Presented in the Second International Conference on Advanced Data and
Information Engineering (DaEng-2015), Bali, Indonesi, April 25-26. To be
published in LNEE-Springer (IN PRESS).
Job Scheduling Technique for Infrastructure as a Service Cloud
Using an Enhanced League Championship Algorithm
Shafi’i Muhammad Abdulhamid1,2 , Muhammad Shafie Abd Latiff*1 and
Mohammed Abdullahi 1,3
1
2
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru MALAYSIA.
Department of Cyber Security Science, Federal University of Technology Minna, NIGERIA.
3
Department of Mathematics, Ahmadu Bello University, Zaria, Kaduna State, NIGERIA.
(E-mail: shafii.abdulhamid@futminna.edu.ng, shafie@utm.my, muham08@gmail.com)
Abstract. League Championship Algorithm (LCA) is a sports-inspired population based
algorithmic framework for global optimization over a continuous search space first proposed by
Ali Husseinzadeh Kashan in the year 2009. A general characteristic between all population
based optimization algorithms similar to the LCA is that, it tries to progress a population of
achievable solutions to potential areas of the search space when seeking the optimization. In
this paper, we proposed a job scheduling algorithm based on an enhanced LCA optimization
technique for the infrastructure as a service (IaaS) cloud. Three other established algorithms i.e.
First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to
evaluate the performance of the proposed algorithm. All four algorithms assumed to be nonpreemptive. The parameters used for this experiment are the average response time, average
completion time and the makespan time. The results obtained shows that, LCA scheduling
algorithm perform moderately better than the other algorithms as the number of virtual
machines increases.
Keywords: League Championship Algorithm; IaaS Cloud; Job Scheduling Algorithm; Cloud
Computing; Cloud Scheduling; Optimization Algorithm.
1
Introduction
The league championship algorithm (LCA) is a new optimization scheme designed
based on the inspiration of soccer competitions in a championship league. The LCA is
a population based algorithmic framework for global optimization over a continuous
search space first proposed by Kashan [1]. A more detailed LCA can found in [2]. It is
a stochastic population based algorithm for continuous global optimization which tries
to imitate a championship situation where synthetic football clubs participate in an
artificial league for a number of weeks. This algorithm has been tested in many areas
and performed creditably well as compared to other known optimization schemes or
heuristics algorithms [3, 4].
In infrastructure-as-a-service (IaaS) cloud computing, computational
resources such as the virtual machines (VMs) are provided to remote clients in the
form of rents (pay per use). For a cloud client, he/she can demand multiple cloud
services concurrently [5]. Job scheduling had been studied in high performance
computing in cloud systems. However, the autonomous attribute and the resource
heterogeneity within the clouds and the VM execution necessitate different schemes
for job scheduling in the IaaS cloud computing, especially in the federated
heterogeneous multi-cloud system [6].
1
Presented in the Second International Conference on Advanced Data and
Information Engineering (DaEng-2015), Bali, Indonesi, April 25-26. To be
published in LNEE-Springer (IN PRESS).
The aim of this paper is to propose a job optimization scheme in a
heterogeneous IaaS cloud computing system based on t he LCA scheme, which
enables a non-preemptable task scheduling. Job scheduling in the IaaS cloud is a nondeterministic polynomial (NP-hard) problem. Section two reviewes some related
works in LCA and also in jobs scheduling in IaaS cloud. Section three puts forward a
proposed scheme from jobs scheduling in IaaS cloud by enhancing the LCA.Section
four presents the simulation and results, while section five presents the conclusion and
future works.
2 Related Works
Abdulhamid and Abd Latiff [7] presents a paper that proposed a League
Championship Algorithm (LCA) based scheme for global optimization tasks schedule
in cloud environment, which mimics the sport league championships. It is a new
algorithm for numerical function optimization. Kashan and Karimi [8] tests the
effectiveness of the proposed optimization algorithm by measuring the test functions
from a recognized yardstick, usually adopted to authenticate new constraint-handling
algorithms strategy. While Sebastián and Isabel [9] presents an implementation of the
LCA in a Job Shop scheduling in an industrial situation.
Diangang, Zhiya [10] presents an efficient VM scheduling scheme in IaaS
cloud computing system. Shen, Deng [11] and [12, 13] presents a category of cloudbased, online, hybrid scheduling procedures that reduces cost by using both ondemand and reserved case in point. Sun, Ji [14] puts forward a VM scheduling
technique, motion and disaster recovery scheme for IaaS cloud environment based on
a runtime and average usage of the three layers of IaaS cloud. A double combinatorial
resource allocation scheme was also proposed for distributed cloud computing in [15].
3
Proposed League Championship Algorithm
The proposed LCA based job scheduling scheme was designed by enhancing the LCA
metaheuristic algorithm inspired based on the metaphor of sporting contests in a
round-robin sport leagues. A detailed LCA steps can found in [2, 7] including the
seven idealized rules guiding its implementation.
