Architecture of Computing Systems (Lecture Notes in Computer ...

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78 M. Bonn and H. Schmeck Uptime-based Distribution As the name of this strategy suggests, this selection algorithm mainly considers the uptime values of the nodes. In practice, a typical node is available for a certain time period, but then it gets shut down with increasing probability. This proceeds every day in the same (or similar) manner. The values �� and ��, which are determined for each node � help to predict how long an asking node will still be available. From these values, a further value, ��, is calculated. The closer the current uptime gets to the average uptime, the shorter are the jobs which have to be selected. If the node’s uptime exceeds its average uptime, the scheduler little by little selects longer-running jobs, depending on the node’s total reliability. To account for the possible different CPU performances among the nodes, the relative benchmark index is finally integrated to scale the average runtime. In other words, �� estimates how long the average runtime �� of a job type � maximally should be, to get a job of this type successfully done by the considered node before it becomes unavailable. |�� −�� = � �|, �� with �� −1,1� �� 1� � |�� −��|, �� = �� To estimate, which job type has to be selected, the average type runtimes are sorted (having �� ,� � �) and the corresponding mean values are calculated: �� = � � �� Finally, the ultimate run length target value �� is calculated: �� = �� −2,2� where �� = � �� , |�� − �� | � |�� − �� | �� , |�� − �� | � |�� − �� | with |�� − �� | = min �� |�� − ��| and |�� − �� | = min|�� − ��| � In other words, from the total set of average job type runtimes and their pair-wise mean values the single value �� is selected which is closest to the previous calculated ideal value ��. Then the individual job � � is selected and delivered which minimizes �� − �� . For clarity, the whole strategy is explained by an example now. We assume jobs of four types/users �� ,…,�� being in the data base. We also assume the following average runtimes (in minutes): �� = 10, �� = 60, �� = 180, �� = 200 Furthermore, the following monitoring values of an asking node are assumed to be estimated so far: �� = 240, �� = 220, � = 2, � = 0
The JoSchKa System 79 This results in �� = �240 − 220� �2=40. The average job runtime closest to this value is �� =60, the closest mean value is �� = � �� =35. So we get �� = 35. Because of �� = �� rnd�−2,2�, the node gets a job of the (randomly chosen) type �� or ��. Fig. 2 explains the example. |�� − ��| ��1 ��2 ��3 ��1 x ��2 x x �� = |�� − ��| ∙ 2 Fig. 2. Uptime-based distribution example Due to the usage of the mean values and the randomization, the system performs a stochastic selection between the two best suited job types, if the ideal target value �� resides in the mean of two neighbored job types. The example shows also, why the largest average runtime (�� , in the example �� ) is excluded when calculating ��. Otherwise, the nodes with the largest remaining uptime would work for the user with the longest running jobs only. In other words, a node gets • a job of type �� if �� lies in the interval � � or • a job of type �� or �� (selected randomly), if �� lies in the interval � �,��. The interval bounds for the example above are graphically shown in Fig. 3. In general, the intervals are described as follows (� types, � ��1, … , � − 1�): �� 3 • �� =� � �� , � �� , where �� =�0, � � �� • ��,�� =� � �� , � �� , where ��,� =� � � �� ,∞� Fig. 3. Interval bounds example �� 4 � 1 � 1,2 � 2 � 2,3 � 3 � 3,4 �� 3 �� 4 �� 1 ��2 ��3 ��1 x ��2 x x ��
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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6 J. Mische et al. 4.1 Instruction
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8 J. Mische et al. Additionally the
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10 J. Mische et al. % of unused pip
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12 J. Mische et al. % of cycles spe
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14 J. Mische et al. 16. Lickly, B.,
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16 G. Aşılıoğlu, E.M. Kaya, and
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26 T.B. Preußer, P. Reichel, and R
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Page 62 and 63: 50 J. Zeppenfeld and A. Herkersdorf
Page 74 and 75: 62 B. Jakimovski, B. Meyer, and E.
Page 86 and 87: 74 M. Bonn and H. Schmeck Fig. 1. J
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128 M. Kayaalp et al. In a supersca
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130 M. Kayaalp et al. INSTRUCTION 1
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132 M. Kayaalp et al. time. Alterna
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134 M. Kayaalp et al. Fig. 3. Numbe
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136 M. Kayaalp et al. The results o
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Efficient Transaction Nesting in Ha
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140 Y. Liu et al. HTMs include TCC
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142 Y. Liu et al. rollback T0 begin
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144 Y. Liu et al. Processor core Pr
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146 Y. Liu et al. 5.2 Results and A
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148 Y. Liu et al. decreases, partia
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Decentralized Energy-Management to
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152 B. Becker et al. Furthermore, t
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154 B. Becker et al. An optimizing
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156 B. Becker et al. smart-home man
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158 B. Becker et al. freedom like w
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160 B. Becker et al. Power [W] 4500
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EnergySaving Cluster Roll: Power Sa
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164 M.F. Dolz et al. Themodulequeri
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166 M.F. Dolz et al. This daemon al
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168 M.F. Dolz et al. been submitted
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170 M.F. Dolz et al. On the other h
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172 M.F. Dolz et al. at the inactiv
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Effect of the Degree of Neighborhoo
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176 T. Abdullah et al. A zone based
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178 T. Abdullah et al. A consumer/p
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180 T. Abdullah et al. Messages 140
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182 T. Abdullah et al. (except when
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184 T. Abdullah et al. % Matchmakin
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186 T. Abdullah et al. show that th
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188 M. Schindewolf, D. Kramer, and
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200 R. Plyaskin and A. Herkersdorf
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212 M.Y. Qadri, D. Matichard, and K
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A Tightly Coupled Accelerator Infra
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224 F. Nowak and R. Buchty where A
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226 F. Nowak and R. Buchty Fig. 3.
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228 F. Nowak and R. Buchty Table 2.
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230 F. Nowak and R. Buchty Table 4.
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232 F. Nowak and R. Buchty 5.3 Comp
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Optimizing Stencil Application on M
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236 F. Xudong et al. compared to th
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238 F. Xudong et al. stencil comput
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240 F. Xudong et al. threads in the
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242 F. Xudong et al. Speedup 14 12
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244 F. Xudong et al. 5 Related Work
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Abdullah, Tariq 174 Alima, Luc Onan
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