CONSUMABILITY ANALYSIS of BATCH PROCESSING SYSTEMS FLAVIO FRATTINI Tesi Di Dottorato Di Ricerca
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UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II Dottorato di Ricerca in Ingegneria Informatica ed Automatica Comunità Europea A. D. MCCXXIV Fondo Sociale Europeo CONSUMABILITY ANALYSIS OF BATCH PROCESSING SYSTEMS FLAVIO FRATTINI Tesi di Dottorato di Ricerca (XXVI Ciclo) Marzo 2014 Il Tutore Il Coordinatore del Dottorato Prof. Stefano Russo Prof. Francesco Garofalo Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione + Via Claudio, 21 - 80125 Napoli - ([+39] 081 76 83813 - 4 [+39] 081 76 83816 CONSUMABILITY ANALYSIS OF BATCH PROCESSING SYSTEMS By Flavio Frattini SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY AT FEDERICO II UNIVERSITY OF NAPLES VIA CLAUDIO 21, 80125 { NAPOLI, ITALY MARCH 2014 c Copyright by Flavio Frattini, 2014 Per aspera ad astra. Abstract The use of large scale processing systems has exploded during the last decade and now they are indicated for significantly contributing to the world energy consumption and, in turn, environmental pollution. Processing systems are no more evaluated only for their performance, but also for how much they consume to perform at a certain level. Those evaluations aim at quantifying the energy efficiency conceived as the relation between a performance metric and a power consumption metric, disregarding the malfunction that commonly happens. The study of a real 500-nodes batch system shows that 9% of its power consumption is ascribable to failures compromising the execution of the jobs. Also fault tolerance techniques, commonly adopted for reducing the frequency of failure occurrences, have a cost in terms of energy consumption. This dissertation introduces the concept of consumability for processing systems, en- compassing performance, consumption and dependability aspects. The idea is to have a unified measure of these three main aspects. The consumability analysis is also described. It is performed by means of a hierarchical stochastic model that considers the three aspects simultaneously in the process of evaluating the system efficiency and effectiveness. The analysis represents a solution to system owners and administrators that need to evaluate cost-benefit trade-off during the design, development, testing and operational phases. The analysis is illustrated for two case studies based on a real batch processing system. The studies provides a set of guidelines for the consumability analysis of other systems and empirically confirm the importance of contemplating dependability jointly with performance and consumption for making processing systems really energy efficient. iii Acknowledgements I sincerely thank my advisor professor Stefano Russo for mentoring me during the PhD and for being a role model since I was an undergraduate. Many thanks to professor Domenico Cotroneo for his insightful suggestions and support, both for research and life. He was tough and mild at the right times, a great professor and a real friend. I am very grateful to professor Marcello Cinque for guiding me from behind the curtains. He played a key role during my doctorate. I also thank professor Kishor S. Trivedi, mentor during the experience at Duke Uni- versity. Thanks to all (past and current) colleagues of the Mobilab research group and to Rahul Ghosh, office mate at Duke. Acknowledgements I thank all my family. First, I am sincerely thankful toI sincerelymy thank parents, professor Stefano who Russo for mentoring sustained me during the PhD me and for being a role model since I was an undergraduate. Many thanks to professor Domenico Cotroneo for from the beginning. Ciro, Fabrizio and Lucrezia were essentialhis constant during support and suggestions. each He step was tough and with mild at the their right times. I am very grateful to professor Marcello Cinque for guiding me from behind the curtains. He played a key role during my PhD. candor. Thanks to Barbara and Ettore for their incitementI andalso thank professorfortrusting Kishor S. Trivedi, mentor me. during theThanks experience at Duke Univer- sity, North Carolina. to Marcella, always willing. I deeply thank Peppe, who inclinedThanks to all methe colleagues towards of the Mobilab. the I would also studies. like to thank Rahul Ghosh, officemate at Duke. Unfortunately, I am still ignorant. I thank all my family. First, my parents, who sustained me from the beginning. Thanks to Barbara, Peppe, Ettore, Marcella, and Ciro, Fabrizio and Lucrezia. Peppe directed me Last but certainly not least, I warmly thank Ilaria for hertowards support the studies. Unfortunately, and I am encouragement, still ignorant. Last but certainly not least, I warmly thank Ilaria for her support, incitement and (par- and for tolerating most of my faults. tial :-) patience. Naples, March 2014. Naples, March 2014. iv iv Table of Contents List of Tables viii List of Figures ix Introduction 1 1 Energy Efficiency of Processing Systems 8 1.1 Problem Overview . .8 1.2 Strategies and Technologies . 