3.1
Parameters Matching
In order to achive optimization with the proposed algorithm (LCA) in scheduling
cloud jobs, we must first have to match the corresponding variables or parameters of
the two systems. To achive this, a simple comparison was made with the variables of
a known evolutionary algorithm (EA) and the following matching was achived;
• League L = population
• week t
= iteration
• team i
= ith member in the population
• formation
= solution
• playing strength ( ) = fitness value
2
Presented in the Second International Conference on Advanced Data and
Information Engineering (DaEng-2015), Bali, Indonesi, April 25-26. To be
published in LNEE-Springer (IN PRESS).
•
3.2
number of seasons S = maximum iterations
Winner/Loser Determination
One of the most important feature of the LCA is the winner/loser determination
scheme. In this resaerch work, we to utilized this feature in determining which job is
scheduled on which VM in the IaaS cloud. Considering a normal league system,
teams play each other weekly and their game result is evaluated on the basis of
win/loss/tie for each of the teams. For instance, in football league, each club is to get
three points for win, zero for loss and one for draw/tie. By ignoring, the irregular
abnormalities which may ensure even outstanding clubs in a variety of unsuccessful
outcomes, it is probable that a more dominant club having a superior playing pattern
defeats the lesser team. In an ideal league situation that is free from uncertainty
effects, an assumption can be easily made for a linear correlation between the playing
pattern of a club and the result of its matches. Utilizing the playing power condition,
the winner/loser in LCA is determined in a stochastic approach with criteria that the
probability of winning for a club is relative to its degree of fit. Given teams i and j
playing a league match at week t, with their formations
and
and playing
powers
(
) and (
), correspondingly. Let
represents the probability of
team i to defeat team j at week t (
is defined respectively). Given also
ideal value (e.g., a lower limit on the best value).
=
be an
(1)
From the idealized 3 rule we can also write:
+
=1
(2)
From equations (1) and (2) above we solve for
=
(3)
In order to find the winner or loser, a random number in between 0 to1 is generated; if
, team i wins and team j loses;
the generated number is less than or equal to
otherwise j wins and i loses. This method of finding the win or lose is in line with the
idealized rules. If ( ) be arbitrarily closed to ( ), then
can be arbitrarily
closed to 1/2. Moreover, if
(
)becomes far greater than (
( ), then approaches to one. Then, the value of
feature, we use from the best function
(i.e.,
= min $%,….,) { (+ )}.
), namely (
)»
may be unavailable in the
value found so far
3
Presented in the Second International Conference on Advanced Data and
Information Engineering (DaEng-2015), Bali, Indonesi, April 25-26. To be
published in LNEE-Springer (IN PRESS).
3.3 The LCA Enhancement
In the original LCA, two teams i and jare to contest for a football match in every week
t, the winner is to maintain its formation
and its playing strenght in order to play
the next match against the next team j. This scenario is similar to the contest between
jobs (represented as Cloudlets in CloudSim) in cloud environment in order to secure
access to resources for execution. If we consider the jobs (cloudlets) to be the teams I
and j, one of the modification we introduced here is that, the winner of the contest will
get access to the cloud resources and be executed. Therefore, new cloudlets i-1 and j-1
with new formations Xti-1 and Xtj-1 and also new playing strenghts f(xti-1) and f(xtj-1)
will be generated to contest for the next match. This partern will be maintained untill
all the cloudlets are executed. Fig. 1. shows the hierarchical execution of jobs using
the enhanced LCA scheme.
team i
f(xti)
1st winner
(execute)
team j
f(xtj)
team i-1
f(xti-1)
new
formation
team j-1
f(xtj-1)
new
formation
Σ
2nd winner
(execute)
team i-2
f(xti-2)
new
formation
Σ
i-th winner
(execute)
solution
team j-2
f(xtj-2)
new
formation
Fig. 1. Enhanced LCA Algorithm
The total sum of the executed cloudlets can be obtainted by summing-up Σ all the
executed jobs, starting from the 1st winner of the contest to the i-th winner. While the
makespan time is the maximum completion time of a cloudlets. It is also described
as the peroid from the start of the first winner execution to the end of the last
cloudlet execution in the schedule. It assumes that the cloudlets are ready at time
zero and resources are continuously available during the whole scheduling.
Mathematically, makespan can be expressed as;
(4)
Makespan Cmax = max{Ci’} = max{C1’, C2’, ..., Cn’}
where, Ci’ is the completion of task i. The lesser the makespan the better the efficiency
4
Presented in the Second International Conference on Advanced Data and
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published in LNEE-Springer (IN PRESS).
of the algorithm, meaning less time is taken to execute the algorithm.
Algorithm 1. League Championship Algorithm [2]
1. Set the league size L and the amount of seasons S
and set t=1
2. Create a league timetable
3. Set team formations
and establish the playing
strengths
( ) along with them. Let the
initialization be the teams’ new formation;
4. While [ t ≤ S (L-1]) ]
5. Using the league timetable at week t, find the
winner/loser among each pair of teams by means
of their playing strength;
6. t=t+1
7. For i = 1 to L
8. Formulate a new formation Xti-1 for team i1 for the next coming fixture, while taking
into
consideration the team’s new formation
f(Xti-1) and previous week events. Determine
the playing strength of the new system;
9.
