global extreme nodes set called gcbest. Step 3: Put m ants randomly on n nodes, put initial staring node of each ant in the current path node set, to each ant k, move to the next node j according to the probability pij, put node j in the current path node set, compute task expected execution time using the equation 5.14. Step 4: Operate as follows for each ant, the path node set of ant j termed N0(j) is crossed by gcbest which gets N1(j), and N1(j) is crossed by pcbest which gets N1(j), N1(j) variants with a specified probability which gets N1(j). Accept the new value if the new objective function gets better, otherwise refuse and N1(j) remains N0(j), then, rediscover individual extremum ptbest and extremal node set pcbest for each ant, find out global extremum gtbest and global extremal node set gcbest. Step 5: Calculate task expected execution time for each ant node set, record the current best solution. Step 6: Update the pheromone concentration using the equation 5.11. Step 7: nc = nc + 1. Step 8: If nc < a predetermined number of iterations, go to step3. Step 9: Obtain the optimal solution; allocate the tasks in the nodes which included in the optimal solution node set.
  • 11. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 148 5.9 Psuedocode of improved hybrid algorithm with FSS The proposed algorithm improves the hybrid ACO-PSO algorithm by combining with FSS. The FSS algorithm can find more global optimal solutions than ACO and PSO. This capability of FSS is being used in the proposed algorithm. The existing FSS algorithm has been enhanced by introducing queuing factor. The improved hybrid algorithm considers the FSS algorithm in the first iteration and then the optimal solution set is made for the global set which is used by ACO iterations. Each ACO results in an optimal solution which is compared with FSS. The solution set with minimum is stored. After performing ACO for few iterations hybrid algorithm ACO-PSO is used. The solution set from FSS helps in finding an optimal solution around the regions of global optimal solution. The psuedocode for the proposed algorithm is given below. Algorithm 3: Psuedocode for Improved hybrid algorithm with FSS 1. Perform FSS 2. Perform ACO for few iterations 3. Compare the global best solution of ant with global best solution ofFSS 4. The global best solution with minimum value is used for hybrid algorithm of ACO and PSO 6. RESULTS Cloud setup with 10 nodes was considered. Consider the cloud with following node characteristics Table 4: Values given for node Characteristics MIPS Number of PEs 7 1469 7 1694 7 680 4 1369 8 1591 7 1573 2 446 2 1457 5 607 3 370 In the first experiment the simulation uses 40 different tasks with varying arrival times. The problem involves assigning tasks to suitable nodes with the aim of reducing the total completion time. The solution is a set of nodes. This is represented using string of node ids. The length of the string of nodes is same as the number of tasks.
  • 12. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 149 Table 5: Comparison of Hybrid Algorithm with FSS and without FSS The table 5 shows a clear difference in the execution time for the existing and proposed systems. If the hybrid algorithm without FSS requires 68 seconds to complete a set of tasks, while FSS improved algorithm needs only 51seconds. The effects of queuing factor have been studied for different values in the range [0, 1]. In the table 6 the average makespan time for improved algorithm and existing system algorithm has been compared for 40 tasks with different queuing and crowding factors. Table 6: Average makespan time values for improved algorithm using different crowding and queuing factors Crowding Factor Queuing Factor Average Makespan (Seconds) Hybrid Algorithm Improved Hybrid Algorithm with FSS 0.5 0.8 51.98 48.90 0.5 0.5 49.06 48.12 0.5 0.3 50.06 49.02 The makespan time of the proposed improved FSS hybrid ACO-PSO algorithm with hybrid ACO-PSO is given in the figure 1. The x axis of the graph represents number of tasks and y axis represents total completion time of set of tasks in seconds. It can be clear from the figure that the proposed algorithm has resulted with minimum completion time of tasks. Figure 1: Makespan graph for 100 Jobs
  • 13. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 150 Figure 2: Comparison upto 500 Jobs The figure 2 shows the comparison of the proposed algorithm upto 500 tasks with a crowding factor and queuing factor 0.5. From the figure we can see that the proposed method shows a significant improvement in the QoS in terms of makespan time. 7. CONCLUSION The proposed method improved the existing system by adding Fish School Search (FSS). The objective of reducing the total completion time of a set of independent tasks has been achieved with this method. As FSS is more powerful in finding global optimal solutions, its usage has helped in reducing the time parameter thereby increasing the quality of service delivered to the customer. Queuing factor introduced in FSS has also helped in getting better results with the improved hybrid algorithm. It has been observed that both the crowding factor and queuing factor plays an equal role in finding global optimal solutions. 6. FUTURE WORK The present work is a single objective one which considers only one QoS parameter i.e. total completion time of tasks. This work can be extended by considering other factors thereby making it a multi objective optimization problem. The factors that can be considered are cost, load balancing and energy consumption. Each can be given weightage according to the priorities of users or type of applications. 8. REFERENCES [1] Xiaotang Wen, Minghe Huang and Jianhua Shi, "Study on Resources Scheduling Based on ACO Algorithm and PSO Algorithm in Cloud Computing", 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science, pp. 219 - 222 , Oct. 2012. [2] KUANG Xiangling, HUANG Guangqiu, "An Optimization algorithm based on Ant Colony Algorithm", International Conference on Automatic Control and Articial Intelligence, pp.469-472, Mar 2012. [3] C. J. A. Bastos Filho, F. B. Lima Neto, M. F. C. Sousa, M. R. Pontes, S. S. Madeiro, "On the Influence of the Swimming Operators in the Fish School Search Algorithm", IEEE International Conference on Systems, Man and Cybernetics, pp.5012 - 5017, Oct 2009.
