01568nas a2200133   4500008004100000245008400041210006900125260003600194490001400230520111700244100001701361700002001378856003601398       2016                        eng d00aPower Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer0 aPower Consumption Modeling and Prediction in a Hybrid CPUGPUMIC   aGrenoble, FrancebSpringer LNCS0 vLNCS 98333 aPower consumption is a major obstacle for High Performance Computing (HPC) systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80 % of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://arxiv.org/abs/1601.0596101787nas a2200121   4500008004100000245006100041210006000102260003800162520137900200100001701579700002001596856004901616       2016                        eng d00aPredicting System-level Power for a Hybrid Supercomputer0 aPredicting Systemlevel Power for a Hybrid Supercomputer  aInnsbruck, AustriabIEEEc07/20163 aFor current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://ieeexplore.ieee.org/document/7568420/02543nas a2200133   4500008004100000245009300041210006900134260001200203300001100215520208300226100001702309700002002326856006302346       2016                        eng d00aTowards operator-less data centers through data-driven, predictive, proactive autonomics0 aTowards operatorless data centers through datadriven predictive   c04/2016  a1–143 aContinued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-h window. Our evaluation reveals that if we limit false positive rates to 5 %, we can achieve true positive rates between 27 and 88 % with precision varying between 50 and 72 %. This level of performance allows us to recover large fraction of jobs’ executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. We discuss the feasibility of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available on GitHub.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://link.springer.com/article/10.1007/s10586-016-0564-y00424nas a2200133   4500008004100000245004500041210004300086100001900129700002100148700002000169700002100189700001700210856006300227       2015                        eng d00aA Big Data Analyzer for Large Trace Logs0 aBig Data Analyzer for Large Trace Logs1 aBalliu, Alkida1 aOlivetti, Dennis1 aBabaoglu, Ozalp1 aMarzolla, Moreno1 aSirbu, Alina  uhttp://link.springer.com/article/10.1007/s00607-015-0480-700450nas a2200109   4500008004100000245011200041210006900153260001300222100001700235700002000252856006800272       2015                        eng d00aA Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q0 aHolistic Approach to Log Data Analysis in HighPerformance Comput  bSpringer1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://link.springer.com/chapter/10.1007%2F978-3-319-27308-2_5100409nas a2200109   4500008004100000245005100041210005000092260000900142100001700151700002000168856011100188       2015                        eng d00aTowards Data-Driven Autonomics in Data Centers0 aTowards DataDriven Autonomics in Data Centers  bIEEE1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312140&filter%3DAND%28p_IS_Number%3A7312127%2900459nas a2200145   4500008004100000245004800041210004700089260004400136100001900180700002100199700002000220700002100240700001700261856003500278       2014                        eng d00aBiDAl: Big Data Analyzer for Cluster Traces0 aBiDAl Big Data Analyzer for Cluster Traces  bGI-Edition Lecture Notes in Informatics1 aBalliu, Alkida1 aOlivetti, Dennis1 aBabaoglu, Ozalp1 aMarzolla, Moreno1 aSirbu, Alina  uhttp://arxiv.org/abs/1410.1309