20120140502016

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
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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.

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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.

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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.

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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

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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)

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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
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
15 update stepind

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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.

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(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

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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.

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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.

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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

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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.
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