  • 14. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 138-151, © IAEME 151 [4] Zheping Yan, Yanchao Zhang, Xiaomin Fu, Shuping Peng, "Research of a genetic algorithm Ant Colony Optimization based on cloud model", International Conference on Mechatronics and Automation, pp.4725 - 4730, Aug 2009. [5] Xin Lu, Zilong Gu, "A Load adaptive cloud resource scheduling model based on ant colony algorithm", IEEE international conference on Cloud Computing and Intelligent Systems, pp. 296-300, Sept 2011. [6] M. Matsumoto, "Energy aware scheduling using hybrid algorithm for cloud computing", 3rd International Conference on Computing Communication and Networking Technologies, pp. 1-6, July 2012. [7] Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Wang D., "Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization", Sixth Annual China Grid Conference, pp. 3-9, Aug 2011. [8] Yun Cai, "Artificial Fish School Algorithm Applied in a Combinatorial Optimization Problem", International Journal of Intelligent systems and applications, vol. 2, no.1, pp. 37- 43, Nov. 2010. [9] Shaobin Zhan and Hongying Huo, "Improved PSO based Task Scheduling Algorithm in Cloud Computing", Journal of Information & Computational Science, vol.9, no.13, pp.38213829, 2012. [10] Andreas Janecek and Ying Tan, "Feeding the Fish Weight Update Strategies for the Fish School Search Algorithm", Proceedings of the Second international conference on Advances in swarm intelligence, Vol.Part II, no. 6, pp. 553-562, Jun. 2011. [11] Marco Dorigo, Mauro Birattari and Thomas Stutzle, "Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique, IEEE Computational Intelligence Magazine, Vol.1, Issue.4, pp.28-39, Nov. 2006. [12] Marco Dorigo,Mauro Birattari and Thomas Stutzle, "Comparative Study of Ant Colony Optimization and Particle Swarm Optimization for Grid Scheduling, Journal of Mathematics and Computer Science, Vol.2, No.3, pp.469-474, 2011. [13] Dexter Duncan, Xingchen Chu, Christian Vecchiola, and Rajkumar Buyya, "The Structure of the New IT Frontier: Cloud Computing Part I”, Strategic Facilities Magazine, Pacific & Strategic Holdings Pte Ltd, Singapore, Issue 9, August/September 2009. [14] R. Buyya, C. S. Yeo, and S. Venugopal, "Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities", Keynote Paper in Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications, pp 5-13, Sept 2008. [15] Carmelo J. A. Bastos Filho, Fernando B. de Lima Neto, Anthony J. C. C. Lins, Antonio I. S. Nascimento, Marlia P. Lima, Nature-Inspired Algorithms for Optimisation Studies in Computational Intelligence, Vol. 193, pp. 261-277, Springer Publishers, 2009. [16] Russell C. Eberhart and Yuhui Shi, Evolutionary Programming VII, Chapter 11, Vol. 1447, pp. 611-616, Springer Publishers, 1998. [17] Zaigham Mahmood, Cloud Computing methods and practical approaches, Chapter 6, pp. 107-132, Springer Publishers, May 2013. [18] Alan C. Bovik, Nature-Inspired metahueristic algorithms, 2nd Edition, Chapter 12, pp. 263- 264, Academic Press Publishers, May 2009. [19] Kalyanmoy Dev and Amit Saha, "Multimodal optimization using a bi-objective evolutionary algorithm, Journal of Evolutionary Computation, Vol.20, Issue.1, pp.27-62, 2012. [20] A.Madhuri and T.V.Nagaraju, “Reliable Security in Cloud Computing Environment”, International Journal of Information Technology and Management Information Systems (IJITMIS), Volume 4, Issue 2, 2013, pp. 23 - 30, ISSN Print: 0976 – 6405, ISSN Online: 0976 – 6413.

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