On-the-fly scheduling vs. reservation-based scheduling for unpredictable workflows

International audienceScientific insights in the coming decade will clearly depend on the effective processing of large datasets generated by dynamic heterogeneous applications typical of workflows in large data centers or of emerging fields like neuroscience. In this paper, we show how these big data workflows have a unique set of characteristics that pose challenges for leveraging HPC methodologies, particularly in scheduling. Our findings indicate that execution times for these workflows are highly unpredictable and are not correlated with the size of the dataset involved or the precise functions used in the analysis. We characterize this inherent variability and sketch the need for new scheduling approaches by quantifying significant gaps in achievable performance. Through simulations, we show how on-the-fly scheduling approaches can deliver benefits in both system-level and user-level performance measures. On average, we find improvements of up to 35% in system utilization and up to 45% in average stretch of the applications, illustrating the potential of increasing performance through new scheduling approaches

Validation of Simplified Rack Boundary Conditions for Numerical Data Center Models

As cloud computing and computational needs grow, data centers will continue to become a larger part of our energy load. Proper design and layout is crucial to efficient energy use in data centers. Modeling the rack is one of critical pieces in this design. Often this is done as a black box rather than modeling the rack in detail. Modeling a computer rack as a black box has been done in numerous data center studies, but rarely has it been validated against experimental temperature and velocity data. This study looks at two simplified rack models and compares them against a rack composed of four 10U server simulators. The first model is an open box model that has a heating and fan plate and allows air to flow through the rack. The second model is a black box model that allows no flow through the rack and imposes a constant pressure boundary across the inlet and exhaust. The model adds the proportion of the rack load to the upwind cells at the rack inlet plate to generate the exhaust temperature profile. The models were tested across a range of airflows and rack loads. Agreements were found to be within 3°C and 0.2 m/s on average over all experiments. An interesting finding of this study was the importance of correctly capturing the boundary conditions at the perforated floor tile. Modeling the perforated floor tile as a nozzle using the momentum method described in ASHRAE RP-1009 was found to produce acceptable results for airflow from the perforated floor tile

Modelling the assimilation and value of sensor information systems in data centres

Sensor Information Systems (SIS) refer to any IS that utilises sensor(s) that are directly or indirectly connected to other sensors or sensor networks in order to automate, inform and/or transform a given task or process or appliance. SIS are promoted as one of the best practices to overcome critical data centres issues such as inefficiency of Information Technology (IT) infrastructure usage, rising cost of operations, and the consumption and efficiency of energy. A review of the sensor, IS, and data centre literature shows that there is a dearth of theory driven empirical research on the utilisation of SIS in data centres, the factors that explain variations in applying SIS in data centres and the value of SIS use to data centres. The aim of this study is therefore to address the gap in the current literature and answer research questions. The research was conducted through a mixed method approach consisting of a literature review, exploratory case studies (pilot study) and large scale survey. Drawing from several theories of innovation adoption and value, and the five exploratory case studies, an integrative theoretical framework, which we call as TOIN (Technology, Organisation, Institutional and Natural Environment), was proposed to investigate the factors that explain the variation in the assimilation of SIS and the impact of SIS use on data centre’s operational and environmental performance. A series of hypotheses are developed by linking the TOIN factors to SIS assimilation and value in a two order-based model. The TOIN framework is tested using Partial Least Squares (PLS) path modelling and data collected from a global survey of 205 data centres. The findings indicate that SIS compatibility, perceived SIS risk, green IT orientation, and normative pressure directly influence the level of SIS usage among data centres. In addition, normative pressure, energy pressure, and natural environmental pressure indirectly affect the assimilation of SIS through influencing the organizational conditions for SIS use. These results are mostly sensitive to differences in data centre characteristics including age and type of data centre. Further, the test of the second order model show that the level of actual usage as well as the level of SIS mangers’ knowledge affect the operational and environmental performance of data centre operations including the facility, cooling and power, and computing platforms. The research represents one of the first studies on the use and value of SIS in general and in the context of data centre environment in particular. It makes an original contribution by proposing and validating the TOIN framework which can be used as a theoretical foundation for future and related studies. It also contributes original knowledge regarding how data centres are using SIS to tackle some of the operational, economic and environmental challenges. Thus, the research adds to the body of knowledge on intelligent systems, infrastructure management, green IS and energy informatics. Furthermore, the research extends the current innovation theories by incorporating the natural environment to study the technology use and value and shows the significance of natural environment considerations on organizations’ activities