water-vapor line (near 22.2 GHz) and continuum opacity increase in proportion .... Maddalena, R. 2008, (http://www.gb.nrao.edu/ rmaddale/Weather/index.html).
Astronomical Data Analysis Software and Systems XVII ASP Conference Series, Vol. XXX, 2008 J. Lewis, R. Argyle, P. Bunclarck, D. Evans, and E. Gonzales-Solares, eds.
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GBT Dynamic Scheduling System: Algorithms, Metrics, and Simulations Dana S. Balser1 , C. Bignell2 , J. Braatz1 , M. Clark2 , J. Condon1 , J. Harnett2 , K. O’Neil2 , R. Maddalena2 , P. Marganian2 , M. McCarty2 , E. Sessoms2,3 , A. Shelton2 Abstract. We discuss the scoring algorithm of the Robert C. Byrd Green Bank Telescope (GBT) Dynamic Scheduling System (DSS). Since the GBT is located in a continental, mid-latitude region where weather is dominated by water vapor and small-scale effects, the weather plays an important role in optimizing the observing efficiency of the GBT. We score observing sessions as a product of many factors. Some are continuous functions while others are binary limits taking values of 0 or 1, any one of which can eliminate a candidate session by forcing the score to zero. Others reflect management decisions to expedite observations by visiting observers, ensure the timely completion of projects, etc. Simulations indicate that dynamic scheduling can increase the effective observing time at frequencies higher than 10 GHz by about 50% over one full year. Beta Tests of the DSS during Summer 2008 revealed the significance of various scheduling constraints and telescope overhead time to the overall observing efficiency.
1.
Weather Forecasts and Metrics
The GBT spans a larger range of frequencies than other comparable centimeter/ millimeter single-dish telescopes, and is located in a continental, mid-latitude region where weather is dominated by water vapor and small-scale effects. High frequency observations are both slowed and degraded by atmospheric opacity, wind gusts, and solar heating. Figure 1 plots the atmospheric opacity versus frequency on a typical summer day with 55% cloud cover (Condon 2007). Both water-vapor line (near 22.2 GHz) and continuum opacity increase in proportion to the precipitable water vapor. Under humid conditions hydrosols dominate at most frequencies above 10 GHz. The oxygen lines near 60 GHz are quite opaque and preclude astronomical observations from the ground near the line frequencies. Weather forecasts for Green Bank have been derived from forecasts of vertical weather profiles supplied by the national weather services. Opacities and mean atmospheric temperatures have been calculated as a function of time and frequency (Maddalena 2008). The weather forecast data are used to calculate two metrics that are input into the scoring algorithm: stringency (S) and observing efficiency (ηobs ). The 1
National Radio Astronomy Observatory, Charlottesville, VA 22903-2475, USA
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National Radio Astronomy Observatory, Green Bank, WV, 24944-0002, USA
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Nub Games, Inc., Chapel Hill, NC, USA
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Figure 1. Zenith opacity versus frequency for a typical summer day in Green Bank, WV with precipitable water vapor (pwv) of 1.0 cm, a surface air temperature of 288 K, and 55% cloud cover.
stringency is the total available time divided by the time under which the conditions are met (see Evans et al. 2003). The observing efficiency is the ratio of the integration time needed to make a transit observation in the best weather to the time needed to reach the same sensitivity given the current weather conditions and hour angle (see Condon & Balser 2007; Maddenlena 2008). 2.
Scoring Algorithm
The DSS uses a scoring equation to rank the available observing sessions for a given time based on a variety of factors grouped into four areas: weather, schedule pressure factors, performance limits, and other factors. The scoring equation is given by R = (ηobs S) (Pα Pν ) (ℓeff ℓHA ℓz ℓtr ℓst ) (foos fcom fsg ftp ).
(1)
The weather related parameters in the scoring equation are the observing efficiency (ηobs ) and stringency (S). The schedule “pressure” (P) is a measure of unsatisfied demand. Feedback can be used to equalize pressure across right ascension, frequency band, etc. by favoring sessions having higher pressure values. Feedback is “blind” and competes with observing efficiency, so it reduces efficiency and should be used only as a last resort. Results from simulations indicated that it was necessary to use pressure feedback to equalize pool pressures across right ascension (Pα ) and observing frequency (Pν ).
