Available online at www.sciencedirect.com
Procedia Engineering 50 (2012) 778 – 785
International Conference on Advances Science and Contemporary Engineering 2012 (ICASCE 2012)
A Priority based Job Scheduling Algorithm in Cloud Computing Shamsollah Ghanbaria,*,Mohamed Othmana,b,* a
Labratory of Computational Science and Mathematical Physics Institute For Mathematical †Research(INSPEM) b
Dept of Communication Tech and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia
Abstract Nowadays cloud computing has become a popular platform for scientific applications. Cloud computing intends to share a large scale resources and equipments of computation, storage, information and knowledge for scientific researches. Job scheduling algorithms is one of the most challenging theoretical issues in the cloud computing area. Some intensive researches have been done in the area of job scheduling of cloud computing. In this paper we have proposed a new priority based job scheduling algorithm (PJSC) in cloud computing. The proposed algorithm is based on multiple criteria decision making model.
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Bina Nusantara ©University. 2012 Elsevier B.V...Selection and peer-review under responsibility of Bin Nusantara University keywords: cloud computing, job scheduling, priority, consistency, AHP.
1.
Introduction
Several job scheduling algorithms have been proposed in distributed computing area [7-16, 22, 23]. Most of them can be applied in the cloud environment with suitable verifications [7, 8, 10-13, 22, 23]. The main goal of job scheduling is to achieve a high performance computing and the best system throughput. Traditional job scheduling algorithms are not able to provide scheduling in the cloud environments. According to a simple classification [8], job scheduling algorithms in cloud computing can be categorized into two main groups; Batch mode heuristic scheduling algorithms(BMHA) and online mode heuristic algorithms. In BMHA, Jobs are queued and collected into a set when they arrive in the system. The scheduling algorithm will start after a fixed period of time. The main examples of BMHA based algorithms are; First Come First Served scheduling algorithm (FCFS), Round Robin scheduling algorithm (RR), Min–Min algorithm and Max–Min algorithm. By On-line mode heuristic scheduling algorithm, Jobs are scheduled when they arrive in the system. Since the cloud environment is a heterogeneous system and the speed of each processor varies quickly, the on-line mode heuristic scheduling algorithms are more appropriate for a cloud environment. Most fit task scheduling algorithm (MFTF) is suitable example of On-line mode heuristic scheduling algorithm [8]. Priority of jobs is an important issue in scheduling because some jobs should be serviced earlier than other those jobs can’t stay for a long time in a system. A suitable job scheduling algorithm must consider priority of jobs. To address this problem some researchers have considered priority of jobs scheduling algorithm [12, 14, 16]. Those researches have focused on a few criteria of jobs in scheduling. In cloud environments we always face a wide variety of attributes that should be considered. It means a particular job scheduling algorithm in * Corresponding author. Tel:+60-3-89461707 E-mail address:
[email protected];
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1877-7058 © 2012 Elsevier B.V...Selection and peer-review under responsibility of Bin Nusantara University doi:10.1016/j.proeng.2012.10.086
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Shamsollah Ghanbari and Mohamed Othman / Procedia Engineering 50 (2012) 778 – 785
cloud environments should pay attention to multi-attribute and multi-criteria properties of jobs. There are several multi-criteria decision-making (MCDM) and multi-attribute decision-making (MCDM) which are based on mathematical modelling. A pair-wise comparison based MADM/MCDM method was developed by T.Saaty [1-4] in 1980, the model was named Analytical Hierarchy Process (AHP). During these three past decades AHP have found a number of applications in various fields. AHP also is a suitable method for priority based problems such as scheduling with various attributes and alternatives [5, 21]. The main objective of this paper is to propose a new priority based job scheduling algorithm called PJSC. The proposed algorithm is based on the theory of AHP. We also discuss about some important aspects of PJSC such as complexity and finish time. The rest of this paper is organized as following sections: section 2 explains the AHP, section 3 proposes a new priority based algorithm for job scheduling of in cloud computing based on AHP, in section 4 we analyze the proposed model and discusses about some issues related to proposed algorithm such as complexity, consistency and makespan, finally in section 5 provides some numerical examples of proposed model. 2.
Analytical Hierarchy Process ( AHP )
In this section we explain the Analytical Hierarchy Process briefly. It is a multi-criteria decision-making (MCDM) and multi-attribute decision-making (MCDM) model. Basically architecture of AHP is consisted of three levels which are objective level, attributes level and alternatives level respectively. The foundation of AHP is comparison matrix which can be shown as Eq. (1).
(1) Each entry in the matrix A is positive (
). Also A is a square matrix (
). For any arbitrary comparison
matrix such as A we can compute a vector of weights such as Relationship between A and
associated with A.
can be shown as Eq.(2).
(2) An essential step in AHP is to calculate vector of weights Eq. (3) [1-4, 17, 9, 20, 21].
. Vector of weights can be computed through the
(3) Actually Eq. (3)
is denoted the principal eigenvalue of A and
eigenvector. If A is absolutely consistent then consistency ratio (CR) as Eq. (5).
is denoted the corresponding
. In this case A will be consistent. Saaty has defined
(5) In Eq. (5), RI is denoted the random index(RI), RI can be calculated randomly based on rank of comparison matrix. Table 1 indicates some values for RI which were calculated by Saaty[1-4]. Also CI in Eq. (5) can be calculated by Eq. (6).
(6)
n RI
3 0.58
4 0.9
Table 1. Random 5 6 1.12 1.24
Index(RI)
7 1.32
8 1.41
9 1.45
10 1.49
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Shamsollah Ghanbari and Mohamed Othman / Procedia Engineering 50 (2012) 778 – 785
Saaty also indicated that if CR