If the new formation is the fittest
one, regard the new formation as the team’s
current finest formation;
0. End For
1. If mod (t, L-1) = 0
2.
Generate a league timetable;
3. End if
4. End While
4
Simulation and Results
Three other well established algorithms i.e. First Come First Served (FCFS), Last Job
First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the
proposed LCA based scheduling technique. The parameters used for measuring the
scheduling algorithms in this experiment are based on three factors - Average
Response Time, the Average Completion Time and Makespan Time. The data set was
formed by using the Delft University of Technology workload traces in CloudSim
toolkit. The experiment was performed by varying the number of VMs in the IaaS
cloud from 10 to 130. All four algorithms assumed to be non-preemptive. Fig. 2
shows a prototype of the simulation setup environment for the experiment.
5
Presented in the Second International Conference on Advanced Data and
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C lou d U ser
C lo ud U ser
C lo ud U se r
V IR TU A L IN F R A S T R U C T U R E
L C A -B ase d S che duling Algorithm
VM
VM
VM
VM
VM
VM
VM
H yp ervisors
H os t
H yperv iso rs
H ost
VM
VM
H yp ervisors
H o st
Fig. 2: IaaS Cloud Setup
The experiment was repeated five times and the average total completion time for
each of the algorithms was captured and tabulated. The total completion time is the
total execution time plus the total waiting time of the job.
Average Completion Time (Sec)
80000
70000
60000
50000
LJF
40000
FCFS
30000
BEF
20000
LCA
10000
0
10
30
50 No. of70VM
90
110
130
Fig. 3. Average Completion Time
Fig. 3. shows that the average total completion times as calculated by the four
scheduling schemes. The average total completion time as processed by the LCA
scheduling algorithm is shorter than the other three algorithms, i.e. FCFS, LJF and
BEF, especially as the VMs increases. The LJF has the longest completion time
amongst the algorithms under consideration. This results obtained from the IaaS cloud
environment also shows that, LCA scheduling algorithm perform moderately better
6
Presented in the Second International Conference on Advanced Data and
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published in LNEE-Springer (IN PRESS).
than the FCFS and the LJF algorithms throughout the experiment, but only
outperformed the BEF as we continue to increase the number of the VMs.
Average Response Time (Sec)
25
20
15
FCFS
BEF
10
LJF
5
LCA
0
10
30
50
70
90
110
130
No. of VM
Fig. 4. Average Response Time
Makespan Time (Sec)
Fig. 4. shows that the average response time for all the four algorithms have declined
by adding more VMs to execute the jobs. The LJF and FCFS scheduling algorithms
result in poor response times as compared to LCA at the beginning of the scheduling
process. But the BEF outperformed all at the beginning of the experiment. As the
number of VMs increased to execute the jobs, the LCA begins to perform better than
BEF, FCFS and the LJF.
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
FCFS
LJF
BEF
LCA
10
30
50
70
90
110
130
No. of CloudLets
Fig. 5. Makespan Time
7
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published in LNEE-Springer (IN PRESS).
Fig. 5. shows the makespan times as calculated by the four scheduling schemes. The
makespan time as processed by the LCA scheduling algorithm is lesser than the other
three algorithms, i.e. FCFS, LJF and BEF, especially as the number of tasks increases.
The FCFS has the highest makespan time amongst the algorithms under
consideration. This results obtained from the IaaS cloud environment also shows that,
LCA scheduling algorithm perform moderately better than the FCFS and the BEF
algorithms throughout the experiment, but only outperformed the LJF as the number
of tasks increases. The implication of this result is that, the proposed LCA scheduling
scheme will help the cloud customers to save more money while using the cloud. This
is because the algorithm helps to reduce the makespan time which is the maximum
completion time of tasks, making the customers to spend lesser time in the pay per use
IaaS cloud.
5 Conclusion and Future Works
The LCA based scheduling technique for jobs optimization in the IaaS cloud has not
been adapted in this environment before. After a comprehensive review of the
proposed algorithm, the LCA-based scheduling scheme shows great prospects of
performing well in this area as it had performed in solving other NP-complete
problems in other areas of research. The results obtained from this experiment shows
that, LCA-based scheduling algorithm performed better than the FCFS, LJF and BEF
algorithms. Especially in reducing the average response time and the average
completion time of the jobs. Cloud being a pay-per-user, it implies that, the LCA
saves cost for the cloud users than the LJF, BEF or the FCFS scheduling algorithms,
as it take lesser time for the response and the completion of job processing. The LCA
is a new sport-based optimization technique that has the potential of adaptation in
various fields of research.
Further reseaches are also required to minimize the makespan time for the
scheduling jobs and improve resource allocation within the VMs in the IaaS cloud.
This proposed algorithm can as well be extented in other areas such as search
techniques in big data, chaotic sequences in some engineering design, assignment
problem in graph coloring and known NP-hard problems.
Acknowledgments
The authors would like to express their appreciation for the support of Universiti
Teknologi Malaysia (UTM) Research University Grant Q. J130000.2528.05H87
sponsorship for this research and and the Nigerian Tertiary Education Trust Fund
(TetFund) for their support. We also wish to appreciate Dr. Kashan A. H. for his
assistance on research materials.
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