GBT DSS: Algorithms, Metrics, and Simulations
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A candidate observing session must satisfy all relevant performance limits; otherwise its score will be set to zero (automatic rejection) or so strongly downgraded that it will run only if nothing else is available to fill potential dead time. The performance limits are associated with observing efficiency (ℓeff ), absolute hour angle (ℓHA ), zenith angle (ℓz ), tracking error (ℓtr ), and atmospheric stability (ℓst ). The “other factors” are needed to implement management decisions. An increase in score to encourage observers to be on site (foos ), to favor timely completion of projects (fcom ), to boost the score for higher rated proposals (fsg ), or to complete thesis projects (ftp ). Like feedback they compete with efficiency factors and potentially degrade the overall observing efficiency of the GBT. They should be kept as small as possible consistent with achieving their goals. The scoring algorithm also consists of a packing algorithm to optimize building a 24 hour schedule with observing sessions of differnent length, and a conflict resolution algorithm to detect scheduling conflicts (see Sessoms et al. 2009).
3.
Simulations
Simulations of the DSS have been developed to test and refine the scoring equation and to make predictions using historical weather forecasts. The simulations have evolved over time. Initially they did not include the packing or conflict resolution algorithms. Moreover, they only considered synthetic, unconstrained (open) observing sessions that could be scheduled at any available time. Two other types of observing sessions have now been added: fixed sessions that have to be scheduled at a specific time (e.g., VLBI observations) and windowed sessions that have to be executed within a specified time range (e.g., pulsar monitoring). Simulations that include both unconstrained and constrained observing sessions as well as real observing projects are under development. Here we only discuss the results from simulations that include open sessions using synthetic observing projects. Figure 2 compares the results from two simulations: the traditional scheduling scheme and the DSS. The traditional scheduling scheme schedules one high frequency session and one low frequency session as a pair, typically separated by 2 days, whereupon the high frequency observer selects the best weather day (see Condon & Balser 2007 for details). Plotted are the observing efficiencies versus frequency. These simulations indicate that dynamic scheduling can increase the effective observing time at frequencies higher than 10 GHz by about 50%. Observers can be given at least 24 or 48 hours advance notice before their observing sessions start. We estimate that about 15% of the dynamically scheduled observing sessions at frequencies higher than 18 GHz will have to be canceled at the last minute because the actual weather is much worse than the forecast weather. The resulting gaps can be filled on short notice if about 25% of the low-frequency (< 10 GHz) sessions in the pool are available as backups that the telescope operator can run from prepared scripts or by astronomers voluntarily on call.
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Figure 2. Observing efficiency versus freqeuncy for traditional scheduling (left) and the DSS (right). Shown are efficiencies for individual observing sessions (plus), average efficiencies (filled circles with error bars), and the minimum observing efficiency (curve). Data points with efficiencies less than 0.1 of the minimum observing efficiency are denoted by triangles.
4.
Beta Test Results
The Beta Test during Summer 2008 included open, fixed, and windowed sessions based on real GBT observing projects. The packing and conflict resolution algorithms were included. The scheduling constraints significantly reduced overall observing efficiency over that predicted by the simulations. Although observing sessions covering 0.3-50 GHz were successfully executed. We are in the process of quantifying these results but this is difficult since both the scoring algorithm and the DSS software evolved during these tests (see McCarty et al. 2009). Future developments will include using more realistic simulations to refine the scoring algorithm, incorporating climate into the scoring algorithm for windowed sessions to improve their observing efficiency, and exploring ways to minimize telescope overhead when switching between projects. References Condon, J. J. 2007, Dynamic Scheduling Project Note 2 (http://wiki.gb.nrao.edu/pub/Dynamic/DynamicProjectNotes/dspn2.pdf). Condon, J. J. & Balser, D. S. 2007, Dynamic Scheduling Project Note 5 (http://wiki.gb.nrao.edu/pub/Dynamic/DynamicProjectNotes/dspn5.2.pdf). Evans, N., et al. 2003,“Site Properties and Stringency”, ALMA Memo 471. Maddalena, R. 2008, (http://www.gb.nrao.edu/ rmaddale/Weather/index.html). McCarty, M. et al. 2009, this volume. Sessoms, E. et al. 2009, this volume.