12 1.2.1 Hardware . 15 1.2.2 Software . 19 1.2.3 Management . 22 1.3 Metrics . 36 1.4 Remarks . 39 2 Performance, Consumption and Dependability 42 2.1 Basic Definitions . 42 2.1.1 Performance . 45 2.1.2 Consumption . 46 2.1.3 Dependability . 48 2.2 Performance and Dependability: Performability . 52 2.2.1 Performability Indices . 53 2.2.2 Performability Examples . 55 2.3 Performance, Consumption and Dependability: Consumability . 59 2.4 Discussion . 63 3 Evaluation Approaches 65 3.1 Evaluating Processing Systems . 65 3.2 Measurement Based Approaches . 66 3.2.1 Examples of Measurement Based Evaluation . 69 3.3 Model Based Approaches . 72 3.3.1 Examples of Model Based Evaluation . 73 v 3.4 Scalable Models for Large Processing Systems . 75 4 Consumability Analysis of Batch Processing Systems 78 4.1 Analysis Rationale . 78 4.1.1 Steps of the analysis . 80 4.2 System Modeling . 81 4.2.1 Performance Model . 83 4.2.2 Consumption Model . 86 4.2.3 Failure Model . 87 4.2.4 Consumability Model . 93 4.2.5 Applicability of the Model . 95 4.3 Fault Tolerance Models . 96 4.3.1 Job Checkpointing . 97 4.3.2 Job Replication . 100 4.3.3 Consumption due to Fault Tolerance . 102 4.4 A Metric for Consumability . 103 5 Case Study 1: Consumability of a Scientific Data Center 105 5.1 The S.Co.P.E. Data Center . 105 5.1.1 The Monitoring System . 107 5.2 Data Analysis for Populating the Model . 110 5.2.1 Workload Analysis . 111 5.2.2 Consumption Analysis . 119 5.2.3 Failure Analysis . 122 5.2.4 Fault Tolerance . 129 5.3 Model Validation . 132 5.4 Experimental Results . 134 5.4.1 Performance . 136 5.4.2 Consumption . 137 5.4.3 Availability . 141 5.5 Energy Efficiency and Consumability . 143 5.6 Discussion . 147 6 Case Study 2: Impact of Virtualization on Consumability 150 6.1 Virtualized Processing Systems . 150 6.2 Analyzing a Virtualized Batch System . 152 6.2.1 Performance Analysis . 153 6.2.2 Consumption Analysis . 160 6.2.3 Failure Analysis . 161 6.2.4 Fault Tolerance . 167 6.3 Experimental Results . 169 6.3.1 Impact of VM Size . 170 vi 6.3.2 Impact of Scheduling Strategies . 173 6.3.3 Impact of Fault Tolerance Strategies . 175 6.4 Discussion . 182 Conclusion 185 Bibliography 191 vii List of Tables 1.1 References for the identified classes of the scientific literature on energy effi- ciency. 14 2.1 References defining the main aspects of processing systems and their inter- sections. 43 3.1 Summary of comparison among the three modeling approaches: (i) single monolithic model, (ii) interacting sub-models, and (iii) simulation. 76 5.1 Results of the regression for the consumption. 120 5.2 Transition distributions of the failure model. 122 5.3 Summary of the results for the consumability analysis of the S.Co.P.E. data center. 135 6.1 Provisioning and deprovisioning distributions. 155 6.2 Transition distributions and weights of the virtualized batch processing sys- tem model. 168 6.3 Design of Experiments: factors and respective levels. 169 viii List of Figures 1.1 Estimate of the carbon footprint of ICT in 2010-2020 decade. .9 1.2 Reasons for using eco-responsible practices in IT. 10 1.3 Classification of the scientific literature on energy efficiency in data centers. 14 1.4 Techniques used for reducing the power consumption of microprocessors. 16 1.5 Possible approaches for the consolidation of VMs. 29 1.6 Common model based approach for estimating future behavior and tuning the system. 34 1.7 Classification of energy efficiency metrics. 37 2.1 A set view of consumption, performance and dependability aspects for pro- cessing systems. 43 2.2 The fault-error-failure chain. 48 2.3 Markov reward model, evolution of the system over time (X), reward rate (Z), accumulated reward rate (Y). 55 2.4 Two-processor-parallel-redundant system with imperfect coverage and with repair. 56 2.5 Expected instantaneous reward rate. 58 4.1 Status transitions of a job in a batch processing system. (a) correct execution; (b-e) execution with failures. 84 4.2 SRN representing the performance model of a batch processing system. 84 4.3 SRNs modeling (a) queue failures, (b) running failures, (c) aborts, (d) exiting failures. 91 4.4 SRN modeling the checkpointing operations. 98 4.5 SRN modeling the abort when checkpointing is adopted. 98.