Purpose of the study:The real-time task scheduling on multiprocessor system is known as an NP-hard problem. This paper proposes a new real-time task scheduling algorithmwhich considers the communication time between processors and the execution order between tasks.
Methodology:Genetic Algorithm (GA)with Adaptive Weight Approach (AWA) is used in our approach.
Main Findings:Our approach has two objectives. The first objective is to minimize the total amount of deadline-miss. And the second objective is to minimize the total number of processors used.
Applications of this study:For two objectives,the range of each objective is readjusted through Adaptive Weight Approach (AWA) and more useful result is obtained.
Novelty/Originality of this study:This study never been done before.This study also wasprovided current information about scheduling algorithm and heuristics algorithm

Yokoyama, Takanori

Yoo, Myungryun

English

Gyandhara International Academic Publication (GIAP): Journals

International Journal of Students’ Research in Technology & Management 
eISSN: 2321-2543, Vol 6, No 1, 2018, pp 14-17 
https://doi.org/10.18510/ijsrtm.2018.613     
14 |www.ijsrtm.in                                                                                                                                                  ©Authors 
GENETIC ALGORITHM WITH TWO OBJECTIVE FOR REAL-TIME TASK 
SCHEDULING WITH COMMUNICATION TIME 
1*Myungryun Yoo, 2Takanori Yokoyama 
1 2Department of Computer Science, Tokyo City University, Japan 
*myoo@tcu.ac.jp, tyoko@tcu.ac.jp  
Article History: Received on 17th February, Revised on 01st March, Published on 04th April 2018 
Abstract 
Purpose of the study: The real-time task scheduling on multiprocessor system is known as an NP-hard problem. This paper 
proposes a new real-time task scheduling algorithm which considers the communication time between processors and the 
execution order between tasks.  
Methodology: Genetic Algorithm (GA) with Adaptive Weight Approach (AWA) is used in our approach. 
Main Findings: Our approach has two objectives. The first objective is to minimize the total amount of deadline-miss. And 
the second objective is to minimize the total number of processors used. 
Applications of this study: For two objectives, the range of each objective is readjusted through Adaptive Weight Approach 
(AWA) and more useful result is obtained.  
Novelty/Originality of this study: This study never been done before. This study also was provided current information 
about scheduling algorithm and heuristics algorithm. 
Keywords: real-time task scheduling algorithm; two objective genetic algorithm; adaptive weight approach; 
communication time; execution order 
INTRODUCTION 
Several scheduling algorithms based on Rate Monotonic (RM) and Earliest Deadline First (EDF) are proposed for hard real-
time system with uniprocessor (Diaz, Garcia & Lopez, 2004; Bernat, Burns & Liamosi, 2001). Every tasks are finished their 
execution before their deadline in these algorithms. However, the overloaded situation is not considered in these algorithms.  
Recently, a lot of soft real time applications are being used and the scheduling algorithm for soft real-time system is needed.  
In soft real-time system, some deadline-miss is acceptable. Figure 1 represents the definition of deadline-miss.  
 
 Figure 1: The definition of deadline-miss 
Several algorithms for soft real time system are proposed (Kim, Lee & Lee, 1997; Yoo, 2016). However, these algorithms 
have some improvement in deadline-miss under the overloaded situation. Furthermore, the scheduling problem on 
multiprocessor is an NP-hard problem (Yalaoui & Chu, 2002). Consequently algorithms based on heuristics have been 
proposed.   
LITERATURE REVIEW 
Recently, several scheduling algorithms using Genetic Algorithm (GA) are proposed for single objective (Mitra & 
Ramanathan, 1993; Lin & Yang, 1999; Monnier, Beauvais & Deplanche, 1998). These algorithms have only one objective 
such as minimizing cost, finish time, deadline-miss.  
Oh and Wu proposed GA for task scheduling on multiprocessors (Oh & Wu, 2004). Their approach has two objectives; to 
minimize the total amount of deadline-miss and the total number of processors used. However this algorithm did not consider 
the confliction between objectives, so called Pareto Optimum.  
In this paper, we propose a new scheduling algorithm using Genetic Algorithm (GA) for in soft real-time multiprocessor 
system with the communication time between processors and the execution order between tasks. The objective of proposed 
approach is to minimize the total amount of deadline-miss and the total number of processors used.  
METHODOLOGY  
Our scheduling problem is defined as to assign tasks to homogeneous multiprocessors with communication time and the 
objective of scheduling is to minimize the total number of processors used f1 and the total amount of deadline-miss f2. Figure2 
represents the definition of our scheduling problem. 
International Journal of Students’ Research in Technology & Management 
eISSN: 2321-2543, Vol 6, No 1, 2018, pp 14-17 
https://doi.org/10.18510/ijsrtm.2018.613     
15 |www.ijsrtm.in                                                                                                                                                  ©Authors 
 
Figure 2: The definition of scheduling problem 
The proposed approach is based on genetic algorithm (GA). In our GA, a chromosome V represents the information of task 
assignment to processor and execution order. A chromosome V is consisted of two rows. The above row V1 represents 
execution order and the below row V2 represents assignment information. The length of a chromosome is the total number 
of tasks. The execution order should be satisfied a precedence constraints on the given task graph.  
Figure 3 represents the example of a chromosome.  
 
