LNCS 0558 -Job Scheduling Strategies for Parallel Processing

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Table of Contents Performance Evaluation with Heavy Tailed Distributions ................... 1 M.E. Crovella SRPT Scheduling for Web Servers ......................................... 11 M. Harchol-Balter, N. Bansal, B. Schroeder, and M. Agrawal An Efficient and Scalable Coscheduling Technique for Large Symmetric Multiprocessor Clusters .............................. 21 A.B. Yoo and M.A. Jette Coscheduling under Memory Constraints in a NOW Environment ......... 41 F. Giné, F. Solsona, P. Hernández, and E. Luque The Influence of Communication on the Performance of Co-allocation ..... 66 A.I.D. Bucur and D.H.J. Epema Core Algorithms of the Maui Scheduler ................................... 87 D. Jackson, Q. Snell, and M. Clement On the Development of an Efficient Coscheduling System ................ 103 B.B. Zhou and R.P. Brent Effects of Memory Performance on Parallel Job Scheduling ............... 116 G.E. Suh, L. Rudolph, and S. Devadas An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration ....................... 133 Y. Zhang, H. Franke, J.E. Moreira, and A. Sivasubramaniam Characteristics of a Large Shared Memory Production Workload ......... 159 S.-H. Chiang and M.K. Vernon Metrics for Parallel Job Scheduling and Their Convergence ............... 188 D.G. Feitelson Author Index ............................................................ 207
Agrawal, M., 11 Bansal, N., 11 Brent, R.P., 103 Bucur, A.I.D., 66 Chiang, S.H., 159 Clement, M., 87 Crovella, M.E., 1 Devadas, S., 116 Epema, D.H.J., 66 Feitelson, D.G., 188 Franke, H., 133 Giné, F., 41 Harchol-Balter, M., 11 Hernández, P., 41 Author Index Jackson, D., 87 Jette, M.A., 21 Luque, E., 41 Moreira, J.E., 133 Rudolph, L., 116 Schroeder, B., 11 Sivasubramaniam, A., 133 Snell, Q., 87 Solsona, F., 41 Suh, G.E., 116 Vernon, M.K., 159 Yoo, A.B., 21 Zhang, Y., 133 Zhou, B.B., 103
Page 1 and 2: Lecture Notes in Computer Science 2
Page 3 and 4: Dror G. Feitelson Larry Rudolph (Ed
Page 5: Preface This volume contains the pa
Page 9 and 10: 2 M.E. Crovella 2 Evidence The evid
Page 11 and 12: 4 M.E. Crovella when X is a heavy t
Page 13 and 14: 6 M.E. Crovella delay [43] although
Page 15 and 16: 8 M.E. Crovella 6. Andrei Broder, R
Page 17 and 18: 10 M.E. Crovella 43. R. W. Weber. O
Page 19 and 20: 12 M. Harchol-Balter et al. indepen
Page 21 and 22: 14 M. Harchol-Balter et al. - Given
Page 23 and 24: 16 M. Harchol-Balter et al. Since t
Page 25 and 26: 18 M. Harchol-Balter et al. - There
Page 27 and 28: 20 M. Harchol-Balter et al. 31. S.
