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71 The GA with Various Mutation Rates 1.00000 Fitness Value f(x) 0.50000 0.00000 1 51 101 151 201 251 301 351 401 451 501 Generation 0.00 0.02 0.20 0.40 FIGURE 4-4 The GA with various mutation rates 4.2.5 Experiment 4: Parallel Execution on the Grid Computing Environment The aim of this experiment is to evaluate the influence of the grid computing environment to the resultant solutions. The experiment tests three different models. The first model uses a single machine to perform the centralized course scheduling strategy as introduced in section 3.4.1. The centralized course scheduling program is used to test a centralized execution that schedules for all courses. The second model also uses a single machine, but both the centralized course scheduling program and the decentralized course scheduling program are used for a serial execution. First, the centralized course scheduling program schedules for all shared resources, and then one after another the decentralized course scheduling program schedules for the remaining resources of each faculty. Finally, the third model uses a grid computing environment for parallel execution. First, the centralized course scheduling program is executed on a machine, and then the decentralized course scheduling program is executed in parallel on remote machines. Both the centralized course scheduling program and the decentralized course scheduling program will set up with the following GA settings:
72 - Population size : 10 - Crossover rate : 0.5 - Mutation rate : 0.02 - Selection method : Steady state - Hard constraint weight W 1 : 0.75 - Soft constraint weight W 2 : 0.25 The first and second models are performed on a Pentium IV 1.7 GHz machine. On the other hand, the third model is performed on a gird computing environment of 3 machines, as shown in Figure 3-23. The Central Manager Host m1 is a Pentium III 700 MHz machine. The remote machines m2 and m3 are Pentium IV 1.7 GHz machines. Figure 4-5 presents a chart of the average execution time of each model after 5 runs. Each model is executed until the GA finds a resultant solution. Execution Time vs Models Parallel Execution on the Grid 439.6 Model Serial Execution 852.6 Centralized Execution 2842.6 0 500 1000 1500 2000 2500 3000 Execution Time in Seconds FIGURE 4-5 The execution time versus various models The first model is slower than the second model. The first model has a global view of the whole data, so it should have given a resultant solution within a short time interval. However, it gave an unexpected result. This is because when the whole data are centralized to be processed on a single machine, the size of the problem becomes
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COURSE SCHEDULING IN MULTIPLE FACUL
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TABLE OF CONTENTS Page Abstract (in
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LIST OF TABLES Table Page 2-1 Cours
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LIST OF FIGURES (CONTINUED) Figure
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2 The classrooms are sometime share
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4 Classroom i Time-slot Hour Mon Tu
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CHAPTER 2 LITERATURE REVIEW In this
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9 teach effectively. However, in so
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11 The set of vertexes are consider
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13 Gueret et al. have implemented a
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15 When the atoms are at a high tem
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17 The outline of the basic genetic
Page 27 and 28: 19 As shown in Figure 2-6, first of
Page 29 and 30: 21 2.3.2.1 Crossover After we have
Page 31 and 32: 23 these chromosomes. According to
Page 33 and 34: 25 2.4.1 Application Considerations
Page 35 and 36: 27 Southern California, the Argonne
Page 37 and 38: 29 2.4.3.2 Application Programming
Page 39 and 40: 31 2.5 Summary The course schedulin
Page 41 and 42: 34 3.1.5 Phase 5: Measurement a) To
Page 43 and 44: 36 All the above problems can be pr
Page 45 and 46: 38 Client at a faculty Client at th
Page 47 and 48: 40 The Grid system is only used by
Page 49 and 50: 42 3.6 The Proposed Genetic Algorit
Page 51 and 52: 44 Chromosome x i Fitness = f(x i )
Page 53 and 54: 46 A population is a list of n chro
Page 55 and 56: 48 Lecturers register their busy ti
Page 57 and 58: 50 A course is booked to the time-l
Page 59 and 60: 52 As mentioned in the previous sec
Page 61 and 62: 54 GIIS from the client machine m1.
Page 63 and 64: 56 The complete source code for man
Page 65 and 66: 58 3.8.2.2 Stage 2 After the centra
Page 67 and 68: 60 3.8.3.3 The Gatekeeper The gatek
Page 69 and 70: 62 TABLE 4-1 (CONTINUED) Class Seme
Page 71 and 72: 64 26 lecturers are assigned to tea
Page 73 and 74: 66 TABLE 4-2 (CONTINUED) Course Sec
Page 75 and 76: 68 This result shows that the GA ra
Page 77: 70 should revise the chromosome rep
Page 81 and 82: 74 for network communication is muc
Page 83 and 84: 76 TABLE 4-5 Timetable created by t
Page 85 and 86: CHAPTER 5 CONCLUSION 5.1 Conclusion
Page 87 and 88: REFERENCES 1. Alkan, A. and Ozcan,
Page 89 and 90: 83 20. Burke, E. K. and Newall, J.
Page 91 and 92: 85 38. Globus Toolkit [Online]. Ava
Page 93 and 94: 88 This section presents the struct
Page 95 and 96: 90 A.5 Building TABLE A-5 Building
Page 97 and 98: 92 A.10 Program TABLE A-10 Program
Page 99 and 100: 94 A.14 Timetable TABLE A-14 Timeta
Page 101 and 102: 96 This section presents in detail
Page 103 and 104: 98 TABLE B-2 Group, user ID and pas
Page 105 and 106: 100 broadcastdelay 0.008 authentica
Page 107 and 108: 102 mycapw [enter] During the above
Page 109 and 110: 104 service gsigatekeeper { socket_
Page 111 and 112: 106 B.3.3 Starting the MDS on All o
Page 113 and 114: 108 B.4 Checking the Installation T
Page 115 and 116: 110 This section introduces the ste
Page 117 and 118: 112 C.3.2 Configuration This sectio
Page 119 and 120: 114 [root@m1 root]#grid-proxy-info
Page 121 and 122: 116 The system will automatically p
Page 123 and 124: 118 C.6 Installing JDBC Driver on L
Page 125 and 126: 120 This section presents how to co
Page 127 and 128: 122 All the following files are com
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124 } } } try { ctx.close(); } catc
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126 }//for }else if(attribute.equal
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128 public static void main(String
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130 System.out.println(CentralizedS
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132 gassJob[jobCount]=new GassJob(t
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134 /** * Init job out listeners fo
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136 // Start GASS server if (! star
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138 // waits for DONE or FAILED sta
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140 //get RSL of Job having index i
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142 class Job { private String jobn
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145 BIOGRAPHY Name : Mr. Nguyen Con
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