Figure 3: The example of a chromosome 
In Figure 3, the matrix (2 by the total number of tasks) is basically filled with a random decimal number. The random number 
of V1 means the execution order. Firstly, task3 is executed. And then task1 will be started. The third execution task is task2. 
The random number of V2 means the assigned processor number. The task1 and task4 are executed on the processor2. The 
task2 is executed on the processor1. However, the total number of processor is not fixed. It is set by initial value in the step2 
of encoding procedure and changed automatically in during of loop (generation of GA). We can obtain the final total number 
of processor after the step2 of encoding procedure.  
For another operation of GA, we use one cut crossover and mutation of only one bit in V2. And we use roulette wheel 
selection. 
For the evaluation of two objective f1 and f2, the following equation is used.  
𝑒𝑣𝑎𝑙(𝑉) =
1
𝐹(𝑉)
 
                                                                                                    =
1
∑
𝑓𝑖(𝑉)
𝑓𝑖
𝑚𝑎𝑥
−𝑓𝑖
𝑚𝑖𝑛
2
𝑖=1
     (1) 
 
FINDINGS / RESULTS 
We evaluate the performance of proposed algorithm (two-GA) through several simulation. We compared proposed two_GA 
with Oh-Wu’s algorithm by Oh and Wu.  
Table 1 shows the comparisons of results of randomly generated task graph with 50 tasks. Tasks’ computation time, deadline 
and communication time between processors are generated randomly based on normal distribution and exponential 
distribution.  
 
International Journal of Students’ Research in Technology & Management 
eISSN: 2321-2543, Vol 6, No 1, 2018, pp 14-17 
https://doi.org/10.18510/ijsrtm.2018.613     
9 |www.ijsrtm.in                                                                                                                                                  ©Authors 
Table 1: Computation results (50 tasks) 
Terms 
Normal distribution Exponential distribution 
Oh-Wu’s 
Algorithm 
Two_GA 
Oh-Wu’s 
Algorithm 
two_GA 
Total number of processors 35 32 32 28 
Final finish time 47 52 46 52 
Computing times (msec) 128 132 135 136 
Average utilization of processors 0.47568 0.49376 0.43674 0.47253 
In the case of no deadline-miss inclusively, the computing time of proposed two_GA is longer than that of Oh-Wu’s 
algorithm. However, the total number of used processors is fewer than that of Oh-Wu’s algorithm. Also, the average 
utilization of processors of our algorithm is higher than that of Oh-Wu’s algorithm. 
Table 2 shows the comparisons of results of randomly generated task graph with 100 tasks under the same condition of Table 
1. In this case, we can see the same results to Table 1. 
Table 2: Computation results (100 tasks) 
Terms 
Normal distribution Exponential distribution 
Oh-Wu’s 
Algorithm 
two_GA 
Oh-Wu’s 
Algorithm 
two_GA 
Total number of processors 36 34 36 32 
Final finish time 162 171 198 199 
Computing times (msec) 455 498 478 503 
Average utilization of processors 0.40582 0.43392 0.43363 0.48823 
Figure 4, Figure 5, Figure 6 and Figure 7 represent the Pareto solution of proposed two_GA and that of Oh-Wu’s algorithm. 
In these figures, the Pareto line by proposed two_GA is closer to ideal point (1, 0) than that of Oh-Wu’s algorithm. 
CONCLUSION 
In this paper, we proposed a new task scheduling algorithm using GA. We consider the scheduling problem on soft real-time 
multiprocessor system with the communication time between processors and the execution order between tasks. The 
objective of proposed approach is to minimize the total amount of deadline-miss and the total number of processors used.  
Our GA is combined Adaptive Weight Approach (AWA) to satisfy two objectives simultaneously. From the simulation tests, 
the performance of the proposed two_GA are better than that of other algorithms.  
LIMITATION AND STUDY FORWARD 
We plan to design real-time tasks scheduling algorithm on heterogeneous multiprocessors system for the next step of study. 
ACKNOWLEDGEMENT 
This work supported in part by JSPSKAKENHI Grant Number 15K00084. 
REFERENCES 
1. Bernat G., Burns, A. & Liamosi, (2001). A. Weakly Hard Real-Time Systems. Transactions on Computer Systems, 
50(4), 308-321. https://doi.org/10.1109/12.919277 
2. Diaz J. L., Garcia, D. F. & Lopez, J. M. (2004). Minimum and Maximum Utilization Bounds for Multiprocessor Rate 
Monotonic Scheduling. IEEE Transactions on Parallel and Distributed Systems,15(7), 642-653. 
https://doi.org/10.1109/TPDS.2004.25 
3. Kim M. H., Lee, H. G. & Lee, J. W. (1997). A Proportional-Share Scheduler for Multimedia Applications. Proc. of 
Multimedia Computing and Systems, 484-491. 
4. Lin, M., & Yang, L. (1999). Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in A Multi-processor 
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International Journal of Students’ Research in Technology & Management 
eISSN: 2321-2543, Vol 6, No 1, 2018, pp 14-17 
https://doi.org/10.18510/ijsrtm.2018.613     
10 |www.ijsrtm.in                                                                                                                                                  ©Authors 
5. Mitra, H., & Ramanathan, P. (1993). A Genetic Approach for Scheduling Non-preemptive Tasks with Precedence and 
Deadline Constraints. In: Proceedings of the 26th Hawaii International Conference on System Sciences, 556–564. 
https://doi.org/10.1109/HICSS.1993.284070 
6. Monnier, Y., Beauvais, J. P. & Deplanche, A. M. (1998). A Genetic Algorithm for Scheduling Tasks in a Real-Time 
Distributed System. Proc. of 24th Euromicro Conference, 708-714. https://doi.org/10.1109/EURMIC.1998.708092 
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GENETIC ALGORITHM WITH TWO OBJECTIVE FOR REAL-TIME TASK SCHEDULING WITH COMMUNICATION TIME

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