Page 29 and 30: 22 A.B. Yoo and M.A. Jette componen
Page 31 and 32: 24 A.B. Yoo and M.A. Jette The DCS
Page 33 and 34: 26 A.B. Yoo and M.A. Jette report o
Page 35 and 36: 28 A.B. Yoo and M.A. Jette A few ne
Page 37 and 38: 30 A.B. Yoo and M.A. Jette The task
Page 39 and 40: 32 A.B. Yoo and M.A. Jette if (the
Page 41 and 42: 34 A.B. Yoo and M.A. Jette Mean Job
Page 43 and 44: 36 A.B. Yoo and M.A. Jette executio
Page 45 and 46: 38 A.B. Yoo and M.A. Jette one of t
Page 47 and 48: 40 A.B. Yoo and M.A. Jette 24. S. P
Page 49 and 50: 42 F. Giné etal. be made taking im
Page 51 and 52: 44 F. Giné etal. Execution time(s)
Page 53 and 54: 46 F. Giné etal. nization grows mo
Page 55 and 56: 48 F. Giné etal. Some studies [24,
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50 F. Giné etal. every time a page
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52 F. Giné etal. Local RQ[∞]:=ta
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54 F. Giné etal. tasks, small and
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56 F. Giné etal. - Slowdown: It is
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58 F. Giné etal. Fig. 6 shows the
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60 F. Giné etal. 5/64 5/32 5/16 5/
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62 F. Giné etal. 30000 25000 20000
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64 F. Giné etal. In this paper, a
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The Influence of Communication on t
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68 A.I.D. Bucur and D.H.J. Epema th
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70 A.I.D. Bucur and D.H.J. Epema by
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72 A.I.D. Bucur and D.H.J. Epema ut
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74 A.I.D. Bucur and D.H.J. Epema In
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76 A.I.D. Bucur and D.H.J. Epema Ta
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78 A.I.D. Bucur and D.H.J. Epema ba
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80 A.I.D. Bucur and D.H.J. Epema Av
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82 A.I.D. Bucur and D.H.J. Epema ex
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84 A.I.D. Bucur and D.H.J. Epema fr
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86 A.I.D. Bucur and D.H.J. Epema 2.
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88 D. Jackson, Q. Snell, and M. Cle
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100 D. Jackson, Q. Snell, and M. Cl
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102 D. Jackson, Q. Snell, and M. Cl
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104 B.B. Zhou and R.P. Brent divide
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106 B.B. Zhou and R.P. Brent though
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108 B.B. Zhou and R.P. Brent s l o
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110 B.B. Zhou and R.P. Brent It is
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112 B.B. Zhou and R.P. Brent s l o
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114 B.B. Zhou and R.P. Brent simult
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Effects of Memory Performance on Pa
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118 G.E. Suh, L. Rudolph, and S. De
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134 Y. Zhang et al. We analyze thre
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136 Y. Zhang et al. Our modeling pr
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138 Y. Zhang et al. Total CPU time
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140 Y. Zhang et al. 3 The Impact of
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142 Y. Zhang et al. Average job wai
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144 Y. Zhang et al. 4. FillMatrix:
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146 Y. Zhang et al. not violate any
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148 Y. Zhang et al. Average job slo
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150 Y. Zhang et al. Average job slo
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152 Y. Zhang et al. The migration f
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154 Y. Zhang et al. improvement is
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156 Y. Zhang et al. Loss of capacit
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158 Y. Zhang et al. 10. B. Gorda an
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160 S.-H. Chiang and M.K. Vernon sc
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162 S.-H. Chiang and M.K. Vernon Ta
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Table 4. Total Monthly Processor an
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166 S.-H. Chiang and M.K. Vernon be
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168 S.-H. Chiang and M.K. Vernon to
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170 S.-H. Chiang and M.K. Vernon Pr
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172 S.-H. Chiang and M.K. Vernon cu
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174 S.-H. Chiang and M.K. Vernon me
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178 S.-H. Chiang and M.K. Vernon ac
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182 S.-H. Chiang and M.K. Vernon pe
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184 S.-H. Chiang and M.K. Vernon pr
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186 S.-H. Chiang and M.K. Vernon ch
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Metrics for Parallel Job Scheduling
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190 D.G. Feitelson cummulative prob
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192 D.G. Feitelson Downey has propo
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194D.G. Feitelson LANL-CM5: jobs pe
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196 D.G. Feitelson average response
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198 D.G. Feitelson with very long j
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200 D.G. Feitelson avg bnd-sld 600
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202 D.G. Feitelson 95% confidence i
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204D.G. Feitelson faster convergenc
Page 213:
Agrawal, M., 11 Bansal, N., 11 Bren
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LNCS 0558 -Job Scheduling Strategies for Parallel Processing

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